This document summarizes research on using machine learning techniques to predict hospital admissions from emergency departments. It first provides background on how emergency department overcrowding can negatively impact patients and the need to improve patient flow. It then reviews 10 previous studies that used methods like logistic regression, decision trees, random forests and neural networks to predict admissions. Three algorithms (gradient boosted machine, random forest, decision tree) were implemented and evaluated on a hospital dataset, with random forest achieving the best performance. The paper concludes that admission prediction can help hospitals plan resources and reduce crowding.
This document describes a proposed model to predict hospital admissions from the emergency department to help reduce overcrowding. It discusses how overcrowding in emergency departments can negatively impact patient care. The proposed model would use machine learning techniques to predict admissions based on patient data from the triage process. This could help allocate hospital resources more efficiently and reduce waiting times in the emergency department. The document reviews several previous studies that developed predictive models using methods like logistic regression, decision trees, and neural networks to forecast admissions based on factors like patient demographics, medical history and symptoms.
An excellent article that uses predictive and optimization methods to reduce hospital readmissions.
Another great article, "Reducing hospital readmissions by integrating empirical prediction with resource optimization" (Helm, Alaeddini, Stauffer, Bretthaur, and Skolarus, 2016) describes how Machine Learning modeling tools were used to determine the root-causes and individualized estimation of readmissions. The post-discharge monitoring schedule and workplans were then optimized to patient changes in health states.
The document describes a simulation study of patient flows in orthopedic and sports medicine clinics at the University of Virginia Health System. The study explored the impact of changes to in-clinic task management, patient and staff scheduling, and patient communications to improve key performance measures like patient waiting times and facility utilization. The simulation model, developed based on patient data, provided evidence that introducing an additional front desk attendant during busy times and altering appointment times from 15 to 10 minutes could allow more patients to be seen while reducing waiting times and the need for overbooking. Implementing these changes may help accommodate future growth in patient visits while maintaining satisfaction.
Indoor patients’ satisfactory influential factors’ on healthcare services of ...HeenaRaffi1
This document discusses a study on factors influencing indoor patient satisfaction with healthcare services at small and medium-sized multi-specialty hospitals in Tiruchirappalli, India. The study examined patient satisfaction across four dimensions: healthcare services, supportive services, auxiliary services, and peripheral services. Adequate ward arrangements had the highest average patient satisfaction score. Statistical analysis found significant associations between overall patient satisfaction and all measured healthcare service factors. A neural network model with 7 input layers, 20 covariates layers, 1 hidden layer, and 1 output layer accurately modeled patient satisfaction based on socio-demographic and service quality factors.
This study assessed the costs and effects of different degrees of task shifting for anti-retroviral therapy (ART) from physicians to other health professionals in Ethiopia. The study found that (1) facilities with maximal task shifting, where non-physicians performed most ART tasks, had similar patient outcomes and costs as facilities with minimal/moderate task shifting; (2) over 88% of patients remained active on ART after two years across all facility types; and (3) maximal task shifting cost $36 more per patient over two years but resulted in 0.4% fewer patients remaining active, though this difference was not statistically significant.
Great article on how to integrate machine learning and optimization technique.
One group of researchers was able to reduce heart failure readmissions by 35% by combining machine learning and decision science technique, see "Data-driven decisions for reducing readmissions for heart failure: general methodology and case study" (Bayati, et. al., 2014).
Using the Bigtown Simulation Model to Predict the Impact of Enhanced Seven Day Services on Hospital Performance and Patient Outcomes
Poster from the 'Delivering NHS services, seven days a week' event held in Birmingham on 16 November 2013
More information about this event can be found at
http://www.nhsiq.nhs.uk/news-events/events/nhs-services-seven-days-a-week.aspx
1) Electronic medical records have the potential to transform medicine by serving as a platform for clinical decision support, personalized medicine, and precision medicine approaches through integration of diverse data sources.
2) Registries built from EMR data can be used to study conditions, compare treatment effectiveness, and recruit for clinical trials, with the goal of reducing the lag time between research and practice.
3) Advances in predictive modeling, diagnostic and treatment algorithms, and artificial intelligence may help optimize clinical decision making if effectively integrated into clinical workflow and EMRs.
This document describes a proposed model to predict hospital admissions from the emergency department to help reduce overcrowding. It discusses how overcrowding in emergency departments can negatively impact patient care. The proposed model would use machine learning techniques to predict admissions based on patient data from the triage process. This could help allocate hospital resources more efficiently and reduce waiting times in the emergency department. The document reviews several previous studies that developed predictive models using methods like logistic regression, decision trees, and neural networks to forecast admissions based on factors like patient demographics, medical history and symptoms.
An excellent article that uses predictive and optimization methods to reduce hospital readmissions.
Another great article, "Reducing hospital readmissions by integrating empirical prediction with resource optimization" (Helm, Alaeddini, Stauffer, Bretthaur, and Skolarus, 2016) describes how Machine Learning modeling tools were used to determine the root-causes and individualized estimation of readmissions. The post-discharge monitoring schedule and workplans were then optimized to patient changes in health states.
The document describes a simulation study of patient flows in orthopedic and sports medicine clinics at the University of Virginia Health System. The study explored the impact of changes to in-clinic task management, patient and staff scheduling, and patient communications to improve key performance measures like patient waiting times and facility utilization. The simulation model, developed based on patient data, provided evidence that introducing an additional front desk attendant during busy times and altering appointment times from 15 to 10 minutes could allow more patients to be seen while reducing waiting times and the need for overbooking. Implementing these changes may help accommodate future growth in patient visits while maintaining satisfaction.
Indoor patients’ satisfactory influential factors’ on healthcare services of ...HeenaRaffi1
This document discusses a study on factors influencing indoor patient satisfaction with healthcare services at small and medium-sized multi-specialty hospitals in Tiruchirappalli, India. The study examined patient satisfaction across four dimensions: healthcare services, supportive services, auxiliary services, and peripheral services. Adequate ward arrangements had the highest average patient satisfaction score. Statistical analysis found significant associations between overall patient satisfaction and all measured healthcare service factors. A neural network model with 7 input layers, 20 covariates layers, 1 hidden layer, and 1 output layer accurately modeled patient satisfaction based on socio-demographic and service quality factors.
This study assessed the costs and effects of different degrees of task shifting for anti-retroviral therapy (ART) from physicians to other health professionals in Ethiopia. The study found that (1) facilities with maximal task shifting, where non-physicians performed most ART tasks, had similar patient outcomes and costs as facilities with minimal/moderate task shifting; (2) over 88% of patients remained active on ART after two years across all facility types; and (3) maximal task shifting cost $36 more per patient over two years but resulted in 0.4% fewer patients remaining active, though this difference was not statistically significant.
Great article on how to integrate machine learning and optimization technique.
One group of researchers was able to reduce heart failure readmissions by 35% by combining machine learning and decision science technique, see "Data-driven decisions for reducing readmissions for heart failure: general methodology and case study" (Bayati, et. al., 2014).
Using the Bigtown Simulation Model to Predict the Impact of Enhanced Seven Day Services on Hospital Performance and Patient Outcomes
Poster from the 'Delivering NHS services, seven days a week' event held in Birmingham on 16 November 2013
More information about this event can be found at
http://www.nhsiq.nhs.uk/news-events/events/nhs-services-seven-days-a-week.aspx
1) Electronic medical records have the potential to transform medicine by serving as a platform for clinical decision support, personalized medicine, and precision medicine approaches through integration of diverse data sources.
2) Registries built from EMR data can be used to study conditions, compare treatment effectiveness, and recruit for clinical trials, with the goal of reducing the lag time between research and practice.
3) Advances in predictive modeling, diagnostic and treatment algorithms, and artificial intelligence may help optimize clinical decision making if effectively integrated into clinical workflow and EMRs.
* Patient-level & wound-level parameters influencing wound
healing were identified from prior research and clinician input
* Probability of wound healing can be predicted with reasonable
accuracy in real-world data from EMRs
This document proposes a prospective study to scale up surgical care at a rural hospital in Nepal using the WHO's Integrated Management for Emergency and Essential Surgical Care (IMEESC) model plus additional community follow-up and quality improvement methods. The study aims to rigorously evaluate this innovative model, pilot an implementation research methodology, and generate data to inform larger scale-up of surgical care worldwide. Specific objectives include describing the implementation process and measuring quality through adherence to protocols, follow-up rates, and complication rates. Metrics are proposed for evaluating pre-op, intra-op, post-op, facilities/supplies, and community follow-up. The study seeks to provide needed research on deploying surgical care in low-
Are you interested in learning how to prevent hospital readmissions for your diabetic population? It is a popular belief that measuring blood glucose for your diabetic population is the most predictive variable in determining a hospital readmission for a diabetic. However, many providers of care simply do not perform the test on known diabetic patients. This study takes a look at an advanced analytic method that works within the current healthcare providers workflow to looks to identify the likelihood of a future 30-day unplanned readmission before hospital discharge.
Understand what healthcare analytics is.
Identify the 5-stage Analytics Program Lifecycle (APL).
Understand how data analytics can be used in healthcare.
Check it on Experfy: https://www.experfy.com/training/courses/introduction-to-healthcare-analytics.
This document proposes a risk-based monitoring approach for clinical trials based on statistical evidence about the effects of errors and error corrections. The analysis shows that errors have minimal effects on study results and conclusions, and this effect diminishes as study size increases. On average, less than 8% source document verification is adequate to ensure data quality, with perhaps higher rates needed for smaller studies and virtually 0% for large studies. It is recommended that monitoring focus on data clarification queries of highly discrepant data, rather than just focusing on key outcomes.
This study sought to improve undertriage and overtriage rates at a Level II Pediatric Trauma Center by updating outdated trauma team activation (TTA) criteria and improving adherence to the criteria. The study was conducted in two phases: Phase I focused on improving adherence to newly revised TTA criteria, while Phase II moved triage responsibility to nurses and included transfer patients. Undertriage decreased from 15% to under 5% by the end of the study, while overtriage rates stabilized within recommended ranges. Standardizing processes through evidence-based criteria updates and role changes led to more accurate trauma patient triage and resource utilization.
1) A PDSA was conducted using queuing simulation modeling to analyze patient flow in an orthopedic outpatient clinic (OFC clinic) with the goals of reducing wait times and improving access to radiology services.
2) Data collection and a simplified initial simulation model found patients spent on average 90 minutes in the clinic, with most time spent waiting.
3) More detailed simulation experiments were run varying clinic parameters to identify improvements, with the goal of implementing changes to achieve wait times of 30 minutes or less.
4) Initial modeling results suggested interventions like prioritizing x-rays could reduce waits while maintaining services for senior patients, and engaged physicians to further test and validate proposed scheduling changes through simulation before
This document discusses capacity planning for radiation treatment machines at Cancer Care Ontario. It provides background on Cancer Care Ontario's role in managing radiation treatment services and current capacity. Between 2000-2012, the number of linear accelerators (linacs) grew from 65 to 100 across Ontario. The document discusses Cancer Care Ontario's Radiation Treatment Capital Investment Strategy (RTCIS) for determining needs. Forecasting shows demand for cancer treatments increasing to 42% of cases in 2020 and 48% in 2031, requiring additional linacs. The recommendation is to add 16 linacs between 2013-2017 in phases at various cancer centers based on forecasted demand.
IMPACT OF HEALTH INFORMATICS TECHNOLOGY ON THE IMPLEMENTATION OF A MODIFIED E...hiij
The Modified Early Warning System (MEWS) is based on a patient score that helps the medical team monitor patients to identify a patient that may be experiencing a sudden decline in care. This study consists of a detailed review of clinical data and patient outcomes to assess impact of technology and patient care.There are a total of thirteen hospitals included in this review. These facilities have implemented vitals capture and the MEWS scoring system.
The document describes how decision trees can be used to predict hospital readmission risk for patients with acute myocardial infarction (AMI), heart failure (HF), or pneumonia (PN). Decision trees were trained on 2010 California hospital data and tested on 2011 data. The decision trees achieved AUC scores of 0.612 for AMI, 0.583 for HF, and 0.650 for PN, indicating moderate predictive ability. However, decision trees provide the advantage of transparent, clinically relevant rules that can help hospitals target high-risk patient groups and design interventions to reduce readmissions.
The document describes a study that evaluated the impact of a mobile application called STOP STEMI on door-to-balloon times for patients presenting with STEMI. The study found that after implementing STOP STEMI, which aims to improve coordination of STEMI care, average door-to-balloon times decreased by 22% (from 91 to 71 minutes). A subgroup analysis of Medicare-reportable cases also saw a 22% reduction in door-to-balloon times (from 68 to 53 minutes). The percentage of cases meeting door-to-balloon time benchmarks of under 90 and 60 minutes improved. The study concluded that STOP STEMI reduced door-to-balloon times and improved meeting of benchmarks in patients presenting with STEMI.
This project charter outlines a research study on autism spectrum disorders to be conducted by EBOCT Technologies over 12 months. The study will examine the etiology, epidemiology, diagnosis, treatment and service delivery for autism across three stages: mild, intermediate, and severe. A budget of $607,660 is requested to fund three investigators and support staff to collaborate with other research centers and hospitals on understanding autism in children and adults. Progress reports will be provided to the National Institutes of Health every three months.
Health Technology Assessment- Overviewshashi sinha
This document discusses health technology assessment (HTA) in India. It provides an outline of HTA and its potential applications. HTA is defined as a multidisciplinary process that systematically evaluates the medical, social, economic and ethical issues related to a health technology. The document discusses the need for HTA in India given rising healthcare costs and limited resources. It outlines the HTA process, including defining the research question, criteria for study inclusion/exclusion, literature searches, and steps like systematic reviews and economic evaluations. Key applications of HTA mentioned are assessing new technologies for investment/disinvestment and informing priority setting and coverage decisions.
This randomized clinical trial compared medication administration error rates between dedicated medication nurses and general nurses across two hospitals. The main findings were:
1) Overall error rates were similar between medication nurses (15.7%) and general nurses (14.9%).
2) At one hospital, medication nurses had a significantly lower error rate than general nurses in surgical units but not medical units.
3) Differences in medication processes and settings highlighted the role of systems design in errors. The study suggests simple interventions may not reduce errors without broader system changes.
This summary provides an overview of a research paper that proposes a new surgical scheduling methodology based on procedure duration and variability:
1. The paper proposes sequencing surgical procedures based on duration groups and variability levels to reduce delayed starts from incorrectly estimated procedure lengths.
2. A simulation model was created using three years of historical surgical schedule and duration data from a local hospital to model their current scheduling approach and a new variability-based approach.
3. The simulation results were analyzed statistically to determine if the new variability-based scheduling method could reduce the number of delayed starts compared to the hospital's current scheduling algorithm.
O PTIMISATION B ASED ON S IMULATION : A P ATIENT A DMISSION S CHEDULING ...IJCI JOURNAL
This document summarizes a study that developed an optimization model to schedule patient admissions in a radiology department with the goal of reducing patient wait times. A mathematical model was created to minimize total completion time and total patient waiting time as a multi-objective problem. A multi-stage queuing system was used to represent the patient flow through registration, examination, and checkout. A case study was conducted of a hospital radiology department to collect data and test the optimization model using a multi-objective evolutionary algorithm. The results showed an average 7% reduction in total completion time and 34% reduction in total patient waiting time.
IRJET - Classification and Prediction for Hospital Admissions through Emergen...IRJET Journal
This document discusses using machine learning techniques to predict hospital admissions from emergency departments in order to improve patient flow and reduce overcrowding. It compares the performance of logistic regression and random forest algorithms on a dataset. Logistic regression identified several factors related to admissions including age, arrival mode, previous admissions. Random forests had the lowest accuracy. Predictive models could allow advance planning of resources to prevent bottlenecks. Future work involves exploring additional machine learning methods.
Machine learning and operations research to find diabetics at risk for readmisison.
A team of researchers was able to apply machine learning to reduce readmissions for diabetics, see "Identifying diabetic patients with high risk of readmission" (Bhuvan,Kumar, Zafar, Aand Kishore, 2016).
Sepsis Prediction Using Machine LearningIRJET Journal
This document summarizes a research paper that used machine learning algorithms to predict sepsis in ICU patients using vital sign and laboratory data. The researchers:
1) Collected data from 36,000 patients including vital signs, lab values, and demographics as features for an MLP classifier model.
2) The top important features for prediction were temperature, oxygen saturation, respiratory rate, and heart rate.
3) The MLP classifier model achieved a log loss of 0.15 and was able to accurately predict sepsis risk from patient data on admission to the ICU.
Early prediction of sepsis using machine learning approaches can help clinicians initiate early treatment and reduce mortality and healthcare costs.
1) The document evaluates integrating HIV care (ART clinics) with regular outpatient care (OPD) in a clinic in Zambia. Data was collected before and after integration on waiting times.
2) Preliminary results found ART patient processing times were longer, and waiting times increased for both patient types after integration. However, more analysis was needed to account for other changing factors.
3) Simulation results also initially found ART waiting times increased after integration, even when controlling for staffing levels. Further simulation informed how and when best to integrate clinics based on patient mix and other factors.
A LEAN SIX SIGMA APPROACH TO REDUCE WAITING AND REPORTING TIME IN THE RADIOLO...Joe Andelija
This document summarizes a research paper that used Lean Six Sigma to reduce waiting and reporting times in the radiology department of a tertiary care hospital in Kolkata, India. The researchers mapped the process from patient entry to report generation and identified areas of delay. Root causes of delay were found to be lack of patient preparation and disorganized operations. Recommendations included improving patient orientation to decrease pre-test wait times and streamlining operations to reduce post-test reporting delays. Implementing these changes statistically significantly reduced both pre-test and post-test waiting times.
* Patient-level & wound-level parameters influencing wound
healing were identified from prior research and clinician input
* Probability of wound healing can be predicted with reasonable
accuracy in real-world data from EMRs
This document proposes a prospective study to scale up surgical care at a rural hospital in Nepal using the WHO's Integrated Management for Emergency and Essential Surgical Care (IMEESC) model plus additional community follow-up and quality improvement methods. The study aims to rigorously evaluate this innovative model, pilot an implementation research methodology, and generate data to inform larger scale-up of surgical care worldwide. Specific objectives include describing the implementation process and measuring quality through adherence to protocols, follow-up rates, and complication rates. Metrics are proposed for evaluating pre-op, intra-op, post-op, facilities/supplies, and community follow-up. The study seeks to provide needed research on deploying surgical care in low-
Are you interested in learning how to prevent hospital readmissions for your diabetic population? It is a popular belief that measuring blood glucose for your diabetic population is the most predictive variable in determining a hospital readmission for a diabetic. However, many providers of care simply do not perform the test on known diabetic patients. This study takes a look at an advanced analytic method that works within the current healthcare providers workflow to looks to identify the likelihood of a future 30-day unplanned readmission before hospital discharge.
Understand what healthcare analytics is.
Identify the 5-stage Analytics Program Lifecycle (APL).
Understand how data analytics can be used in healthcare.
Check it on Experfy: https://www.experfy.com/training/courses/introduction-to-healthcare-analytics.
This document proposes a risk-based monitoring approach for clinical trials based on statistical evidence about the effects of errors and error corrections. The analysis shows that errors have minimal effects on study results and conclusions, and this effect diminishes as study size increases. On average, less than 8% source document verification is adequate to ensure data quality, with perhaps higher rates needed for smaller studies and virtually 0% for large studies. It is recommended that monitoring focus on data clarification queries of highly discrepant data, rather than just focusing on key outcomes.
This study sought to improve undertriage and overtriage rates at a Level II Pediatric Trauma Center by updating outdated trauma team activation (TTA) criteria and improving adherence to the criteria. The study was conducted in two phases: Phase I focused on improving adherence to newly revised TTA criteria, while Phase II moved triage responsibility to nurses and included transfer patients. Undertriage decreased from 15% to under 5% by the end of the study, while overtriage rates stabilized within recommended ranges. Standardizing processes through evidence-based criteria updates and role changes led to more accurate trauma patient triage and resource utilization.
1) A PDSA was conducted using queuing simulation modeling to analyze patient flow in an orthopedic outpatient clinic (OFC clinic) with the goals of reducing wait times and improving access to radiology services.
2) Data collection and a simplified initial simulation model found patients spent on average 90 minutes in the clinic, with most time spent waiting.
3) More detailed simulation experiments were run varying clinic parameters to identify improvements, with the goal of implementing changes to achieve wait times of 30 minutes or less.
4) Initial modeling results suggested interventions like prioritizing x-rays could reduce waits while maintaining services for senior patients, and engaged physicians to further test and validate proposed scheduling changes through simulation before
This document discusses capacity planning for radiation treatment machines at Cancer Care Ontario. It provides background on Cancer Care Ontario's role in managing radiation treatment services and current capacity. Between 2000-2012, the number of linear accelerators (linacs) grew from 65 to 100 across Ontario. The document discusses Cancer Care Ontario's Radiation Treatment Capital Investment Strategy (RTCIS) for determining needs. Forecasting shows demand for cancer treatments increasing to 42% of cases in 2020 and 48% in 2031, requiring additional linacs. The recommendation is to add 16 linacs between 2013-2017 in phases at various cancer centers based on forecasted demand.
IMPACT OF HEALTH INFORMATICS TECHNOLOGY ON THE IMPLEMENTATION OF A MODIFIED E...hiij
The Modified Early Warning System (MEWS) is based on a patient score that helps the medical team monitor patients to identify a patient that may be experiencing a sudden decline in care. This study consists of a detailed review of clinical data and patient outcomes to assess impact of technology and patient care.There are a total of thirteen hospitals included in this review. These facilities have implemented vitals capture and the MEWS scoring system.
The document describes how decision trees can be used to predict hospital readmission risk for patients with acute myocardial infarction (AMI), heart failure (HF), or pneumonia (PN). Decision trees were trained on 2010 California hospital data and tested on 2011 data. The decision trees achieved AUC scores of 0.612 for AMI, 0.583 for HF, and 0.650 for PN, indicating moderate predictive ability. However, decision trees provide the advantage of transparent, clinically relevant rules that can help hospitals target high-risk patient groups and design interventions to reduce readmissions.
The document describes a study that evaluated the impact of a mobile application called STOP STEMI on door-to-balloon times for patients presenting with STEMI. The study found that after implementing STOP STEMI, which aims to improve coordination of STEMI care, average door-to-balloon times decreased by 22% (from 91 to 71 minutes). A subgroup analysis of Medicare-reportable cases also saw a 22% reduction in door-to-balloon times (from 68 to 53 minutes). The percentage of cases meeting door-to-balloon time benchmarks of under 90 and 60 minutes improved. The study concluded that STOP STEMI reduced door-to-balloon times and improved meeting of benchmarks in patients presenting with STEMI.
This project charter outlines a research study on autism spectrum disorders to be conducted by EBOCT Technologies over 12 months. The study will examine the etiology, epidemiology, diagnosis, treatment and service delivery for autism across three stages: mild, intermediate, and severe. A budget of $607,660 is requested to fund three investigators and support staff to collaborate with other research centers and hospitals on understanding autism in children and adults. Progress reports will be provided to the National Institutes of Health every three months.
Health Technology Assessment- Overviewshashi sinha
This document discusses health technology assessment (HTA) in India. It provides an outline of HTA and its potential applications. HTA is defined as a multidisciplinary process that systematically evaluates the medical, social, economic and ethical issues related to a health technology. The document discusses the need for HTA in India given rising healthcare costs and limited resources. It outlines the HTA process, including defining the research question, criteria for study inclusion/exclusion, literature searches, and steps like systematic reviews and economic evaluations. Key applications of HTA mentioned are assessing new technologies for investment/disinvestment and informing priority setting and coverage decisions.
This randomized clinical trial compared medication administration error rates between dedicated medication nurses and general nurses across two hospitals. The main findings were:
1) Overall error rates were similar between medication nurses (15.7%) and general nurses (14.9%).
2) At one hospital, medication nurses had a significantly lower error rate than general nurses in surgical units but not medical units.
3) Differences in medication processes and settings highlighted the role of systems design in errors. The study suggests simple interventions may not reduce errors without broader system changes.
This summary provides an overview of a research paper that proposes a new surgical scheduling methodology based on procedure duration and variability:
1. The paper proposes sequencing surgical procedures based on duration groups and variability levels to reduce delayed starts from incorrectly estimated procedure lengths.
2. A simulation model was created using three years of historical surgical schedule and duration data from a local hospital to model their current scheduling approach and a new variability-based approach.
3. The simulation results were analyzed statistically to determine if the new variability-based scheduling method could reduce the number of delayed starts compared to the hospital's current scheduling algorithm.
O PTIMISATION B ASED ON S IMULATION : A P ATIENT A DMISSION S CHEDULING ...IJCI JOURNAL
This document summarizes a study that developed an optimization model to schedule patient admissions in a radiology department with the goal of reducing patient wait times. A mathematical model was created to minimize total completion time and total patient waiting time as a multi-objective problem. A multi-stage queuing system was used to represent the patient flow through registration, examination, and checkout. A case study was conducted of a hospital radiology department to collect data and test the optimization model using a multi-objective evolutionary algorithm. The results showed an average 7% reduction in total completion time and 34% reduction in total patient waiting time.
IRJET - Classification and Prediction for Hospital Admissions through Emergen...IRJET Journal
This document discusses using machine learning techniques to predict hospital admissions from emergency departments in order to improve patient flow and reduce overcrowding. It compares the performance of logistic regression and random forest algorithms on a dataset. Logistic regression identified several factors related to admissions including age, arrival mode, previous admissions. Random forests had the lowest accuracy. Predictive models could allow advance planning of resources to prevent bottlenecks. Future work involves exploring additional machine learning methods.
Machine learning and operations research to find diabetics at risk for readmisison.
A team of researchers was able to apply machine learning to reduce readmissions for diabetics, see "Identifying diabetic patients with high risk of readmission" (Bhuvan,Kumar, Zafar, Aand Kishore, 2016).
Sepsis Prediction Using Machine LearningIRJET Journal
This document summarizes a research paper that used machine learning algorithms to predict sepsis in ICU patients using vital sign and laboratory data. The researchers:
1) Collected data from 36,000 patients including vital signs, lab values, and demographics as features for an MLP classifier model.
2) The top important features for prediction were temperature, oxygen saturation, respiratory rate, and heart rate.
3) The MLP classifier model achieved a log loss of 0.15 and was able to accurately predict sepsis risk from patient data on admission to the ICU.
Early prediction of sepsis using machine learning approaches can help clinicians initiate early treatment and reduce mortality and healthcare costs.
1) The document evaluates integrating HIV care (ART clinics) with regular outpatient care (OPD) in a clinic in Zambia. Data was collected before and after integration on waiting times.
2) Preliminary results found ART patient processing times were longer, and waiting times increased for both patient types after integration. However, more analysis was needed to account for other changing factors.
3) Simulation results also initially found ART waiting times increased after integration, even when controlling for staffing levels. Further simulation informed how and when best to integrate clinics based on patient mix and other factors.
A LEAN SIX SIGMA APPROACH TO REDUCE WAITING AND REPORTING TIME IN THE RADIOLO...Joe Andelija
This document summarizes a research paper that used Lean Six Sigma to reduce waiting and reporting times in the radiology department of a tertiary care hospital in Kolkata, India. The researchers mapped the process from patient entry to report generation and identified areas of delay. Root causes of delay were found to be lack of patient preparation and disorganized operations. Recommendations included improving patient orientation to decrease pre-test wait times and streamlining operations to reduce post-test reporting delays. Implementing these changes statistically significantly reduced both pre-test and post-test waiting times.
Intensive care unit deals with data that are dynamic in nature like real time measurement of health condition to laboratory test data that are continuously
changes accordingly with time. Artificial intelligence (AI’s) potential ability to perform complex pattern analyses using large volumes of data. Generated
pattern discovers the new symptoms of the disease in the Intensive care units (ICUs), helps the doctors to prescribe the new drug discovery which is
helpful to intelligent use. Currently research work has been focused in the ICU making more efficient clinical workflow by generation of high-risk
patterns from improved high volumes of data. Emerging area of AI in the ICU includes mortality prediction, uses of powerful sensors, new drug
discovery, prediction of length of stay and legal role in uses of drugs for severity of disease. This review focuses latest application of AI drugs and
other relevant issues for the ICU.
ICU Patient Deterioration Prediction : A Data-Mining Approachcsandit
A huge amount of medical data is generated every da
y, which presents a challenge in analysing
these data. The obvious solution to this challenge
is to reduce the amount of data without
information loss. Dimension reduction is considered
the most popular approach for reducing
data size and also to reduce noise and redundancies
in data. In this paper, we investigate the
effect of feature selection in improving the predic
tion of patient deterioration in ICUs. We
consider lab tests as features. Thus, choosing a su
bset of features would mean choosing the
most important lab tests to perform. If the number
of tests can be reduced by identifying the
most important tests, then we could also identify t
he redundant tests. By omitting the redundant
tests, observation time could be reduced and early
treatment could be provided to avoid the risk.
Additionally, unnecessary monetary cost would be av
oided. Our approach uses state-of-the-art
feature selection for predicting ICU patient deteri
oration using the medical lab results. We
apply our technique on the publicly available MIMIC
-II database and show the effectiveness of
the feature selection. We also provide a detailed a
nalysis of the best features identified by our
approach.
ICU PATIENT DETERIORATION PREDICTION: A DATA-MINING APPROACHcscpconf
A huge amount of medical data is generated every day, which presents a challenge in analysing
these data. The obvious solution to this challenge is to reduce the amount of data without
information loss. Dimension reduction is considered the most popular approach for reducing
data size and also to reduce noise and redundancies in data. In this paper, we investigate the
effect of feature selection in improving the prediction of patient deterioration in ICUs. We
consider lab tests as features. Thus, choosing a subset of features would mean choosing the
most important lab tests to perform. If the number of tests can be reduced by identifying the
most important tests, then we could also identify the redundant tests. By omitting the redundant
tests, observation time could be reduced and early treatment could be provided to avoid the risk.
Additionally, unnecessary monetary cost would be avoided. Our approach uses state-of-the-art
feature selection for predicting ICU patient deterioration using the medical lab results. We
apply our technique on the publicly available MIMIC-II database and show the effectiveness of
the feature selection. We also provide a detailed analysis of the best features identified by our
approach.
Background Hospital contributes significantly tangible and intangible resources on a concurred plan by the scheduling of surgery on the OT list. Postponement decreases efficiency by declining throughput leads to wastage of resources hence burden to the nation. Patients and their family face economic and emotional implication due to the postponement. Postponement rate being a quality indicator controls check mechanism could be developed from the results. Postponement of elective scheduled operations results in inefficient use of the operating room (OR) time on the day of surgery. Inconvenience to patients and families are also caused by postponements. Moreover, the day of surgery (DOS) postponement creates logistic and financial burden associated with extended hospital stay and repetitions of pre-operative preparations to an extent of repetition of investigations in some cases causing escalated costs, wastage of time and reduced income. Methodology A cross-sectional study was done in the operation theaters of a tertiary care hospital in which total ten operation theaters of General Surgery Data of scheduled, performed and postponed surgeries was collected from all the operation theater with effect from March 1st to September 30th, 2018. A questionnaire was developed to find out the reasons for the postponement for all hospital’s stakeholders (surgeons, Anesthetist, Nursing Officer) and they were further evaluated time series analysis of scheduling of Operation Theater for moving average technique. Results Total 958 surgeries were scheduled and 772 surgeries performed were and 186 surgeries were postponed with a postponement rate of 19.42% in the cardiac surgery department during the study period. Month-wise postponement Rate exponential smoothing of time series data shows the dynamic of operating suits. To test throughput Postponement rate was plotted the postponed surgeries and on regression analysis is in a perfect linear relationship.
System for Recommending Drugs Based on Machine Learning Sentiment Analysis of...IRJET Journal
This document presents a system for recommending drugs based on machine learning sentiment analysis of drug reviews. The system aims to minimize the workload on experts by providing drug recommendations based on patient feedback. It uses various machine learning and deep learning techniques like naive bayes, logistic regression, LSTM, and GRU to analyze drug reviews and predict sentiment. Based on the sentiment analysis of features extracted from reviews, the system conditionally recommends medications for specific patients and conditions. It achieved up to 93% accuracy in recommending the top drugs for common conditions like acne, high blood pressure, anxiety, and depression. The system aims to enhance healthcare access and reduce medical errors by supplementing expert recommendations with an AI-powered recommendation platform.
Introduction: Postponement of elective scheduled operations results in inefficient use of operating room (OR) time on the day of surgery. Inconvenience to patients and families also caused by postponements. Moreover, day of surgery (DOS) postponement creates logistic and financial burden associated with extended hospital stay and repetitions of pre-operative preparations to an extend of repetition of investigations in some cases causing escalated costs, wastage of time and reduced income. Methodology: A cross sectional study was done in the operation theaters of a tertiary care hospital in which total ten operation theaters of General Surgery Data of scheduled, performed and postponed surgeries was collected from all the operation theater with effect from march 1st to September 30th 2018. A questionnaire was developed to find out the reasons for the postponement for all hospital’s stakeholders (Surgeons, Anesthetist, Nursing officer) and they were further evaluated Time series analysis of scheduling of Operation Theater for Moving average Technique. Results: total 2,466 surgeries were scheduled and 1,980 surgeries were performed and 486 surgeries were postponed in the general surgery department during the study period. Month wise postponement forecast was in accordance with the performed surgeries and on regression analysis postponed surgeries were in perfect linear relationship with the postponement Rate.
IRJET- A System for Complete Healthcare Management: Ask-Us-Health A Secon...IRJET Journal
This document proposes a system called ASK-US-HEALTH that uses machine learning algorithms and data mining to provide healthcare management. It aims to help patients access a second medical opinion by entering symptoms and receiving the probable diagnosis. It would also provide doctor recommendations and store patient medical histories and prescriptions. The system intends to improve healthcare access and help manage patient care and data for research through connecting patients, doctors, and nearby pharmacies via a web application.
Scheduling Of Nursing Staff in Hospitals - A Case Studyinventionjournals
This document summarizes a study that developed a goal programming algorithm to schedule 11 nurses across a two-week period at a hospital. The goals were to satisfy each nurse's contracted time, ensure minimum nurse requirements by role each day, give full-time nurses a weekend off while avoiding more than two consecutive days off, and honor nurses' weekend preference when possible. The algorithm solved the 154-variable, 120-constraint scheduling problem in under 30 seconds. The results showed schedules that met goals for minimum nurse levels each day and individual nurses' two-week schedules.
This document summarizes a proposed prospective study to rigorously evaluate the implementation of an expanded surgical care model (IMEESC-plus) at a district hospital in rural Nepal. The study aims to 1) evaluate the implementation process using mixed quantitative and qualitative methods at the hospital, staff, and patient levels, 2) pilot an implementation research methodology for potential larger studies, and 3) generate data to inform wider scale-up of surgical care globally. Specific objectives include evaluating hospital operations and costs, staff adherence to protocols and experiences, and changes in surgical volumes, complications, and patient follow-up over time.
38 www.e-enm.org
Endocrinol Metab 2016;31:38-44
http://dx.doi.org/10.3803/EnM.2016.31.1.38
pISSN 2093-596X · eISSN 2093-5978
Review
Article
How to Establish Clinical Prediction Models
Yong-ho Lee1, Heejung Bang2, Dae Jung Kim3
1Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea; 2Division of Biostatistics, Department
of Public Health Sciences, University of California Davis School of Medicine, Davis, CA, USA; 3Department of Endocrinology
and Metabolism, Ajou University School of Medicine, Suwon, Korea
A clinical prediction model can be applied to several challenging clinical scenarios: screening high-risk individuals for asymp-
tomatic disease, predicting future events such as disease or death, and assisting medical decision-making and health education.
Despite the impact of clinical prediction models on practice, prediction modeling is a complex process requiring careful statisti-
cal analyses and sound clinical judgement. Although there is no definite consensus on the best methodology for model develop-
ment and validation, a few recommendations and checklists have been proposed. In this review, we summarize five steps for de-
veloping and validating a clinical prediction model: preparation for establishing clinical prediction models; dataset selection;
handling variables; model generation; and model evaluation and validation. We also review several studies that detail methods
for developing clinical prediction models with comparable examples from real practice. After model development and vigorous
validation in relevant settings, possibly with evaluation of utility/usability and fine-tuning, good models can be ready for the use
in practice. We anticipate that this framework will revitalize the use of predictive or prognostic research in endocrinology, leading
to active applications in real clinical practice.
Keywords: Clinical prediction model; Development; Validation; Clinical usefulness
INTRODUCTION
Hippocrates emphasized prognosis as a principal component of
medicine [1]. Nevertheless, current medical investigation
mostly focuses on etiological and therapeutic research, rather
than prognostic methods such as the development of clinical
prediction models. Numerous studies have investigated wheth-
er a single variable (e.g., biomarkers or novel clinicobiochemi-
cal parameters) can predict or is associated with certain out-
comes, whereas establishing clinical prediction models by in-
corporating multiple variables is rather complicated, as it re-
quires a multi-step and multivariable/multifactorial approach to
design and analysis [1].
Clinical prediction models can inform patients and their
physicians or other healthcare providers of the patient’s proba-
bility of having or developing a certain disease and help them
with associated decision-making (e.g., facilitating patient-doc-
tor communication based on more objective information). Ap-
Received: 9 January 2016, Revised: 14 ...
Structure and development of a clinical decision support system: application ...komalicarol
Clinical decision requires to infer great, diverse and not suitably
organized quantity of information and having low time to decide.
The therapeutic choice is fundamental to formulate a strategy to
avoid complications and to achieve favorable results, being more
important in some specialties. In addition, medical decision-makers are overloaded with clinical tasks, have an intense work rate and
are subject to a great demand, and are prone to greater tiredness.
In this sense, computer tools can be extremely useful, as can deal
with a lot of information in much less time than decision-makers.
Thus, the existence of a tool that assists them in decision-making
is of crucial importance
Predictions And Analytics In Healthcare: Advancements In Machine LearningIRJET Journal
This document discusses advancements in machine learning and predictive analytics for healthcare. It begins with an introduction discussing how technologies like machine learning and artificial intelligence can help researchers and doctors achieve goals faster when integrated with healthcare. The document then reviews literature on challenges with analyzing big healthcare data due to issues like data variety, speed and volume. It discusses different machine learning algorithms that have been used for disease prediction and diagnosis, including decision trees, random forests, bagging and boosting. The methodology section outlines the use of an ensemble approach, combining multiple models to improve overall accuracy. Technologies implemented in this work include Python libraries like Pandas, NumPy and Scikit-learn for data processing and modeling, along with Flask and AWS for web app deployment. The
PATIENT AWOL GUESS AFTER BOOKING SESSIONIRJET Journal
This document discusses predicting patient absenteeism from scheduled medical appointments using machine learning models. It begins by outlining the problem of high no-show rates at appointments and how this wastes resources and increases wait times. The authors then review literature on factors that influence patient absenteeism. The document goes on to describe building predictive models using machine learning algorithms like random forest on data from medical appointments in Brazil. The results showed AUC scores below 0.6, indicating the models did poorly at prediction. The conclusion states more patient and appointment data is needed to build more accurate models.
Similar to IRJET- Hospital Admission Prediction: A Technology Survey (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
Generative AI Use cases applications solutions and implementation.pdfmahaffeycheryld
Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
https://www.leewayhertz.com/generative-ai-use-cases-and-applications/
Supermarket Management System Project Report.pdfKamal Acharya
Supermarket management is a stand-alone J2EE using Eclipse Juno program.
This project contains all the necessary required information about maintaining
the supermarket billing system.
The core idea of this project to minimize the paper work and centralize the
data. Here all the communication is taken in secure manner. That is, in this
application the information will be stored in client itself. For further security the
data base is stored in the back-end oracle and so no intruders can access it.
Blood finder application project report (1).pdfKamal Acharya
Blood Finder is an emergency time app where a user can search for the blood banks as
well as the registered blood donors around Mumbai. This application also provide an
opportunity for the user of this application to become a registered donor for this user have
to enroll for the donor request from the application itself. If the admin wish to make user
a registered donor, with some of the formalities with the organization it can be done.
Specialization of this application is that the user will not have to register on sign-in for
searching the blood banks and blood donors it can be just done by installing the
application to the mobile.
The purpose of making this application is to save the user’s time for searching blood of
needed blood group during the time of the emergency.
This is an android application developed in Java and XML with the connectivity of
SQLite database. This application will provide most of basic functionality required for an
emergency time application. All the details of Blood banks and Blood donors are stored
in the database i.e. SQLite.
This application allowed the user to get all the information regarding blood banks and
blood donors such as Name, Number, Address, Blood Group, rather than searching it on
the different websites and wasting the precious time. This application is effective and
user friendly.
Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
A high-Speed Communication System is based on the Design of a Bi-NoC Router, ...DharmaBanothu
The Network on Chip (NoC) has emerged as an effective
solution for intercommunication infrastructure within System on
Chip (SoC) designs, overcoming the limitations of traditional
methods that face significant bottlenecks. However, the complexity
of NoC design presents numerous challenges related to
performance metrics such as scalability, latency, power
consumption, and signal integrity. This project addresses the
issues within the router's memory unit and proposes an enhanced
memory structure. To achieve efficient data transfer, FIFO buffers
are implemented in distributed RAM and virtual channels for
FPGA-based NoC. The project introduces advanced FIFO-based
memory units within the NoC router, assessing their performance
in a Bi-directional NoC (Bi-NoC) configuration. The primary
objective is to reduce the router's workload while enhancing the
FIFO internal structure. To further improve data transfer speed,
a Bi-NoC with a self-configurable intercommunication channel is
suggested. Simulation and synthesis results demonstrate
guaranteed throughput, predictable latency, and equitable
network access, showing significant improvement over previous
designs
Determination of Equivalent Circuit parameters and performance characteristic...pvpriya2
Includes the testing of induction motor to draw the circle diagram of induction motor with step wise procedure and calculation for the same. Also explains the working and application of Induction generator
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
This study Examines the Effectiveness of Talent Procurement through the Imple...DharmaBanothu
In the world with high technology and fast
forward mindset recruiters are walking/showing interest
towards E-Recruitment. Present most of the HRs of
many companies are choosing E-Recruitment as the best
choice for recruitment. E-Recruitment is being done
through many online platforms like Linkedin, Naukri,
Instagram , Facebook etc. Now with high technology E-
Recruitment has gone through next level by using
Artificial Intelligence too.
Key Words : Talent Management, Talent Acquisition , E-
Recruitment , Artificial Intelligence Introduction
Effectiveness of Talent Acquisition through E-
Recruitment in this topic we will discuss about 4important
and interlinked topics which are
Road construction is not as easy as it seems to be, it includes various steps and it starts with its designing and
structure including the traffic volume consideration. Then base layer is done by bulldozers and levelers and after
base surface coating has to be done. For giving road a smooth surface with flexibility, Asphalt concrete is used.
Asphalt requires an aggregate sub base material layer, and then a base layer to be put into first place. Asphalt road
construction is formulated to support the heavy traffic load and climatic conditions. It is 100% recyclable and
saving non renewable natural resources.
With the advancement of technology, Asphalt technology gives assurance about the good drainage system and with
skid resistance it can be used where safety is necessary such as outsidethe schools.
The largest use of Asphalt is for making asphalt concrete for road surfaces. It is widely used in airports around the
world due to the sturdiness and ability to be repaired quickly, it is widely used for runways dedicated to aircraft
landing and taking off. Asphalt is normally stored and transported at 150’C or 300’F temperature