The use of artificial intelligence in clinical care to improve decision support systems is increasing. This is not surprising since by its very nature, the practice of medicine consists of making decisions based on observations from different systems both inside and outside the human body. In this paper, we combine three general systems (ICU, diabetes, and comorbidities) and use them to make patient clinical predictions. We use an artificial intelligence approach to show that we can improve mortality prediction of hospitalized diabetic patients. We do this by utilizing a machine learning approach to select clinical input features that are more likely to predict mortality. We then use these features to create a hybrid mortality prediction model and compare our results to non artificial intelligence models. For simplicity, we limit our input features to patient comorbidities and features derived from a well-known mortality measure, the Sequential Organ Failure Assessment (SOFA).
This document presents a system for remotely monitoring the health of diabetic patients. The system has two main components: 1) an intelligent device that monitors patients' blood glucose, blood pressure, and other readings and sends the data to the patient's phone via Bluetooth, and 2) a cloud-based module where the data is stored and accessible to doctors. The cloud-based module includes educational resources for patients and decision support tools for doctors. The system allows for 24/7 monitoring of patients' health and ensures timely updates and education are provided to help manage their condition.
SUPERVISED FEATURE SELECTION FOR DIAGNOSIS OF CORONARY ARTERY DISEASE BASED O...csitconf
Feature Selection (FS) has become the focus of much research on decision support systems
areas for which datasets with tremendous number of variables are analyzed. In this paper we
present a new method for the diagnosis of Coronary Artery Diseases (CAD) founded on Genetic
Algorithm (GA) wrapped Bayes Naïve (BN) based FS.
Basically, CAD dataset contains two classes defined with 13 features. In GA–BN algorithm, GA
generates in each iteration a subset of attributes that will be evaluated using the BN in the
second step of the selection procedure. The final set of attribute contains the most relevant
feature model that increases the accuracy. The algorithm in this case produces 85.50%
classification accuracy in the diagnosis of CAD. Thus, the asset of the Algorithm is then
compared with the use of Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and
C4.5 decision tree Algorithm. The result of classification accuracy for those algorithms are
respectively 83.5%, 83.16% and 80.85%. Consequently, the GA wrapped BN Algorithm is
correspondingly compared with other FS algorithms. The Obtained results have shown very
promising outcomes for the diagnosis of CAD.
Supervised Feature Selection for Diagnosis of Coronary Artery Disease Based o...cscpconf
This document presents a new method for diagnosing coronary artery disease (CAD) using genetic algorithm (GA) wrapped Bayes naive (BN) feature selection. The method uses a GA to generate feature subsets that are evaluated using BN classification. Over multiple iterations, the GA selects the feature subset that provides the highest accuracy. The algorithm is tested on a CAD dataset containing 13 features and achieves 85.5% classification accuracy. This performance is compared to other machine learning algorithms like SVM, MLP and C4.5 decision trees, which achieve lower accuracies of 83.5%, 83.16% and 80.85% respectively. The proposed method is also compared to other feature selection techniques like best first search and sequential floating forward search wrapped
IRJET - Prediction and Detection of Diabetes using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to predict and detect diabetes. It first provides background on diabetes and different types. It then reviews related work applying algorithms like SVM, KNN, and random forest to diabetes prediction. The document describes datasets and algorithms used in the proposed system, including Naive Bayes, support vector machines, and gradient boosting. It presents results showing gradient boosting achieved the highest accuracy of 96% and discusses using a voting classifier to combine algorithms. The proposed system aims to help people understand their diabetes risk and condition.
This research task develops a mobile healthcare analysis system (PHAS) which combines both easy ECG signal measurement and reliable analysis of heart rate variability for home care purpose. The PHAS is composed by a health care platform (HCP) and a data system analysis (DSA) module. The HCP consists of a self-developed two pole electrocardiography (ECG) measuring device and the DSA a data processing unit for detection and analysis of heart rate variability. For the DSA module, the adaptive R Peak detection algorithm is proposed to reliably detect the R peak of ECG for HRV analysis. A number of features are extracted from ECG signals. A data mining method is employed for HRV analysis to exploit the correlation between HRV and these features. Experiments are conducted by establishing a database of ECG signals measured from 29 subjects under rest and exercise condition. The results show the PHAS’s significant potential in mobile applications of personal daily health care.
This document presents the research methodology for a kidney cancer prediction system using rule-based methods. The system aims to help users predict early signs of kidney cancer based on their symptoms. It will have modules for user registration and login, a questionnaire to evaluate symptoms, and advice/suggestions generated by a rule-based algorithm. System frameworks and data flow diagrams are presented to show the high-level processes and breakdown of sub-processes like managing user profiles and questionnaires. An entity relationship diagram also models the system's data requirements. Preliminary screenshots demonstrate a proof of concept for the system's homepage, registration, login, administration, user, and results pages.
This document describes a computer-aided diagnosis system for detecting liver cancer in early stages from CT chest images. The system uses a Hidden Markov Model (HMM) for classification. It involves preprocessing the CT image through segmentation, noise removal and morphological operations. Features are then extracted and classified using HMM. The system achieved a high detection rate of 96.5% on 100 cases, outperforming other methods. This automated early detection system could help reduce risks for liver cancer patients by enabling earlier medical treatment.
This document presents a system for remotely monitoring the health of diabetic patients. The system has two main components: 1) an intelligent device that monitors patients' blood glucose, blood pressure, and other readings and sends the data to the patient's phone via Bluetooth, and 2) a cloud-based module where the data is stored and accessible to doctors. The cloud-based module includes educational resources for patients and decision support tools for doctors. The system allows for 24/7 monitoring of patients' health and ensures timely updates and education are provided to help manage their condition.
SUPERVISED FEATURE SELECTION FOR DIAGNOSIS OF CORONARY ARTERY DISEASE BASED O...csitconf
Feature Selection (FS) has become the focus of much research on decision support systems
areas for which datasets with tremendous number of variables are analyzed. In this paper we
present a new method for the diagnosis of Coronary Artery Diseases (CAD) founded on Genetic
Algorithm (GA) wrapped Bayes Naïve (BN) based FS.
Basically, CAD dataset contains two classes defined with 13 features. In GA–BN algorithm, GA
generates in each iteration a subset of attributes that will be evaluated using the BN in the
second step of the selection procedure. The final set of attribute contains the most relevant
feature model that increases the accuracy. The algorithm in this case produces 85.50%
classification accuracy in the diagnosis of CAD. Thus, the asset of the Algorithm is then
compared with the use of Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and
C4.5 decision tree Algorithm. The result of classification accuracy for those algorithms are
respectively 83.5%, 83.16% and 80.85%. Consequently, the GA wrapped BN Algorithm is
correspondingly compared with other FS algorithms. The Obtained results have shown very
promising outcomes for the diagnosis of CAD.
Supervised Feature Selection for Diagnosis of Coronary Artery Disease Based o...cscpconf
This document presents a new method for diagnosing coronary artery disease (CAD) using genetic algorithm (GA) wrapped Bayes naive (BN) feature selection. The method uses a GA to generate feature subsets that are evaluated using BN classification. Over multiple iterations, the GA selects the feature subset that provides the highest accuracy. The algorithm is tested on a CAD dataset containing 13 features and achieves 85.5% classification accuracy. This performance is compared to other machine learning algorithms like SVM, MLP and C4.5 decision trees, which achieve lower accuracies of 83.5%, 83.16% and 80.85% respectively. The proposed method is also compared to other feature selection techniques like best first search and sequential floating forward search wrapped
IRJET - Prediction and Detection of Diabetes using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to predict and detect diabetes. It first provides background on diabetes and different types. It then reviews related work applying algorithms like SVM, KNN, and random forest to diabetes prediction. The document describes datasets and algorithms used in the proposed system, including Naive Bayes, support vector machines, and gradient boosting. It presents results showing gradient boosting achieved the highest accuracy of 96% and discusses using a voting classifier to combine algorithms. The proposed system aims to help people understand their diabetes risk and condition.
This research task develops a mobile healthcare analysis system (PHAS) which combines both easy ECG signal measurement and reliable analysis of heart rate variability for home care purpose. The PHAS is composed by a health care platform (HCP) and a data system analysis (DSA) module. The HCP consists of a self-developed two pole electrocardiography (ECG) measuring device and the DSA a data processing unit for detection and analysis of heart rate variability. For the DSA module, the adaptive R Peak detection algorithm is proposed to reliably detect the R peak of ECG for HRV analysis. A number of features are extracted from ECG signals. A data mining method is employed for HRV analysis to exploit the correlation between HRV and these features. Experiments are conducted by establishing a database of ECG signals measured from 29 subjects under rest and exercise condition. The results show the PHAS’s significant potential in mobile applications of personal daily health care.
This document presents the research methodology for a kidney cancer prediction system using rule-based methods. The system aims to help users predict early signs of kidney cancer based on their symptoms. It will have modules for user registration and login, a questionnaire to evaluate symptoms, and advice/suggestions generated by a rule-based algorithm. System frameworks and data flow diagrams are presented to show the high-level processes and breakdown of sub-processes like managing user profiles and questionnaires. An entity relationship diagram also models the system's data requirements. Preliminary screenshots demonstrate a proof of concept for the system's homepage, registration, login, administration, user, and results pages.
This document describes a computer-aided diagnosis system for detecting liver cancer in early stages from CT chest images. The system uses a Hidden Markov Model (HMM) for classification. It involves preprocessing the CT image through segmentation, noise removal and morphological operations. Features are then extracted and classified using HMM. The system achieved a high detection rate of 96.5% on 100 cases, outperforming other methods. This automated early detection system could help reduce risks for liver cancer patients by enabling earlier medical treatment.
Computer aided diagnosis for liver cancer using statistical modeleSAT Journals
Abstract Liver Cancer is one of the most difficult cancer to cure and the number of deaths that it causes generally increasing. The signs and the symptoms of the liver cancer are not known, till the cancer is in its advanced stage. So, early detection is the main problem. If it is detected earlier then it can be helpful for the Medical treatment to limit the danger, but it is a challenging task due to the Cancer cell structure. Interpretation of Medical image is often difficult and time consuming, even for the experienced Physicians. Most traditional medical diagnosis systems founded needs huge quantity of training data and takes long processing time. Focused on the solution to these problems, a Medical Diagnosis System based on Hidden Markov Model (HMM) is presented. This paperdescribes a computer aided diagnosis system for liver cancer that detects the liver tumor at an early stage from the chest CT images. This automation process reduces the time complexity and increases the diagnosis confidence. Keywords—HMM, Segmentation, Feature Extraction.
BLOOD TUMOR PREDICTION USING DATA MINING TECHNIQUEShiij
Healthcare systems generate a huge data collected from medical tests. Data mining is the computing
process of discovering patterns in large data sets such as medical examinations. Blood diseases are not an
exception; there are many test data can be collected from their patients. In this paper, we applied data
mining techniques to discover the relations between blood test characteristics and blood tumor in order to
predict the disease in an early stage, which can be used to enhance the curing ability. We conducted
experiments in our blood test dataset using three different data mining techniques which are association
rules, rule induction and deep learning. The goal of our experiments is to generate models that can
distinguish patients with normal blood disease from patients who have blood tumor. We evaluated our
results using different metrics applied on real data collected from Gaza European hospital in Palestine.
The final results showed that association rules could give us the relationship between blood test
characteristics and blood tumor. Also, it demonstrated that deep learning classifiers has the best ability to
predict tumor types of blood diseases with an accuracy of 79.45%. Also, rule induction gave us an
explanation of rules that describes both tumor in blood and normal hematology.
IRJET -Improving the Accuracy of the Heart Disease Prediction using Hybrid Ma...IRJET Journal
This document describes research on improving the accuracy of heart disease prediction using hybrid machine learning techniques. The researchers collected data on patient biomarkers and risk factors from hospitals and online repositories. They applied data preprocessing, feature selection, and various classification models like decision trees, support vector machines, random forests, and K-nearest neighbors. Evaluating the models showed that a hybrid of fuzzy K-nearest neighbor and K-nearest neighbor achieved the highest accuracy rate of 94% for heart disease prediction. The researchers then built a web application using this hybrid model to allow users to predict their risk of heart disease online with high accuracy. The study demonstrates that machine learning can effectively analyze medical data and help predict diseases.
How EMR can connect You and Your Doctor?75health .com
EMR software allow the transfer of data from anywhere to any place, in the shortest possible time, to enable doctors across the globe to take care of their patients in a much better way than before. This has been made possible through the wide usage of EMRs. Implementation of EMR, that has been a personal preference hitherto, may be mandated in the medical service over time.
As electronic exchange of information facilitates better service for patients and supports physicians as well, it is no wonder that a major portion of the medical professionals are beginning to lean more toward EMR as their preferred system. Quick access, reliability, ‘anytime and anywhere’ reach are the main factors that make EMRs popular among today’s learned medicos. At times, it becomes necessary to share patient details using EMRs, when patients need medical attention from different doctors. EMR involves sharing individual information over the digital media. This calls for a cautious and secure exchange of data.
This study analyzed the technical efficiency of Turkish hospitals using data from 1,078 public and private hospitals in 2011. It found that:
1) Public general hospitals run by the Ministry of Health had the highest mean technical efficiency score of 0.57, compared to 0.49 for private general hospitals, contrary to expectations of higher efficiency in private hospitals.
2) University hospitals, both public and private, had the lowest mean scores of 0.41 and 0.39 respectively, since they allocate more resources to medical education than service delivery.
3) Integrated county hospitals in rural areas had lower scores than other public hospitals, likely due to lower patient volumes.
The study concludes more research is needed to
A study on “impact of artificial intelligence in covid19 diagnosis”Dr. C.V. Suresh Babu
A study on “Impact of Artificial Intelligence in COVID-19 Diagnosis”, Presentation slides for International Conference on "Life Sciences: Acceptance of the New Normal", St. Aloysius' College, Jabalpur, Madhya Pradesh, India, 27-28 August, 2021
This document discusses biomedical instrumentation and provides an overview of the subject in 3 sentences. It mentions that biomedical instrumentation involves engineering, science, and medicine. It also involves medical instruments, biology, chemistry, physics, electricity, computers and cognitive sciences. It then lists John D. Enderle and Metin Akay as references on the topic of bioinstrumentation and medical devices and instrumentation.
Insurance | Stratifying risk using wearable data | MunichReGalen Growth
Using wearables for insurance risk assessment. MunichRe concludes that there is strong evidence that physical activity as measured by steps per day can effectively segment mortality risk
Automated diagnosis of hepatitis b using multilayer mamdanimga_pk
This research article proposes a new multilayer Mamdani fuzzy inference system (ML-MFIS) to automate the diagnosis of hepatitis B. The system, called ADHB-ML-MFIS, classifies hepatitis B into no hepatitis, acute HBV, or chronic HBV stages. It has two input variables (ALT and AST levels) at layer 1 to detect liver conditions. Seven additional input variables at layer 2 (including HBsAg, anti-HBsAg, etc.) further determine the hepatitis condition output. The accuracy of ADHB-ML-MFIS in modeling hepatitis B processes according to medical experts is 92.2%.
المركز الرابع لمشاريع تحدي الامراض المزمنة
في مبادرة التحول الرقمي fekra_tech
وهو عبارة عن توضيف مكائن الفحص الذاتي للكشف عن المرضي المعرضين للاصابة بالسكري
fekratech.gov.sa
@NDU_KSA
Application of Support Vector Machine and Fuzzy Logic for Detecting and Ident...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Hybrid System of Tiered Multivariate Analysis and Artificial Neural Network f...IJECEIAES
Improved system performance diagnosis of coronary heart disease becomes an important topic in research for several decades. One improvement would be done by features selection, so only the attributes that influence is used in the diagnosis system using data mining algorithms. Unfortunately, the most feature selection is done with the assumption has provided all the necessary attributes, regardless of the stage of obtaining the attribute, and cost required. This research proposes a hybrid model system for diagnosis of coronary heart disease. System diagnosis preceded the feature selection process, using tiered multivariate analysis. The analytical method used is logistic regression. The next stage, the classification by using multi-layer perceptron neural network. Based on test results, system performance proposed value for accuracy 86.3%, sensitivity 84.80%, specificity 88.20%, positive prediction value (PPV) 90.03%, negative prediction value (NPV) 81.80%, accuracy 86,30% and area under the curve (AUC) of 92.1%. The performance of a diagnosis using a combination attributes of risk factors, symptoms and exercise ECG. The conclusion that can be drawn is that the proposed diagnosis system capable of delivering performance in the very good category, with a number of attributes that are not a lot of checks and a relatively low cost.
AN ALGORITHM FOR PREDICTIVE DATA MINING APPROACH IN MEDICAL DIAGNOSISijcsit
The document describes a predictive data mining algorithm for medical diagnosis that uses support vector machine (SVM) and random forest (RF) algorithms. It analyzes diabetes, kidney, and liver disease databases using these techniques. The proposed algorithm applies SVM and RF to the datasets and achieves high prediction accuracies of 99.35%, 99.37%, and 99.14% for diabetes, kidney, and liver diseases respectively. It also compares the performance of SVM and RF based on metrics like precision, recall, accuracy, and execution time.
Biomedical engineering is the application of engineering principles and design concepts to medicine and biology. It seeks to close the gap between engineering and medicine by designing products and procedures that solve medical problems, such as artificial organs, prostheses, medical instrumentation, and health systems. Biomedical engineers work with doctors and scientists to develop and apply technology including designing equipment to analyze blood samples, creating artificial hearts and skin grafts, and developing prosthetic hips and devices to repair bones.
Summarization Techniques in Association Rule Data Mining For Risk Assessment ...IJTET Journal
Abstract— At Early exposure of patients with dignified risk of developing diabetes mellitus is so hyper critical to the bettered prevention and global clinical management of these patients. In an existing system, apriori algorithm is used to find the itemsets for association rules but it is not efficient in finding itemsets and it uses only four association rules for finding the risk of diabetes mellitus so it have low precision. In this paper we are focusing to implement association rule mining to electronic medical records to detect set of danger factors and their equivalent or identical subpopulations that indicates patients at especially steep risk of progressing diabetes. Association rule mining accomplishes a very bulky set of rules for summarizing the EMR with huge dimensionability. We proposed a system in enlargement to combine risk of diabetes for the purpose of finding an suitable summary for this we use ten association rule and using the reorder algorithm for finding the itemsets and rules. For identifying the risk we considered four association rule set summarization techniques and organised a related calculation to support counselling with respect to their applicability merits and demerits and provide solutions to reduce the risk of diabetes. The above four methods having its fair strength but the bus algorithm developed the best acceptable summary.
The document presents a proposed healthcare monitoring system using machine learning techniques to improve diagnosis of heart disease in an Internet of Medical Things (IoMT) cloud environment. It introduces a modified salp swarm optimization algorithm (MSSO) combined with an adaptive neuro-fuzzy inference system (ANFIS) called MSSO-ANFIS. MSSO-ANFIS aims to improve ANFIS prediction accuracy by optimizing its learning parameters. Simulation results show MSSO-ANFIS achieves higher accuracy, precision, and performance than other methods for predicting heart disease risk based on patient data like blood pressure, age, sex, etc.
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).
This document presents research on using deep learning models to predict mortality for patients in the intensive care unit (ICU). The study uses the MIMIC-II dataset to evaluate models. It first discusses related work on ICU mortality prediction using machine learning. It then describes preprocessing the MIMIC-II data, which includes clustering repeated variables within each hour. Two models are evaluated: a recurrent neural network-long short term memory (RNN-LSTM) model and a convolution neural network (CNN) model. Evaluation metrics show the CNN model achieves higher precision, F1, and AUC scores compared to the RNN-LSTM model. Therefore, the CNN model may be better suited at mortality prediction tasks.
IRJET- Survey on Risk Estimation of Chronic Disease using Machine LearningIRJET Journal
This document summarizes research on using machine learning to predict chronic disease risk. It discusses how healthcare generates massive amounts of data that can be used for prediction. The paper proposes a new convolutional neural network (CNN) based model that uses both structured and unstructured data from hospitals to predict disease risk. It compares this multimodal approach to existing unimodal prediction models. The document also reviews several other studies applying machine learning to tasks like heart disease prediction using large healthcare datasets. The goal is to develop effective machine learning models for predicting disease outbreaks in communities using real hospital data.
Computer aided diagnosis for liver cancer using statistical modeleSAT Journals
Abstract Liver Cancer is one of the most difficult cancer to cure and the number of deaths that it causes generally increasing. The signs and the symptoms of the liver cancer are not known, till the cancer is in its advanced stage. So, early detection is the main problem. If it is detected earlier then it can be helpful for the Medical treatment to limit the danger, but it is a challenging task due to the Cancer cell structure. Interpretation of Medical image is often difficult and time consuming, even for the experienced Physicians. Most traditional medical diagnosis systems founded needs huge quantity of training data and takes long processing time. Focused on the solution to these problems, a Medical Diagnosis System based on Hidden Markov Model (HMM) is presented. This paperdescribes a computer aided diagnosis system for liver cancer that detects the liver tumor at an early stage from the chest CT images. This automation process reduces the time complexity and increases the diagnosis confidence. Keywords—HMM, Segmentation, Feature Extraction.
BLOOD TUMOR PREDICTION USING DATA MINING TECHNIQUEShiij
Healthcare systems generate a huge data collected from medical tests. Data mining is the computing
process of discovering patterns in large data sets such as medical examinations. Blood diseases are not an
exception; there are many test data can be collected from their patients. In this paper, we applied data
mining techniques to discover the relations between blood test characteristics and blood tumor in order to
predict the disease in an early stage, which can be used to enhance the curing ability. We conducted
experiments in our blood test dataset using three different data mining techniques which are association
rules, rule induction and deep learning. The goal of our experiments is to generate models that can
distinguish patients with normal blood disease from patients who have blood tumor. We evaluated our
results using different metrics applied on real data collected from Gaza European hospital in Palestine.
The final results showed that association rules could give us the relationship between blood test
characteristics and blood tumor. Also, it demonstrated that deep learning classifiers has the best ability to
predict tumor types of blood diseases with an accuracy of 79.45%. Also, rule induction gave us an
explanation of rules that describes both tumor in blood and normal hematology.
IRJET -Improving the Accuracy of the Heart Disease Prediction using Hybrid Ma...IRJET Journal
This document describes research on improving the accuracy of heart disease prediction using hybrid machine learning techniques. The researchers collected data on patient biomarkers and risk factors from hospitals and online repositories. They applied data preprocessing, feature selection, and various classification models like decision trees, support vector machines, random forests, and K-nearest neighbors. Evaluating the models showed that a hybrid of fuzzy K-nearest neighbor and K-nearest neighbor achieved the highest accuracy rate of 94% for heart disease prediction. The researchers then built a web application using this hybrid model to allow users to predict their risk of heart disease online with high accuracy. The study demonstrates that machine learning can effectively analyze medical data and help predict diseases.
How EMR can connect You and Your Doctor?75health .com
EMR software allow the transfer of data from anywhere to any place, in the shortest possible time, to enable doctors across the globe to take care of their patients in a much better way than before. This has been made possible through the wide usage of EMRs. Implementation of EMR, that has been a personal preference hitherto, may be mandated in the medical service over time.
As electronic exchange of information facilitates better service for patients and supports physicians as well, it is no wonder that a major portion of the medical professionals are beginning to lean more toward EMR as their preferred system. Quick access, reliability, ‘anytime and anywhere’ reach are the main factors that make EMRs popular among today’s learned medicos. At times, it becomes necessary to share patient details using EMRs, when patients need medical attention from different doctors. EMR involves sharing individual information over the digital media. This calls for a cautious and secure exchange of data.
This study analyzed the technical efficiency of Turkish hospitals using data from 1,078 public and private hospitals in 2011. It found that:
1) Public general hospitals run by the Ministry of Health had the highest mean technical efficiency score of 0.57, compared to 0.49 for private general hospitals, contrary to expectations of higher efficiency in private hospitals.
2) University hospitals, both public and private, had the lowest mean scores of 0.41 and 0.39 respectively, since they allocate more resources to medical education than service delivery.
3) Integrated county hospitals in rural areas had lower scores than other public hospitals, likely due to lower patient volumes.
The study concludes more research is needed to
A study on “impact of artificial intelligence in covid19 diagnosis”Dr. C.V. Suresh Babu
A study on “Impact of Artificial Intelligence in COVID-19 Diagnosis”, Presentation slides for International Conference on "Life Sciences: Acceptance of the New Normal", St. Aloysius' College, Jabalpur, Madhya Pradesh, India, 27-28 August, 2021
This document discusses biomedical instrumentation and provides an overview of the subject in 3 sentences. It mentions that biomedical instrumentation involves engineering, science, and medicine. It also involves medical instruments, biology, chemistry, physics, electricity, computers and cognitive sciences. It then lists John D. Enderle and Metin Akay as references on the topic of bioinstrumentation and medical devices and instrumentation.
Insurance | Stratifying risk using wearable data | MunichReGalen Growth
Using wearables for insurance risk assessment. MunichRe concludes that there is strong evidence that physical activity as measured by steps per day can effectively segment mortality risk
Automated diagnosis of hepatitis b using multilayer mamdanimga_pk
This research article proposes a new multilayer Mamdani fuzzy inference system (ML-MFIS) to automate the diagnosis of hepatitis B. The system, called ADHB-ML-MFIS, classifies hepatitis B into no hepatitis, acute HBV, or chronic HBV stages. It has two input variables (ALT and AST levels) at layer 1 to detect liver conditions. Seven additional input variables at layer 2 (including HBsAg, anti-HBsAg, etc.) further determine the hepatitis condition output. The accuracy of ADHB-ML-MFIS in modeling hepatitis B processes according to medical experts is 92.2%.
المركز الرابع لمشاريع تحدي الامراض المزمنة
في مبادرة التحول الرقمي fekra_tech
وهو عبارة عن توضيف مكائن الفحص الذاتي للكشف عن المرضي المعرضين للاصابة بالسكري
fekratech.gov.sa
@NDU_KSA
Application of Support Vector Machine and Fuzzy Logic for Detecting and Ident...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Hybrid System of Tiered Multivariate Analysis and Artificial Neural Network f...IJECEIAES
Improved system performance diagnosis of coronary heart disease becomes an important topic in research for several decades. One improvement would be done by features selection, so only the attributes that influence is used in the diagnosis system using data mining algorithms. Unfortunately, the most feature selection is done with the assumption has provided all the necessary attributes, regardless of the stage of obtaining the attribute, and cost required. This research proposes a hybrid model system for diagnosis of coronary heart disease. System diagnosis preceded the feature selection process, using tiered multivariate analysis. The analytical method used is logistic regression. The next stage, the classification by using multi-layer perceptron neural network. Based on test results, system performance proposed value for accuracy 86.3%, sensitivity 84.80%, specificity 88.20%, positive prediction value (PPV) 90.03%, negative prediction value (NPV) 81.80%, accuracy 86,30% and area under the curve (AUC) of 92.1%. The performance of a diagnosis using a combination attributes of risk factors, symptoms and exercise ECG. The conclusion that can be drawn is that the proposed diagnosis system capable of delivering performance in the very good category, with a number of attributes that are not a lot of checks and a relatively low cost.
AN ALGORITHM FOR PREDICTIVE DATA MINING APPROACH IN MEDICAL DIAGNOSISijcsit
The document describes a predictive data mining algorithm for medical diagnosis that uses support vector machine (SVM) and random forest (RF) algorithms. It analyzes diabetes, kidney, and liver disease databases using these techniques. The proposed algorithm applies SVM and RF to the datasets and achieves high prediction accuracies of 99.35%, 99.37%, and 99.14% for diabetes, kidney, and liver diseases respectively. It also compares the performance of SVM and RF based on metrics like precision, recall, accuracy, and execution time.
Biomedical engineering is the application of engineering principles and design concepts to medicine and biology. It seeks to close the gap between engineering and medicine by designing products and procedures that solve medical problems, such as artificial organs, prostheses, medical instrumentation, and health systems. Biomedical engineers work with doctors and scientists to develop and apply technology including designing equipment to analyze blood samples, creating artificial hearts and skin grafts, and developing prosthetic hips and devices to repair bones.
Summarization Techniques in Association Rule Data Mining For Risk Assessment ...IJTET Journal
Abstract— At Early exposure of patients with dignified risk of developing diabetes mellitus is so hyper critical to the bettered prevention and global clinical management of these patients. In an existing system, apriori algorithm is used to find the itemsets for association rules but it is not efficient in finding itemsets and it uses only four association rules for finding the risk of diabetes mellitus so it have low precision. In this paper we are focusing to implement association rule mining to electronic medical records to detect set of danger factors and their equivalent or identical subpopulations that indicates patients at especially steep risk of progressing diabetes. Association rule mining accomplishes a very bulky set of rules for summarizing the EMR with huge dimensionability. We proposed a system in enlargement to combine risk of diabetes for the purpose of finding an suitable summary for this we use ten association rule and using the reorder algorithm for finding the itemsets and rules. For identifying the risk we considered four association rule set summarization techniques and organised a related calculation to support counselling with respect to their applicability merits and demerits and provide solutions to reduce the risk of diabetes. The above four methods having its fair strength but the bus algorithm developed the best acceptable summary.
The document presents a proposed healthcare monitoring system using machine learning techniques to improve diagnosis of heart disease in an Internet of Medical Things (IoMT) cloud environment. It introduces a modified salp swarm optimization algorithm (MSSO) combined with an adaptive neuro-fuzzy inference system (ANFIS) called MSSO-ANFIS. MSSO-ANFIS aims to improve ANFIS prediction accuracy by optimizing its learning parameters. Simulation results show MSSO-ANFIS achieves higher accuracy, precision, and performance than other methods for predicting heart disease risk based on patient data like blood pressure, age, sex, etc.
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).
This document presents research on using deep learning models to predict mortality for patients in the intensive care unit (ICU). The study uses the MIMIC-II dataset to evaluate models. It first discusses related work on ICU mortality prediction using machine learning. It then describes preprocessing the MIMIC-II data, which includes clustering repeated variables within each hour. Two models are evaluated: a recurrent neural network-long short term memory (RNN-LSTM) model and a convolution neural network (CNN) model. Evaluation metrics show the CNN model achieves higher precision, F1, and AUC scores compared to the RNN-LSTM model. Therefore, the CNN model may be better suited at mortality prediction tasks.
IRJET- Survey on Risk Estimation of Chronic Disease using Machine LearningIRJET Journal
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Sepsis Prediction Using Machine LearningIRJET Journal
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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.
Diabetes Prediction by Supervised and Unsupervised Approaches with Feature Se...IJARIIT
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This document summarizes a research paper on developing a cloud-based health prediction system. The system allows users to enter their health issues and details like weight and height online. It then provides an accurate health prediction by matching the user's data to an analysis database. The cloud-based system is designed to be user-friendly and accessible from anywhere at any time. It aims to help users identify potential health problems early without visiting a doctor. The system architecture uses HTML, CSS, JavaScript, PHP and a MySQL database. It flows user data through registration, selecting health details, and logout for security.
Multiple disease prediction using Machine Learning AlgorithmsIRJET Journal
This document discusses a proposed system for predicting multiple diseases using machine learning algorithms. It aims to predict diabetes, brain tumors, heart disease, and Alzheimer's disease using factors like age, sex, BMI, blood glucose levels, and other health parameters. Previous systems could only predict single diseases. The proposed system uses TensorFlow, Flask API, and machine learning techniques. It saves models using Python pickling and loads them using unpickling when needed. The system allows adding new disease prediction models. It analyzes full disease impacts by considering all contributing factors. This allows better prediction accuracy compared to existing single-disease models.
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HYBRID MORTALITY PREDICTION USING MULTIPLE SOURCE SYSTEMS
1. DOI: 10.5121/ijci.2019.8101 1
HYBRID MORTALITY PREDICTION USING
MULTIPLE SOURCE SYSTEMS
Isaac Mativo1
, Yelena Yesha 1
, Michael Grasso2
, Tim Oates 1
, Qian Zhu 3
1
Department of Computer Science, University of Maryland Baltimore County (UMBC)
2
School of Medicine, University of Maryland Baltimore (UMB)
3
Department of Information Systems, University of Maryland Baltimore County
(UMBC)
ABSTRACT
The use of artificial intelligence in clinical care to improve decision support systems is increasing. This is
not surprising since by its very nature, the practice of medicine consists of making decisions based on
observations from different systems both inside and outside the human body. In this paper, we combine
three general systems (ICU, diabetes, and comorbidities) and use them to make patient clinical predictions.
We use an artificial intelligence approach to show that we can improve mortality prediction of hospitalized
diabetic patients. We do this by utilizing a machine learning approach to select clinical input features that
are more likely to predict mortality. We then use these features to create a hybrid mortality prediction
model and compare our results to non artificial intelligence models. For simplicity, we limit our input
features to patient comorbidities and features derived from a well-known mortality measure, the Sequential
Organ Failure Assessment (SOFA).
KEYWORDS
Decision Support Systems; Artificial Intelligence; Hybrid Systems.
I. INTRODUCTION
Clinical decision support systems are driven by large amounts of data sourced from various
disparate systems. These may include data from lab instruments, wearable devices, prior hospital
records, patient reported data, etc. Often, the clinician is exposed to a lot of this data and is
expected to make decisions to improve clinical outcomes. Although decision support systems are
effective in logical tasks such as alerting the user when some parameters are outside defined
ranges, they are not designed to learn from the data they have. As such, they are limited to what
they have been programmed to do. This limitation becomes an opportunity to experiment with
artificial intelligence within the clinical setting [1]. Specifically, a hybrid system that combines
attributes from both traditional clinical decision support systems and attributes derived from
machine learning tools to create a prediction model becomes attractive.Hybrid systems have been
used in many fields to improve performance in areas such as managing humanitarian relief chains
[2] and multiple-criteria decision-making [3] . In healthcare, hybrid decision support systems
have been used to help clinicians make more informed decisions [4] [5]. For mortality prediction,
several hybrid approaches have been proposed such as the use of hybrid image features on neural
network for cancer patients [3][6].In this paper, we take a systems theory view of a clinical
setting whereby many different systems come together to inform the clinician on the best
decisions to make. In the next section, we will describe what we consider to be the three systems
that we consider and apply machine learning tools on.
2. International Journal on Cybernetics & Informatics (IJCI) Vol. 8, No.1, February 2019
2
2. THE THREE SYSTEMS
A. Diabetes
For this paper, we consider the chronic disease diabetes as one of our three systems. This is
because diabetes, being a disorder of metabolism, affects multiple systems within the human
body [7]. When blood sugar is not well controlled and therefore outside the normal range for
prolonged periods of time, the proper functioning of various organ systems is compromised.
Consequently, this manifests in various body conditions such as kidney disease, neuropathy, liver
disease, etc. [8] .From a cybernetics perspective, diabetes is interesting because it involves the
delicate control of blood sugar by both systems inside and outside of the body. Diabetic patients
often rely on external interventions such as medication ingestion, insulin injection, dietary
controls, and lifestyle changes such as exercise. When the sugar level in blood is found to be
outside the normal range based on information gathered, various actions are taken to bring it
under control.
B. Intensive Care Unit
We consider the Intensive Care Unit (ICU) in a hospital to be our next system. This is because the
ICU is a specialized facility with many controlled and interconnected systems that work together
to intervene in critically ill patients. In the ICU, information is availed through various channels
such as electronic medical records, clinical notes, instrument measurements, etc. [8] [9] All this
information is then used to provide a level of clarity in making clinical decisions. Various aspects
of this information such as temporal occurrence (e.g. did the blood pressure fall after or before
takings a specific medication?) dependency (e.g. eat solid foods before taking a certain
medication), are important. As such, in an ICU facility, the proper control of information from
various systems is critical [10] . In this paper therefore, we view the ICU facility as one
large system with many subsystems within it.
C. Comorbidities
Comorbidities are co-occurring disease conditions within a person. Often, diabetic patients have
other disease conditions as well. Some of these conditions are more commonly associated with
diabetes than others [11]. In this paper, we think of comorbidities as a system because they are
well-defined conditions within the human body that individually and collectively influence the
wellbeing of the entire body. Since comorbidities are an important piece of information
considered in patient treatment, we considered comorbidities to be relevant in informing our
hybrid prediction model.
With these three systems, we build a hybrid mortality prediction model and evaluated its
performance. It is a hybrid model because it combined both machine learning techniques and
non-machine learning techniques to select it features and perform its predictions. In the next
section, we discuss the materials we used to do our experiment.
3. MATERIALS
A. Medical Information Mart for Intensive Care (MIMIC) III
3. International Journal on Cybernetics & Informatics (IJCI) Vol. 8, No.1, February 2019
3
The data source we used to get our patient population is MIMIC-III [12] . MIMIC-III is a large
and single-center database comprising de identified health related information containing 46,520
patients of whom 20,399 are female and 26,121 are male. These patients stayed in critical care
units of the Beth Israel Deaconess Medical Center between 2001 and 2012. The database includes
information such as demographics, vital sign measurements, laboratory test results, procedures,
medications, caregiver notes, imaging reports, and mortality. In this study, we accessed MIMIC-
III to extract diabetes patients for mortality prediction.
B. Elixhauser comorbidity measure.
In order to get the comorbidity information for our diabetic patients, we used The Elixhauser
comorbidity index [13] . This index consists of 30 comorbidity measures that have been shown to
have an impact on patient hospital length of stay, cost, and mortality [14] . Classification in to this
index is based on conditions described by the ICD-9-CM (International Classification of
Diseases, Ninth Edition, Clinical Modifications) discharge records. The Elixhauser index assigns
a value of 1 for each of 30 predefined conditions, and computes the sum. Therefore, a score of 10
indicates a patient has 10 conditions present.
C. Mortality Measure
To compare our hybrid model prediction accuracy with a needed a tool that measured mortality.
For this, we chose the Sequential Organ Failure Assessment (SOFA) [15]. SOFA is developed
from a large sample of ICU patients from around the world and gives a score based on the
severity of on a patient. The SOFA score is computed based on six variables, with each variable
corresponding to an organ system. The organ systems included in SOFA are respiratory,
cardiovascular, hepatic, coagulation, renal and neurological systems. Each organ system is
assigned a value between 0 (good) and 4 (severe). The SOFA score is the sum of all the
individual organ system values.
4. METHODS
In this section, we describe the methods we used to retrieve the patient cohort, computer the
mortality and comorbidity measure, and to predict mortality. For this study, ethical approval was
not needed because the data used is de-identified t o conform with the Health Insurance
Portability and Accountability Act (HIPAA). Additionally, the data is publicly available to
researchers.
A. Patient Retrieval
From our MIMIC III PostgreSQL database, we retrieved the diabetes patient cohort from MIMIC
III database based on ICD-9 codes for diabetes. To avoid restricting diabetic patients with
comorbidities for further mortality prediction, we extracted the entire diabetes diagnoses for each
of the diabetic patients.
B. Mortality Measure Computation
We used the SOFA score as a mortality measure. To calculate the SOFA score for each patient,
4. International Journal on Cybernetics & Informatics (IJCI) Vol. 8, No.1, February 2019
4
we extracted the patient in the PostgreSQL database for calculating the the SOFA score
corresponding to the organ areas as shown in table 1. We extracted this data using query scripts in
PostgreSQL from the MIMIC III diabetic patients data based on the first day of each patient's’
ICU stay. For each patient, we used an SQL script to calculate the actual points for each organ
system, then added up the points for each organ system to obtain the SOFA score. For missing
values, we assigned zero points to that organ system. We did this for all collected diabetic
patients.
Table 1: Sofa Organ Areas and Points Thresholds
Organ/Points 1 2 3 4
Respiratory
PaO 2 /FiO 2
(mmHg)
<400 <300 <200 and
respiratory
support
<100 and
respiratory
support
Cardiological
Mean arterial
pressure
MAP < 70
mm/Hg
dop <=
5 or
dob
dop > 5
OR epi <=
0.1 OR nor
<= 0.1
dop > 15 OR epi
> 0.1 OR nor >
0.1
Hepatic
Bilirubin (mg/dl)
[μmol/L]
1.2–1.9
[> 20-32]
2.0–5.9
[33-101]
6.0–11.9
[102-204]
> 12.0
[> 204]
Neurological
Glasgow coma
scale
13 - 14 10 - 12 6 - 9 <6
Coagulation
Platelets×103/μl
<150 <100 <50 <20
C. Comorbidity Measure and Weight Computation
We used the Elixhauser index to calculate the comorbidity measure for each diabetic patient in
MIMIC-III. For each diabetic patient, we extracted 29 different diagnosis excluding diabetes and
quantified them by using the Elixhauser index for comorbidities represented in the diabetic
population. In order to improve the efficiency of our classifier by reducing the number of
features, we identified the top five comorbidities that are most predictive of mortality across the
identified diabetic patients. To do this, we experimented using 5 different feature selection
algorithms, which are Gain Ratio [16] , Correlation [17] , Symmetrical Uncertainty [18] ,
Information Gain [19] , and Correlation Feature Selection (CFS) Subset Evaluator [20] . For each
algorithm run, we ranked the comorbidities based on the resulted predictive mortality. We then
averaged these results over the 5 algorithms to get our final top 5 most predictive comorbidities.
The final list of the comorbidities was cardiac arrhythmias, coagulopathy, metastatic cancer,
congestive heart failure, and fluid electrolyte.
D. Mortality Prediction
For mortality prediction, we created a hybrid mortality prediction model that took as input the 6
SOFA scores representing the 6 organ systems and the top 6 ranked comorbidities, for a total of
11 features. We then experimented with a Naive Bayes classifier and a Random Forest classifier
5. International Journal on Cybernetics & Informatics (IJCI) Vol. 8, No.1, February 2019
5
with 10-fold cross validation across our patient population. We compared the prediction accuracy
of our hybrid system with the accuracy of (i) a prediction model with only the SOFA scores and
(ii) a prediction model with only the comorbidity information. Our accuracy comparison was
based on the AUC on both the Naive Bayes and Random Forest classifiers on mortality.
5. RESULTS
A. Patient Retrieval
A total of 10,403 of diabetes patients were retrieved from 46,520 patients contained in MIMIC III
database. Of all the identified diabetic patients, 1,513 died while in ICU. Among the 10,403
diabetes patients, we identified 29 different comorbidities. B. Mortality Measure Computation
Table 2 shows the calculated SOFA scores and the corresponding mortality rates as computed
from our data. Our mortality rates were consistent with the expected SOFA mortality rates.
Table 2: Calculated Mortality Rates
C. Comorbidity Measure and Weights
Figure 1 shows the results of calculated comorbidity for the diabetic patients using the
Elixhauser index. As can be seen, most of the patients had comorbidities.
Figure 1: Comorbidities Across Patients
6. International Journal on Cybernetics & Informatics (IJCI) Vol. 8, No.1, February 2019
6
Table 3 shows results generated via the five feature extraction algorithms to determine what
comorbidities to be applied for prediction classifiers. These are the comorbidities that were used
as features in our model.
Table 3: Highly Weighed Comorbidities
D. Mortality Prediction
Table 4 shows the results generated by the Naive Bayes and Random Forest classifiers. The ROC
is lowest when using only the 5 comorbidity features are used, is better when using the 6 SOFA
scores are used, and best when both the 5 comorbidities and 6 SOFA scores are combined. Thus,
the hybrid model outperforms the natively SOFA or comorbidity only models.
Table 4: Mortality Prediction Results
6. DISCUSSION
In this study, we proposed viewing ICU clinical care through the lens of cybernetics. We
7. International Journal on Cybernetics & Informatics (IJCI) Vol. 8, No.1, February 2019
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identified three systems; the ICU, diabetes, and patient comorbidity, as related and defined
systems that are important in supporting clinical decisions. We examined the mortality impact of
comorbidities on diabetic patients patients admitted in an ICU unit using machine learning
tools.We created a hybrid mortality prediction model and compare its results to non-hybrid
models.The results we obtained support our claim that mortality prediction can be improved by
incorporation key features from different systems (comorbidities in our case) and using artificial
intelligence to train our classifiers.
Another key observation from our results is that we can still get improved performance from our
prediction model without using all of the available input features. In our case, we used statistical
techniques to rank all our comorbidities based on their weight in impacting mortality. This
observation is important because within systems, we can optimize performance by carefully
selecting the components that affect our output. This careful selection results in dimension
reduction and consequently reduces computational resources needed to run algorithms while at
the same time producing improved results.
A logical extension of this research is further defining the subsystems within our system, or yet
still identifying other systems that are important in improving clinical decision support
systems.An improvement of our approach would be to take a closer look at the information flow
within and across our systems and evaluate its impact on the overall efficiency of an ICU
setting.Important makers, in this case, could include items such as readmission rates, adverse
events, hospital length of stay, central line infection, etc. We hope that with this study we have
demonstrated that it is possible to view clinical decision support systems from cybernetics
perspective and encouraged other researchers to consider this approach.
DISCLOSURE OF INTERESTS
The authors report no conflict of interest.
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