How Deep Learning is Improving Medical Diagnosis and Saving Lives
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Deep learning is impacting healthcare by helping identify high-risk patients. A proposed solution uses an interpretable deep learning model called RETAIN that uses attention mechanisms. RETAIN processes patient data like claims and lab results to provide clinically interpretable predictions and attention weights that mimic how doctors make decisions. This helps supplement human experts and allows for more proactive care of high-risk patients to improve outcomes and lower costs compared to traditional rule-based approaches. Trade-offs include needing more granular disease data and addressing class imbalances.
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How Deep Learning is Improving Medical Diagnosis and Saving Lives
- 1. Deep Diagnosis How is Deep Learning Impacting Medical Domain and Saving Lives R a g h a v B a l i
- 2. Agenda Problem Statement Challenges Proposed Solution Trade-offs KeyTakeaways
- 3. Problem Statement US population spends the highest per capita upon medical expenses.This is despite improvements in the overall ecosystem and medical science.Also, every year millions of dollars are spent on medical issues which are preventable. Multiple preventive healthcare programs are launched every year with limited success. Can Data Science help improve effectiveness of such programs by identifying high risk population cohorts?
- 4. Challenges General skeptism towards technology/AI driven solutions Interpretability of AI driven solutions Disease incidence can be influenced by many factors Immense Domain Knowledge requirements
- 5. ~ USD 3Trillion ~USD 1.1Trillion ~USD 300 Billion Evaluation Criteria ROC/AUC METRICS Addressable Scope Recall Interpretability Healthcare Spends Hospitalization Spends Heart Failure, Diabetes, etc.
- 6. Proposed Solution An end to end Deep Learning based interpretable framework: Which addresses concerns of interpretability [using attention] Which maintains performance levels of State of the Art DL models Reduces impact of knowledge bottleneck
- 7. RETAIN* : Architecture RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism Choi et al.
- 8. RETAIN* : Architecture RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism Choi et al. Code Level Attention Branch Visit Level Attention Branch
- 9. RETAIN* : Best of BothWorlds MultilevelAttention Architecture Clinically interpretable outputs SOTA performance Mimics human decisioning process Supplements human in the loop
- 10. Pipeline demographics Med Claims Rx Claims Lab Results RAW DATA Preprocess Sequence Builder Attention based DL Model PREPROCESSING ENGINE MODEL BUSINESS LAYER Provider Interface Actuarial Interface Preventive Plans Interpretability
- 11. Impact Current State Rule BasedTargeting Post-facto Action Plan Lesser Coverage Proposed State Pro-active Actions Address High Risk Patients Lift in number of effective enrollments Overall Cost benefits
- 12. Tradeoffs Disease Level Granularity DataVolume Under-sampling due to Class Imbalance Social Determinants/Bio-markers
- 13. KeyTakeaways Levels of Attention : RETAIN Deep Learning and Healthcare More Data compliments Domain Knowledge
Editor's Notes
- Sources : https://www.cms.gov/research-statistics-data-and-systems/statistics-trends-and-reports/nationalhealthexpenddata/nhe-fact-sheet.html https://www.cdc.gov/dhdsp/data_statistics/fact_sheets/fs_heart_failure.htm http://www.diabetes.org/diabetes-basics/statistics/
- Retain: Reverse Time Attention Retain* : Bi-directional Time Attention
- Retain: Reverse Time Attention Retain* : Bi-directional Time Attention
- Retain: Reverse Time Attention Retain* : Bi-directional Time Attention