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PhD Viva Presentation
- 1. Evaluating the impact of home telehealth monitoring and clinical predictive models for patients with chronic heart failure JOHN STAMFORD
- 2. Aims Evaluate the efficacy of HTM for patients with CHF under the care of the cardiology department at the Castle Hill hospital Explore the state-of-the-art predictive models for patients with Chronic Heart Failure (CHF)
- 3. Motivation Social and Economical Impact High number of people suffering High cost of care Expected to rise HTM Efficacy Clinical Interest Commercial Interest Political Interest Background
- 4. Approach Data Science Data Extraction Statistics Machine Learning Evaluation Statistical Analysis Cohort Comparison Survival Analysis Time-to-first-event Repeat Event Analysis Risk Model Development Statistical Models Machine Learning Models Explore Simulated Data Clinical Data Univariate Bivariate Multivariate
- 5. Challenges Real Clinical Data Missing Values Highly Skewed Extreme Values Measuring HTM efficacy No Randomised Control Trial First Event Analysis Need a more holistic picture Risk Models Definition of risk Reported metrics Clinical Computer Science Reliance on explanatory models
- 6. Dataset Clinical Dataset Cardiology Department at Castle Hill Hospital Longitudinal Unique Location Data Contained
- 7. Propensity Matching Goal Match Patients Challenge Invariant under permutation
- 10. Risk Modelling What is risk? Clinical Models Logistic Regression Dual Purpose Monotonic Relationship Computer Science Model more complex relationships Perspective
- 11. Risk Modelling (Simulated Data)
- 12. Risk Modelling (Clinical Data)
- 13. Risk Modelling (Clinical Data)
- 14. Risk Modelling (Clinical Data)
- 15. Sensitivity vs Specificity Clinical Decision Sensitivity Proportion of high risk patients who are identified as high risk Specificity Proportion of low risk patients who are identified as high risk Therefore Decision/Outcome Specific
- 16. Contributions Aim 1 - Evaluate the efficacy of HTM for patients with CHF under the care of the cardiology department at the Castle Hill hospital Efficacy can be assessed using a longitudinal dataset PSM can effectively match patients (reduce differences in baseline characteristics) But – need to ensure the algorithm is ‘invariant under permutation’ HTM efficacy Patients have better survival rates but more hospitalisation events Spend more time alive and out of hospital
- 17. Contributions Aim 2 - Explore the state-of-the-art predictive models for patients with CHF Relationship between independent and dependant variables is not always linear / monotonic Logistic Regression can perform similar to MLPs, and better than naïve Bayes each model will derived different decision boundaries Evaluation Methods Precision, Recall, Sensitivity and Specificity are threshold dependent Comparison – Area under the Curve
- 18. Future Work Propensity score matching Test with other datasets Home Telehealth Monitoring which functions of the HTM equipment, or support package, facilitated these improved outcomes? Can we incorporate risk into the evaluation of HTM Need larger numbers of patients Variable selection Unstructured Data (MIMIC Dataset)
Editor's Notes
- 900,000 people in England and Wales UK is estimated at £563 million per year Efficacy is still unknown WSD - At least three million people with Long Term Conditions and/or social care needs could benefit from using telehealth and telecare The early indications show that if used correctly telehealth can deliver a 15% reduction in A&E visits, a 20% reduction in emergency admissions, a 14% reduction in elective admissions, a 14% reduction in bed days and an 8% reduction in tariff costs. More strikingly they also demonstrate a 45% reduction in mortality rates. 6191 patients and 238 GP External Review - the study found less evidence that telehealth will reduce costs (was finding but not statistically significant) Political Care Act 2014 Government Digital Strategy (2013) Department of Health’s Digital Strategy: Leading the Culture Change in Health and Care (2012) Department of Health’s Power of Information (2012).