The second of a two-part talk from Richard Lilford and Sam Watson on modelling causal pathways in health services for the CLAHRC West Midlands Scientific Advisory Group meeting, 9th June 2015, Birmingham, UK
This document discusses a study that aimed to determine the accuracy of consumer wearable activity trackers in measuring heart rate and heart rate variability compared to a Holter monitor. A single healthy volunteer wore a Mio Link wristband and MyPatch Holter monitor during exercise and meditation. The data showed a weak correlation between the devices. While consumer trackers have benefits, the study found room for improvement in software algorithms and hardware detection to ensure accurate stress management and health monitoring applications. More research is still needed with larger sample sizes and controlled experiments.
Another presentation I have given at the SRR. This is from the 2013 Winter Meeting in Bath.
The European Brain Injury Questionnaire is a useful tool for capturing self and proxy ratings of symptom frequency. We have been using at Oliver Zan
gwill Centre since the unit opened, and have amassed a substantial database that is yielding valuable insights for prioritising services.
For me, the interesting message in this latest research is that there is a range of types of self-proxy discrepancies in the different EBIQ domains. I have been doing work on the EBIQ questionnaire for some years now, do contact me for more information about this symptom checklist.
“ High Precision Analytics for Healthcare: Promises and Challenges” by Sriram...The Hive
1) Predictive analytics in healthcare often provides risk scores and predictions but lacks actionable insights on how to prevent outcomes.
2) The right methodology is needed to transform raw data like claims, prescriptions and medical records into meaningful predictions using machine learning algorithms.
3) Accurate predictions require measuring precision down to the individual level while accounting for both patient and provider factors that influence health outcomes.
The document summarizes a catalog of English language books on statistics in the social sciences. It announces that 2013 was declared the International Year of Statistics by the American Statistical Association to honor the achievements of statistics in science. The catalog promotes important books in the field. It provides summaries and details of several books applying statistics and quantitative analysis in social sciences, including titles on mathematical modeling of human thought, introduction to Stata software, multivariate statistics primer, and agent-based computational sociology.
Genetic algorithms and feature selection techniques are used to analyze medical diagnosis data and predict disease. The process involves obtaining patient data, selecting relevant features, and using a genetic algorithm to evolve a mathematical model for accurate prediction. Specifically, (1) medical records are collected as training data, (2) irrelevant variables are removed via filter feature selection, and (3) a genetic algorithm simulates natural selection to iteratively improve a model for predicting disease in new patient records. This automated approach helps analyze large datasets, minimize human interaction, and facilitate timely treatment recommendations.
Analysis of "A Predictive Analytics Primer" by Tom DavenportEt Hish
Predictive analytics uses statistical techniques like predictive modelling, machine learning, and data mining to analyze past and present data to predict future events. The quality of predictive analytics depends on three factors: the quality of past data used, the appropriate use of statistical techniques like various types of regression analysis, and the validity of assumptions made in the analysis. Issues like poor historical data, assumptions not holding true over time, and data not being streamlined across sources can create barriers to accurate prediction.
The document outlines the benefits and opportunities of using artificial intelligence (AI) and machine learning (ML) in healthcare. It discusses how AI can help improve clinical outcomes by using both clinical and non-clinical patient data to identify high-risk patients, recommend interventions, and learn from previous results. This allows providers to reduce readmissions, complications, and costs while improving the bottom line. The document also covers the types of AI models that can be used, from descriptive models that learn from past data to predictive and prescriptive models, as well as challenges to implementation.
Meta-analysis is a systematic procedure for combining data from multiple studies to arrive at a meaningful conclusion on a topic. It is being included as part of PhD theses, especially in clinical and psychological studies, to strengthen or support thesis results. Perfect Statistics provides top dissertation writing services for scientists, students and pharmaceuticals including meta-analysis, systematic reviews, medical statistics and medical writing projects.
This document discusses a study that aimed to determine the accuracy of consumer wearable activity trackers in measuring heart rate and heart rate variability compared to a Holter monitor. A single healthy volunteer wore a Mio Link wristband and MyPatch Holter monitor during exercise and meditation. The data showed a weak correlation between the devices. While consumer trackers have benefits, the study found room for improvement in software algorithms and hardware detection to ensure accurate stress management and health monitoring applications. More research is still needed with larger sample sizes and controlled experiments.
Another presentation I have given at the SRR. This is from the 2013 Winter Meeting in Bath.
The European Brain Injury Questionnaire is a useful tool for capturing self and proxy ratings of symptom frequency. We have been using at Oliver Zan
gwill Centre since the unit opened, and have amassed a substantial database that is yielding valuable insights for prioritising services.
For me, the interesting message in this latest research is that there is a range of types of self-proxy discrepancies in the different EBIQ domains. I have been doing work on the EBIQ questionnaire for some years now, do contact me for more information about this symptom checklist.
“ High Precision Analytics for Healthcare: Promises and Challenges” by Sriram...The Hive
1) Predictive analytics in healthcare often provides risk scores and predictions but lacks actionable insights on how to prevent outcomes.
2) The right methodology is needed to transform raw data like claims, prescriptions and medical records into meaningful predictions using machine learning algorithms.
3) Accurate predictions require measuring precision down to the individual level while accounting for both patient and provider factors that influence health outcomes.
The document summarizes a catalog of English language books on statistics in the social sciences. It announces that 2013 was declared the International Year of Statistics by the American Statistical Association to honor the achievements of statistics in science. The catalog promotes important books in the field. It provides summaries and details of several books applying statistics and quantitative analysis in social sciences, including titles on mathematical modeling of human thought, introduction to Stata software, multivariate statistics primer, and agent-based computational sociology.
Genetic algorithms and feature selection techniques are used to analyze medical diagnosis data and predict disease. The process involves obtaining patient data, selecting relevant features, and using a genetic algorithm to evolve a mathematical model for accurate prediction. Specifically, (1) medical records are collected as training data, (2) irrelevant variables are removed via filter feature selection, and (3) a genetic algorithm simulates natural selection to iteratively improve a model for predicting disease in new patient records. This automated approach helps analyze large datasets, minimize human interaction, and facilitate timely treatment recommendations.
Analysis of "A Predictive Analytics Primer" by Tom DavenportEt Hish
Predictive analytics uses statistical techniques like predictive modelling, machine learning, and data mining to analyze past and present data to predict future events. The quality of predictive analytics depends on three factors: the quality of past data used, the appropriate use of statistical techniques like various types of regression analysis, and the validity of assumptions made in the analysis. Issues like poor historical data, assumptions not holding true over time, and data not being streamlined across sources can create barriers to accurate prediction.
The document outlines the benefits and opportunities of using artificial intelligence (AI) and machine learning (ML) in healthcare. It discusses how AI can help improve clinical outcomes by using both clinical and non-clinical patient data to identify high-risk patients, recommend interventions, and learn from previous results. This allows providers to reduce readmissions, complications, and costs while improving the bottom line. The document also covers the types of AI models that can be used, from descriptive models that learn from past data to predictive and prescriptive models, as well as challenges to implementation.
Meta-analysis is a systematic procedure for combining data from multiple studies to arrive at a meaningful conclusion on a topic. It is being included as part of PhD theses, especially in clinical and psychological studies, to strengthen or support thesis results. Perfect Statistics provides top dissertation writing services for scientists, students and pharmaceuticals including meta-analysis, systematic reviews, medical statistics and medical writing projects.
The document describes the process of analyzing NHS administrative data on patient appointments to categorize patients based on their appointment attendance and missed appointments. It involved processing over 800,000 appointments for 73,000 patients to compute attendance rates and classify patients into categories of zero, low, medium, or high missed appointments. Demographic data on patients was then merged to analyze patterns between attendance and factors like age, distance from practice, and socioeconomic status.
This document provides an overview of statistics for social work research. It defines statistics as the science of developing knowledge through empirical data expressed quantitatively, based on probability theory. Statistics involves collecting, summarizing, and analyzing numerical data. Descriptive statistics summarize and describe data, while inferential statistics model patterns in data to draw inferences about populations. The document discusses the characteristics, functions, scope, limitations, and potential misuse of statistics.
What is statistics and how is the discipline of statistics different to machine learning? Statistics is the oldest kind on the block of data science. However, it is not as popular as machine learning or deep learning is. Nevertheless, there are countless applications of statistical science in the real world.
Agent based modelling is a very useful and flexible modelling technique. It is especially useful when modelling complex systems, such as societies or economies. This makes it particularly useful when modelling token economies. Agent based modelling can be a powerful tool for any ICO.
This slideshare has been produced by the Tesseract Academy (http://tesseract.academy), a company that educates decision makers in deep technical topics such as data science, analytics, and blockchain.
For more information about this topic also visit The Data Scientist:
http://thedatascientist.com/statistics-vs-machine-learning-two-worlds/
1) The document discusses the future of electronic health records (EHR) for research in New Zealand beyond 2014.
2) It notes that the PREDICT/VIEW research programme has collected EHR data on cardiovascular disease risk factors for over 250,000 participants in Auckland/Northland.
3) However, it cautions that a key challenge is ensuring the accuracy and completeness of data in the EHR, as data is often incomplete, inaccurate, or out of date.
LeAnna Kent - Using Network Analysis to Detect Kickback Schemes Among Medical...MLconf
This document describes a method for detecting kickback schemes using network analysis. It builds a graph with doctors as nodes and their connections based on shared patients. It then analyzes each doctor's egonet independently and applies an algorithm to select the subgraph with the strongest edges for each. These subgraphs are then ranked based on their strength scores to prioritize investigations into potential kickback schemes.
Prof Mendel Singer Big Data Meets Public Health and Medicine 2018 12-22mjbinstitute
Presentation by Prof. Mendel Singer of Case Western Reserve University, on the issue of "big data" in health care and policy research. Presented at the Myers-JDC-Brookdale Institute in Jerusalem.
Filling the gaps in translational researchPaul Agapow
- Translational research often focuses on early-stage problems that are interesting scientifically but do not address the most important problems in developing new therapies. This neglects later and more difficult stages of drug development where the largest costs and failures occur.
- More focus is needed on developing therapies for complex, systemic diseases and diverse patient populations using real-world data and approaches that incorporate biological complexity early in the process. Machine learning should be applied where it can have the most impact in reducing costs, such as predicting adverse events later in development.
- Efforts are also needed to build more diverse, representative datasets and use data science approaches like drug repurposing that have the potential to accelerate therapy development.
Multi morbidity - the notion of tacit knowledge - Magdalena Skrybant and Celi...NIHR CLAHRC West Midlands
Magdalena Skrybant and Celia Taylor stepped into the breach on the second day of our Scientific Advisory Group after one of our presenters was taken ill.
The document summarizes the activities and outputs of the NIHR Collaborations for Leadership in Applied Health Research and Care West Midlands (CLAHRC WM) program over the past year. Some key points:
- CLAHRC WM involves collaboration between hospitals, primary care, local authorities to implement and evaluate new interventions, with £20m in funding.
- It has produced over 140 academic papers, with a total impact factor of over 139,000. External grant income totaled over £8 million for the year and £47 million total.
- 83 postgraduate students were supported across various research themes including maternity/child health, mental health, chronic diseases, and implementation research.
-
The document discusses methodological challenges in assessing the effectiveness of interventions for coordinated care of chronic diseases. It summarizes preliminary findings from 81 reviews on interventions' impact on hospitalization rates. Key challenges include double counting of primary studies across reviews and determining appropriate ways to group heterogeneous interventions for analysis given interventions often have overlapping elements. Excluding reviews based solely on quality scores may overlook potentially useful outcomes data, requiring consideration of alternative strategies.
Presentation given by Dr Sam Watson and Dr Yen-Fu Chen at the latest CLAHRC WM Programme Steering Committee meeting on 15th April 2015, at the University of Warwick.
Dr John Ovretveit's critique on Dr Yen-Fu Chen's presentation on publication bias in service delivery research for the CLAHRC WM Scientific Advisory Group, 10th June 2015, Birmingham, UK
The first of a two-part talk from Richard Lilford and Sam Watson on modelling causal pathways in health services for the CLAHRC West Midlands Scientific Advisory Group meeting, 9th June 2015, Birmingham, UK
The document is a wedding invitation for Bhavish Sharma and Deepthi Sharma. It invites guests to attend pre-wedding celebrations including a Sangeet musical celebration on an unspecified date where guests can dance and enjoy music. It also invites guests to attend the wedding ceremony on April 6, 2015 at City Pride Garden in Ajmer, India to celebrate the new beginnings and love between Bhavish and Deepthi.
A talk on design choices for cluster randomised trials by Dr Alan Girling for the CLAHRC WM Scientific Advisory Group meeting, 9th June 2015, Birmingham, UK
The document describes the process of analyzing NHS administrative data on patient appointments to categorize patients based on their appointment attendance and missed appointments. It involved processing over 800,000 appointments for 73,000 patients to compute attendance rates and classify patients into categories of zero, low, medium, or high missed appointments. Demographic data on patients was then merged to analyze patterns between attendance and factors like age, distance from practice, and socioeconomic status.
This document provides an overview of statistics for social work research. It defines statistics as the science of developing knowledge through empirical data expressed quantitatively, based on probability theory. Statistics involves collecting, summarizing, and analyzing numerical data. Descriptive statistics summarize and describe data, while inferential statistics model patterns in data to draw inferences about populations. The document discusses the characteristics, functions, scope, limitations, and potential misuse of statistics.
What is statistics and how is the discipline of statistics different to machine learning? Statistics is the oldest kind on the block of data science. However, it is not as popular as machine learning or deep learning is. Nevertheless, there are countless applications of statistical science in the real world.
Agent based modelling is a very useful and flexible modelling technique. It is especially useful when modelling complex systems, such as societies or economies. This makes it particularly useful when modelling token economies. Agent based modelling can be a powerful tool for any ICO.
This slideshare has been produced by the Tesseract Academy (http://tesseract.academy), a company that educates decision makers in deep technical topics such as data science, analytics, and blockchain.
For more information about this topic also visit The Data Scientist:
http://thedatascientist.com/statistics-vs-machine-learning-two-worlds/
1) The document discusses the future of electronic health records (EHR) for research in New Zealand beyond 2014.
2) It notes that the PREDICT/VIEW research programme has collected EHR data on cardiovascular disease risk factors for over 250,000 participants in Auckland/Northland.
3) However, it cautions that a key challenge is ensuring the accuracy and completeness of data in the EHR, as data is often incomplete, inaccurate, or out of date.
LeAnna Kent - Using Network Analysis to Detect Kickback Schemes Among Medical...MLconf
This document describes a method for detecting kickback schemes using network analysis. It builds a graph with doctors as nodes and their connections based on shared patients. It then analyzes each doctor's egonet independently and applies an algorithm to select the subgraph with the strongest edges for each. These subgraphs are then ranked based on their strength scores to prioritize investigations into potential kickback schemes.
Prof Mendel Singer Big Data Meets Public Health and Medicine 2018 12-22mjbinstitute
Presentation by Prof. Mendel Singer of Case Western Reserve University, on the issue of "big data" in health care and policy research. Presented at the Myers-JDC-Brookdale Institute in Jerusalem.
Filling the gaps in translational researchPaul Agapow
- Translational research often focuses on early-stage problems that are interesting scientifically but do not address the most important problems in developing new therapies. This neglects later and more difficult stages of drug development where the largest costs and failures occur.
- More focus is needed on developing therapies for complex, systemic diseases and diverse patient populations using real-world data and approaches that incorporate biological complexity early in the process. Machine learning should be applied where it can have the most impact in reducing costs, such as predicting adverse events later in development.
- Efforts are also needed to build more diverse, representative datasets and use data science approaches like drug repurposing that have the potential to accelerate therapy development.
Multi morbidity - the notion of tacit knowledge - Magdalena Skrybant and Celi...NIHR CLAHRC West Midlands
Magdalena Skrybant and Celia Taylor stepped into the breach on the second day of our Scientific Advisory Group after one of our presenters was taken ill.
The document summarizes the activities and outputs of the NIHR Collaborations for Leadership in Applied Health Research and Care West Midlands (CLAHRC WM) program over the past year. Some key points:
- CLAHRC WM involves collaboration between hospitals, primary care, local authorities to implement and evaluate new interventions, with £20m in funding.
- It has produced over 140 academic papers, with a total impact factor of over 139,000. External grant income totaled over £8 million for the year and £47 million total.
- 83 postgraduate students were supported across various research themes including maternity/child health, mental health, chronic diseases, and implementation research.
-
The document discusses methodological challenges in assessing the effectiveness of interventions for coordinated care of chronic diseases. It summarizes preliminary findings from 81 reviews on interventions' impact on hospitalization rates. Key challenges include double counting of primary studies across reviews and determining appropriate ways to group heterogeneous interventions for analysis given interventions often have overlapping elements. Excluding reviews based solely on quality scores may overlook potentially useful outcomes data, requiring consideration of alternative strategies.
Presentation given by Dr Sam Watson and Dr Yen-Fu Chen at the latest CLAHRC WM Programme Steering Committee meeting on 15th April 2015, at the University of Warwick.
Dr John Ovretveit's critique on Dr Yen-Fu Chen's presentation on publication bias in service delivery research for the CLAHRC WM Scientific Advisory Group, 10th June 2015, Birmingham, UK
The first of a two-part talk from Richard Lilford and Sam Watson on modelling causal pathways in health services for the CLAHRC West Midlands Scientific Advisory Group meeting, 9th June 2015, Birmingham, UK
The document is a wedding invitation for Bhavish Sharma and Deepthi Sharma. It invites guests to attend pre-wedding celebrations including a Sangeet musical celebration on an unspecified date where guests can dance and enjoy music. It also invites guests to attend the wedding ceremony on April 6, 2015 at City Pride Garden in Ajmer, India to celebrate the new beginnings and love between Bhavish and Deepthi.
A talk on design choices for cluster randomised trials by Dr Alan Girling for the CLAHRC WM Scientific Advisory Group meeting, 9th June 2015, Birmingham, UK
This document provides an overview of basic statistical analyses that are commonly used for research projects, including descriptive and inferential statistics. Descriptive statistics like frequencies, percentages, means and standard deviations are used to summarize single variables. Inferential statistics like correlation, t-tests, chi-square, and logistic regression are used to determine relationships between variables and make inferences about populations. The document outlines when each statistical test is appropriate, how to interpret results, and how to report findings for common analyses like correlation, t-tests, chi-square, and logistic regression.
This document provides an overview of basic statistical concepts and techniques for analyzing data that are important for oncologists to understand. It covers topics such as types of data, measures of central tendency and variability, theoretical distributions, sampling, hypothesis testing, and basic techniques for analyzing categorical and numerical data, including t-tests, ANOVA, chi-square tests, correlation, and regression. The goal is to equip oncologists with fundamental statistical knowledge for handling, describing, and making inferences from medical data.
This document provides an introduction to biostatistics in health. It discusses:
- How data is collected through instruments which have limitations and human biases. Statistics help extract meaningful information from large amounts of raw data.
- Key concepts including populations, samples, variables, and different measurement scales. Variables can be qualitative taking categories like gender, or quantitative measured on interval/ratio scales.
- Descriptive statistics help summarize and present data through tables, graphs, and measures of central tendency and spread. Inferential statistics are used to draw conclusions beyond the sample studied.
- The importance of biostatistics in health fields like understanding diagnostic tests, clinical trials, epidemiology, and evidence-based practice. Statistics under
This document provides information about medical statistics including what statistics are, how they are used in medicine, and some key statistical concepts. It discusses that statistics is the study of collecting, organizing, summarizing, presenting, and analyzing data. Medical statistics specifically deals with applying these statistical methods to medicine and health sciences areas like epidemiology, public health, and clinical research. It also overview some common statistical analyses like descriptive versus inferential statistics, populations and samples, variables and data types, and some statistical notations.
Dive into our students' innovative project leveraging machine learning for heart disease prediction. Discover how advanced analytics and predictive modeling can revolutionize healthcare, providing early detection and personalized interventions for better patient outcomes. To learn more, do check out https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/.
Dive into an extensive analysis of heart disease classification, exploring key factors, trends, and predictive models for improved diagnosis and treatment strategies. Visit, https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/ for more
This NLP Project Presentation explores how Natural Language Processing (NLP) and Data Science are revolutionizing the prediction of heart disease. Discover how cutting-edge techniques are being used to analyze textual data, such as patient records and medical reports, to predict the likelihood of heart disease with unprecedented accuracy. For more details on data science Visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
1. The document discusses types of data and statistical analysis methods used in clinical research. It describes numerical and categorical data, as well as measures used to summarize and describe central tendency, variability, and distributions of data.
2. Hypothesis testing is introduced as a way to evaluate research questions through null hypotheses. The process involves selecting an appropriate statistical test to calculate a p-value to determine if the null hypothesis can be rejected or not.
3. Confidence intervals provide a range of values that is likely to include the true population estimate based on sample data, with a specified confidence level, most commonly 95%.
This document discusses using statistical process control (CUSUM) charts to monitor mortality rates at the level of individual general practitioners and health authorities. It describes how CUSUM charts could potentially have detected Harold Shipman, a GP who murdered over 200 patients, by spotting outliers in the routine mortality data. The document also discusses challenges in risk adjusting outcomes to account for differences in patient characteristics and casemix between providers. Accurately adjusting for factors like age, comorbidities, and emergency status is important for fair comparisons but difficult using only administrative data.
Study of the distribution and determinants of
health-related states or events in specified populations and the application of this study to control health problems.
John M. Last, Dictionary of Epidemiology
This document discusses a project that aims to predict re-admission of diabetes patients using machine learning. The project aims to help both patients and hospitals by providing a model to predict re-admission cases so hospitals can better prepare. The dataset contains over 10,000 observations on diabetes patients over 10 years. Several features like weight, payer code, and medical specialty will be dropped due to missing data. Other features like age, admission type, and discharge disposition will be consolidated. Feature engineering will also add a total number of visits feature and preprocess the data. The goal is to build a model that can help hospitals better manage resources and reduce costs and improve patient care.
Statistics.pdf.pdf for Research Physiotherapy and Occupational TherapySakhileKhoza2
This document discusses statistical concepts and how statisticians can assist with research studies. It begins by noting that statistical analysis is common in health research and that medical practitioners need a basic understanding of statistics. It then discusses how statisticians can help with all stages of a study design, ensuring results are comparable and generalizable. The document outlines different types of data - categorical, numerical, count - and how data can be summarized using proportions, rates, and ratios. It provides examples of summarizing binary outcome data from studies using tables, risks, risk differences, risk ratios, and odds ratios. Statisticians are emphasized as important consultants early in planning studies to optimize design and analysis.
BASIC STATISTICS AND THEIR INTERPRETATION AND USE IN EPIDEMIOLOGY 050822.pdfAdamu Mohammad
This document provides an introduction to basic statistical concepts and their use in epidemiology. It discusses different types of data including categorical, quantitative, discrete, and continuous data. It also covers measures of central tendency (mean, median, mode) and dispersion (range, variance, standard deviation). The document introduces the concepts of skewness and the normal distribution. It then discusses inferential statistics, hypothesis testing, and parametric vs non-parametric tests. Key statistical tests are outlined depending on whether populations are related or independent. The overall goal is to provide health professionals with foundational statistical knowledge for investigating medical science.
Dive into the forefront of healthcare analytics with our latest project showcase on heart disease classification. Our students at the Boston Institute of Analytics have delved deep into the complexities of heart disease diagnosis using advanced data science and artificial intelligence techniques. Explore the innovative methodologies, insightful findings, and impactful solutions presented in this collection of projects. From predictive modeling to risk assessment, these projects demonstrate the power of data-driven approaches in revolutionizing healthcare. To learn more about our data science and artificial intelligence programs, visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/.
Dive into the forefront of healthcare analytics with our latest project showcase on heart disease classification. Our students at the Boston Institute of Analytics have delved deep into the complexities of heart disease diagnosis using advanced data science and artificial intelligence techniques. Explore the innovative methodologies, insightful findings, and impactful solutions presented in this collection of projects. From predictive modeling to risk assessment, these projects demonstrate the power of data-driven approaches in revolutionizing healthcare. To learn more about our data science and artificial intelligence programs, visit https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/.
This document provides an overview of key concepts in epidemiology and statistics as they relate to nutritional epidemiology. It discusses random error and how statistics are used to estimate effects and account for biases in epidemiologic studies. Specific topics covered include point estimates, confidence intervals, p-values, statistical hypothesis testing, selection bias, information bias, and confounding. Examples are provided to illustrate concepts like how selection bias can influence estimates of vaccine efficacy. The roles of statistics in estimating effects, accounting for biases, and assessing the role of chance in epidemiologic studies are also summarized.
This document provides an overview of basic statistics, including descriptive and inferential statistics. Descriptive statistics such as frequencies, percentages, means and standard deviations are used to summarize single variables. Inferential statistics such as correlation, t-tests, chi-square, and logistic regression are used to test hypotheses, determine associations between variables, and make predictions. The document explains when each statistical test is appropriate and how to interpret and report the results.
Introduction to Data Management in Human EcologyKern Rocke
This document provides an introduction to data management concepts in human ecology. It defines data and describes common data types like qualitative and quantitative data. It also discusses topics like sources of data, types of statistical analyses, strategies for computer-aided analysis, principles of statistical analysis, and interpreting p-values. Examples of statistical programs and various statistical analysis methods for comparing groups and exploring relationships between variables are also outlined.
Data Con LA 2019 - Best Practices for Prototyping Machine Learning Models for...Data Con LA
Medical institutions, universities and software giants like Google and Microsoft are dedicating increasing resources to machine learning for healthcare. This is a very exciting but relatively young field. However, best practices for methods and reporting of results are not yet fully established. I have 2.5 years of experience as data scientist at a national cancer center working on clinical data, evaluating external vendors and peer reviewing machine learning in healthcare papers. The talk gives an overview of best practices in prototyping machine learning models on data from the patient electronic health record (EHR). The topics addressed are:1. Introduction to the EHR2. Overview of machine learning applications to the EHR3. Cohort definition for survival problems4. Data cleaning5. Performance metricsExcerpts of papers from renowned institutions will be critically reviewed. The material is intended to be useful not only to machine learning for healthcare professionals, but to practitioners dealing with very unbalanced dataset in the temporal domain. For example, customer churn prediction can be modeled as survival problem.
Similar to Modelling causal pathways in health services part 2 - Sam Watson (20)
This document describes a comparative analysis project that evaluated whether a rapid qualitative analysis approach could deliver findings more quickly than a traditional in-depth analysis method. The rapid analysis used summary templates to analyze data within a short timeframe, while the in-depth analysis used coding and the Framework method. The results found that rapid analysis was much faster for data management but took longer for interpretation. Both methods produced similar key issues and recommendations, but the in-depth analysis provided more specific, context-informed findings. The document reflects on the applications and limitations of rapid qualitative analysis approaches.
This document discusses moving from current ad-hoc healthcare systems to a national learning health system. It outlines challenges facing healthcare like rising costs and an aging population. Current digital health data is underused. Examples show how data can enable epidemiological research, evaluate policies, and support clinical trials. Bigger efforts are needed to create a prototype national asthma learning health system. This would use various data sources to monitor asthma burden, improve outcomes and reduce deaths. The goal is an integrated system that continuously learns from patient care to drive discovery and improve value.
The document summarizes the history of healthcare development in Birmingham over the past century, from the origins of early hospitals to the planning and construction of the new Queen Elizabeth Hospital. It describes:
- The early voluntary and municipal hospitals established in Birmingham from the 18th century to treat the poor.
- The social, medical, and technological changes in the early 20th century that increased demand for hospital services.
- The controversies over proposals in the 1920s-30s to build a new united hospital center to replace overcrowded facilities.
- The planning process that ultimately led to the construction of the original Queen Elizabeth Hospital, which opened in 1938.
- The need for renewal that resulted in
This document discusses stepped wedge cluster randomized trial designs and recent related research. It provides background on cluster studies over time and describes traditional parallel and crossover cluster designs. It then explains classic and modified stepped wedge designs, issues in methodology, and recent related papers addressing topics like sample size calculations and extending CONSORT guidelines. Finally, it proposes future projects on developing CONSORT standards for stepped wedge trials and exploring designs beyond the standard stepped wedge like "dog leg" and ladder designs to improve efficiency.
4 - Further info - Setting up research in the NHS: practical and ethical cons...NIHR CLAHRC West Midlands
This document provides information about a training event on setting up research in the NHS, including practical and ethical considerations. It outlines the day's program, with a lunch break from 12-1pm, and information about an evaluation form that will be circulated by email after the event. It also provides details about an online NIHR ethics learning module, and further information sources like the CLAHRC West Midlands website and how to sign up for their news blog or email the contact listed.
3 - UoB - Setting up research in the NHS: practical and ethical considerationsNIHR CLAHRC West Midlands
Presentation by Prof Heather Draper, University of Birmingham - exploring information provided to research participants/patients during informed consent process
1 - HRA - Setting up research in the NHS: practical and ethical considerationsNIHR CLAHRC West Midlands
The Health Research Authority (HRA) was established in 2011 to simplify the process for approval of health research in the NHS in England. The HRA aims to reduce the time and cost of setting up studies through a single application process called HRA Approval. When fully implemented, HRA Approval will provide assurance to NHS organizations in England that a study can be undertaken, replacing other approval processes. The presentation provides updates on the phased implementation of HRA Approval for different study types.
CLAHARC WM Capacity Development Strategy - Nathalie Maillard and Tom MarshallNIHR CLAHRC West Midlands
Presentation to Programme Steering Committee on 14th January 2016 on the CLAHARC WM Capacity Development Strategy. Given by Nathalie Maillard and Tom Marshall.
Feedback from 'speed dating' - Postgrad / Early Career Researcher event 19th ...NIHR CLAHRC West Midlands
The document outlines the programme for a postgraduate and early career researcher event held by CLAHRC West Midlands. The programme includes sessions on ethics, collaboration and engagement, public and patient involvement, networking and peer support, and research methodology. Attendees will discuss topics like navigating ethics reviews, balancing clinical and academic work, engaging stakeholders, and designing sound studies.
The document summarizes a trial called the CO-OPS Trial which studied the effects of fatigue on radiologists' performance in breast cancer screening. It tested whether reversing the order radiologists read screening mammograms (to optimize performance patterns) and taking breaks could help address the normal vigilance decrement seen in tasks requiring sustained attention over long periods. The document outlines the trial methods, results, and interpretations that will be presented on fatigue and changing case order in breast screening radiology.
Rapid qualitative analysis vs the 'traditional approach': early findings and ...NIHR CLAHRC West Midlands
Dr Beck Taylor of Theme 1, Maternity and Child Health, presented her latest project, comparing a rapid approach to synthesising evidence from qualitative research to traditional research methods, presented at CLAHRC WM Programme Steering Committee meeting, 22nd October 2015
This document describes the SPACER study which aims to identify the benefits and disadvantages of electronic prescribing and medication administration (EPMA) compared to paper-based systems. The SPACER study will consist of 3 strands conducted over 3 years: 1) An ethnographic study to observe organizational changes and staff perspectives with EPMA implementation. 2) A data envelopment analysis study to assess the impact of EPMA on healthcare service efficiency. 3) A Drugs, Data, Decisions study to identify changes to key performance measures, processes, reporting and decision making regarding the medication process before, during and after EPMA implementation.
Presentation: Dr Amanda Daley, Effectiveness of regular weighing and feedback by community midwives in preventing excessive gestational weight gain (POPS 2) – Theme 1 Maternity & Child Health
Aileen Clarke and Sian Taylor-Phillips' presentation development of a preference based well-being measure for the CLAHRC WM Scientific Advisory Group, 10th June 2015, Birmingham, UK
Giloy in Ayurveda - Classical Categorization and SynonymsPlanet Ayurveda
Giloy, also known as Guduchi or Amrita in classical Ayurvedic texts, is a revered herb renowned for its myriad health benefits. It is categorized as a Rasayana, meaning it has rejuvenating properties that enhance vitality and longevity. Giloy is celebrated for its ability to boost the immune system, detoxify the body, and promote overall wellness. Its anti-inflammatory, antipyretic, and antioxidant properties make it a staple in managing conditions like fever, diabetes, and stress. The versatility and efficacy of Giloy in supporting health naturally highlight its importance in Ayurveda. At Planet Ayurveda, we provide a comprehensive range of health services and 100% herbal supplements that harness the power of natural ingredients like Giloy. Our products are globally available and affordable, ensuring that everyone can benefit from the ancient wisdom of Ayurveda. If you or your loved ones are dealing with health issues, contact Planet Ayurveda at 01725214040 to book an online video consultation with our professional doctors. Let us help you achieve optimal health and wellness naturally.
5-hydroxytryptamine or 5-HT or Serotonin is a neurotransmitter that serves a range of roles in the human body. It is sometimes referred to as the happy chemical since it promotes overall well-being and happiness.
It is mostly found in the brain, intestines, and blood platelets.
5-HT is utilised to transport messages between nerve cells, is known to be involved in smooth muscle contraction, and adds to overall well-being and pleasure, among other benefits. 5-HT regulates the body's sleep-wake cycles and internal clock by acting as a precursor to melatonin.
It is hypothesised to regulate hunger, emotions, motor, cognitive, and autonomic processes.
Debunking Nutrition Myths: Separating Fact from Fiction"AlexandraDiaz101
In a world overflowing with diet trends and conflicting nutrition advice, it’s easy to get lost in misinformation. This article cuts through the noise to debunk common nutrition myths that may be sabotaging your health goals. From the truth about carbohydrates and fats to the real effects of sugar and artificial sweeteners, we break down what science actually says. Equip yourself with knowledge to make informed decisions about your diet, and learn how to navigate the complexities of modern nutrition with confidence. Say goodbye to food confusion and hello to a healthier you!
Spontaneous Bacterial Peritonitis - Pathogenesis , Clinical Features & Manage...Jim Jacob Roy
In this presentation , SBP ( spontaneous bacterial peritonitis ) , which is a common complication in patients with cirrhosis and ascites is described in detail.
The reference for this presentation is Sleisenger and Fordtran's Gastrointestinal and Liver Disease Textbook ( 11th edition ).
Travel vaccination in Manchester offers comprehensive immunization services for individuals planning international trips. Expert healthcare providers administer vaccines tailored to your destination, ensuring you stay protected against various diseases. Conveniently located clinics and flexible appointment options make it easy to get the necessary shots before your journey. Stay healthy and travel with confidence by getting vaccinated in Manchester. Visit us: www.nxhealthcare.co.uk
Osvaldo Bernardo Muchanga-GASTROINTESTINAL INFECTIONS AND GASTRITIS-2024.pdfOsvaldo Bernardo Muchanga
GASTROINTESTINAL INFECTIONS AND GASTRITIS
Osvaldo Bernardo Muchanga
Gastrointestinal Infections
GASTROINTESTINAL INFECTIONS result from the ingestion of pathogens that cause infections at the level of this tract, generally being transmitted by food, water and hands contaminated by microorganisms such as E. coli, Salmonella, Shigella, Vibrio cholerae, Campylobacter, Staphylococcus, Rotavirus among others that are generally contained in feces, thus configuring a FECAL-ORAL type of transmission.
Among the factors that lead to the occurrence of gastrointestinal infections are the hygienic and sanitary deficiencies that characterize our markets and other places where raw or cooked food is sold, poor environmental sanitation in communities, deficiencies in water treatment (or in the process of its plumbing), risky hygienic-sanitary habits (not washing hands after major and/or minor needs), among others.
These are generally consequences (signs and symptoms) resulting from gastrointestinal infections: diarrhea, vomiting, fever and malaise, among others.
The treatment consists of replacing lost liquids and electrolytes (drinking drinking water and other recommended liquids, including consumption of juicy fruits such as papayas, apples, pears, among others that contain water in their composition).
To prevent this, it is necessary to promote health education, improve the hygienic-sanitary conditions of markets and communities in general as a way of promoting, preserving and prolonging PUBLIC HEALTH.
Gastritis and Gastric Health
Gastric Health is one of the most relevant concerns in human health, with gastrointestinal infections being among the main illnesses that affect humans.
Among gastric problems, we have GASTRITIS AND GASTRIC ULCERS as the main public health problems. Gastritis and gastric ulcers normally result from inflammation and corrosion of the walls of the stomach (gastric mucosa) and are generally associated (caused) by the bacterium Helicobacter pylor, which, according to the literature, this bacterium settles on these walls (of the stomach) and starts to release urease that ends up altering the normal pH of the stomach (acid), which leads to inflammation and corrosion of the mucous membranes and consequent gastritis or ulcers, respectively.
In addition to bacterial infections, gastritis and gastric ulcers are associated with several factors, with emphasis on prolonged fasting, chemical substances including drugs, alcohol, foods with strong seasonings including chilli, which ends up causing inflammation of the stomach walls and/or corrosion. of the same, resulting in the appearance of wounds and consequent gastritis or ulcers, respectively.
Among patients with gastritis and/or ulcers, one of the dilemmas is associated with the foods to consume in order to minimize the sensation of pain and discomfort.
Are you looking for a long-lasting solution to your missing tooth?
Dental implants are the most common type of method for replacing the missing tooth. Unlike dentures or bridges, implants are surgically placed in the jawbone. In layman’s terms, a dental implant is similar to the natural root of the tooth. It offers a stable foundation for the artificial tooth giving it the look, feel, and function similar to the natural tooth.
Summer is a time for fun in the sun, but the heat and humidity can also wreak havoc on your skin. From itchy rashes to unwanted pigmentation, several skin conditions become more prevalent during these warmer months.
Nano-gold for Cancer Therapy chemistry investigatory projectSIVAVINAYAKPK
chemistry investigatory project
The development of nanogold-based cancer therapy could revolutionize oncology by providing a more targeted, less invasive treatment option. This project contributes to the growing body of research aimed at harnessing nanotechnology for medical applications, paving the way for future clinical trials and potential commercial applications.
Cancer remains one of the leading causes of death worldwide, prompting the need for innovative treatment methods. Nanotechnology offers promising new approaches, including the use of gold nanoparticles (nanogold) for targeted cancer therapy. Nanogold particles possess unique physical and chemical properties that make them suitable for drug delivery, imaging, and photothermal therapy.
- Video recording of this lecture in English language: https://youtu.be/Pt1nA32sdHQ
- Video recording of this lecture in Arabic language: https://youtu.be/uFdc9F0rlP0
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Breast cancer: Post menopausal endocrine therapyDr. Sumit KUMAR
Breast cancer in postmenopausal women with hormone receptor-positive (HR+) status is a common and complex condition that necessitates a multifaceted approach to management. HR+ breast cancer means that the cancer cells grow in response to hormones such as estrogen and progesterone. This subtype is prevalent among postmenopausal women and typically exhibits a more indolent course compared to other forms of breast cancer, which allows for a variety of treatment options.
Diagnosis and Staging
The diagnosis of HR+ breast cancer begins with clinical evaluation, imaging, and biopsy. Imaging modalities such as mammography, ultrasound, and MRI help in assessing the extent of the disease. Histopathological examination and immunohistochemical staining of the biopsy sample confirm the diagnosis and hormone receptor status by identifying the presence of estrogen receptors (ER) and progesterone receptors (PR) on the tumor cells.
Staging involves determining the size of the tumor (T), the involvement of regional lymph nodes (N), and the presence of distant metastasis (M). The American Joint Committee on Cancer (AJCC) staging system is commonly used. Accurate staging is critical as it guides treatment decisions.
Treatment Options
Endocrine Therapy
Endocrine therapy is the cornerstone of treatment for HR+ breast cancer in postmenopausal women. The primary goal is to reduce the levels of estrogen or block its effects on cancer cells. Commonly used agents include:
Selective Estrogen Receptor Modulators (SERMs): Tamoxifen is a SERM that binds to estrogen receptors, blocking estrogen from stimulating breast cancer cells. It is effective but may have side effects such as increased risk of endometrial cancer and thromboembolic events.
Aromatase Inhibitors (AIs): These drugs, including anastrozole, letrozole, and exemestane, lower estrogen levels by inhibiting the aromatase enzyme, which converts androgens to estrogen in peripheral tissues. AIs are generally preferred in postmenopausal women due to their efficacy and safety profile compared to tamoxifen.
Selective Estrogen Receptor Downregulators (SERDs): Fulvestrant is a SERD that degrades estrogen receptors and is used in cases where resistance to other endocrine therapies develops.
Combination Therapies
Combining endocrine therapy with other treatments enhances efficacy. Examples include:
Endocrine Therapy with CDK4/6 Inhibitors: Palbociclib, ribociclib, and abemaciclib are CDK4/6 inhibitors that, when combined with endocrine therapy, significantly improve progression-free survival in advanced HR+ breast cancer.
Endocrine Therapy with mTOR Inhibitors: Everolimus, an mTOR inhibitor, can be added to endocrine therapy for patients who have developed resistance to aromatase inhibitors.
Chemotherapy
Chemotherapy is generally reserved for patients with high-risk features, such as large tumor size, high-grade histology, or extensive lymph node involvement. Regimens often include anthracyclines and taxanes.
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2. Modelling
• Representations of the world
– Models of data and models of phenomena
• Make our assumptions clear and transparent
3. Why?
• For policy we need a causal effect
• Usually ATE or ATET
– E.g. 𝐸 𝑌 𝐷1 − 𝐸 𝑌 𝐷0
• Barriers:
– Observational data
– Can’t measure endpoints
• But data, even observational data, tell us something
5. Outline
• Interested in the effect X->Y
• Some information on 𝑝𝑞
• Lots of information on 𝑝
X Z Y
p q
6. Outline
• Interested in X->Y
• But confounded by 𝑢
• Can still identify causal effect by making use of 𝑍
X Z Y
u
7. Outline
• Model describes relationships between variables
• Can combine information on different data sources
Intervention
Upstream
endpoint
Patient
outcomes
10. CPOE ME ADE
𝑅𝑅 =
𝑃(𝐴𝐷𝐸|𝐶𝑃𝑂𝐸 = 1)
𝑃(𝐴𝐷𝐸|𝐶𝑃𝑂𝐸 = 0)
=
𝑃(𝐴𝐷𝐸|𝑀𝐸)𝑃(𝑀𝐸|𝐶𝑃𝑂𝐸 = 1)
𝑃(𝐴𝐷𝐸|𝑀𝐸)𝑃(𝑀𝐸|𝐶𝑃𝑂𝐸 = 0)
=
𝑃(𝑀𝐸|𝐶𝑃𝑂𝐸 = 1)
𝑃(𝑀𝐸|𝐶𝑃𝑂𝐸 = 0)
Using only studies with ADE endpoint Using studies with ADE and ME endpoint
18. Weekend mortality
• Many studies have examined the effect of weekend admission on
risk of mortality (at least 105).
• In the UK the estimated relative risk 1.1-1.2 (Meacock, Doran, and
Sutton, 2015, Freemantle et al., 2012)
• Confounded by patient health
19. Weekend mortality
• Examine data that measure day of admission, mortality, and errors
• SPI2 data
– Patients aged >65 with acute respiratory illness
• Crude mortality relative risk: 1.17 [0.79, 1.60]
• Adjusted (age, sex, number of comorbidities) RR: 1.19 [0.79, 1.75]
• Similar point estimates. Under powered (n=670)
21. Weekend mortality
• Assumption of no relationship between errors and health may be too
strong:
– Sicker patients more exposed to risk of error
– Sicker patients more likely to die, less exposed to risk of error
Weekend
admission
Errors Mortality
Health
22. Weekend mortality
• Examine performance of estimators under different assumptions
using simulated data
– Two types of individual: sick v healthy. Sick 4x more likely to die.
• Only when there is no unobserved confounding due to health is the
‘standard’ estimator preferred, even with fairly large relationship
between errors and health.
• No evidence of a difference in errors by weekend or by health in
SPI2 data.
24. Expert Elicitation
• What happens when there are no data?
• Can use expert elicitation.
Figure: Example group subjective prior,
from Yao et al. (2012) BMJ Qual Saf. See
also Lilford et al. (2014) BMC Health Serv
Res.