1. Healthcare and Medicine: New frontiers for analytics and data mining
Akshay Bhat
With Prof. Ramin Zabih and Dr. George Shih

2. Definitions Biology Medicine Healthcare Genes, Proteins and Organisms
How do proteins work? What differentiates different strains of viruses?
Medicine
Drugs, Devices and Surgeries
How can we correctly diagnose a condition? How can we treat a condition?
Healthcare
Surveillance, Clinical trials and Payment policies
Is nexium better than prilosec? How should we pay hospitals, when they commit errors.

3. This talk is about Medicine and Healthcare!
Not Bioinformatics
Understanding the challenges faced by the Healthcare system.
Who pays whom for what?
What is appropriate care?
How can we use data mining and analytics to identify issues with the healthcare system?
Detecting outright fraud (Already a huge market)
Areas for improving efficiency
Decreasing overutilization

4. Ongoing changes in Healthcare & Medicine
Research on EMR/EHR vs. data captured by EMR/EHR
First one is a usability problem
The second one is analytics/data mining problem
Escaping the EHR Trap — The Future of Health IT
Shifting focus to using data captured by EMR/EHR
Perspective in New England Journal of Medicine
A Glimpse of the Next 100 Years in Medicine
Editorial envisioning a Data Driven Future
New England Journal of Medicine (December, 2012)

5. Hierarchy of Medical/Healthcare data
Size
Availability
Dataset
~ few Millions
Easily available
Census, Social Security, Cause of death records
~ 10 – 100 M
Easily available for research
Structured (Codes) Medicare, State, Insurance records, Prescription data.
~ 100 – 1000 M
Requires affiliation
Unstructured data (text) Discharge notes, Radiology/Lab reports.
Few Billion to Trillion
Requires affiliation & special arrangement
Signals: Lab values, Physiologic time series, CT/MRI/Ultrasound scans.
Infinite ?
Very hard, Collect yourself
Smartphone, Fitness, Heart rate sensor data

6. Easily available for research
This talk is about
Size
Availability
Dataset
~ 10 – 300 M
Easily available for research
Structured (Codes) Medicare, State, Insurance records, Prescription data.
~ 100 – 1000 M
Requires affiliation
Unstructured data (text) Discharge notes, Radiology/Lab reports.
Huge opportunity in next 10 years as EMR/EHR are adopted and standardized

7. Three projects Project Dataset
Determining optimal protocol for CT Imaging. (Multi-label semi supervised classification problem)
Imaging orders (Text) from Weill Cornell Radiology,
Building Predictive models for assessing risk of readmission (Classification problem)
De-identified publicly available hospitalization records from California and Florida
Building analytical tools for understanding healthcare systems
(Data Mining / Data Analysis/ User Interface development)
De-identified publicly available records

8. Determining optimal protocol for CT Imaging
Using short-text description of diagnoses and symptom, recommend an appropriate CT Imaging protocol
A Multi-label classification problem.
Insight: The domain of all possible symptoms and diagnoses is very large

9. Determining optimal protocol for CT Imaging
Extract UMLS (Unified Medical Language System) concepts from short texts
Build a graph to represent relationships present in UMLS between different concepts. Such anatomical hierarchy and relations between location of diseases and diseases.
Semi-supervised label propagation for learning: Assign labels to concepts in the graph using training data, propagate labels over the over the graph.

10. Determining optimal protocol for CT Imaging
Performs better than baseline for Mean Reciprocal Rank metric.
Drawbacks: Protocols are not consistent across institutions or vendors.
Represent protocols as structures and use structure learning SVM.
Presented at a KDD 2011 Workshop and as an abstract at RSNA conference

11. Predicting risk of readmission upon discharge
Unplanned readmission are frequent.
A 30 day readmission is generally defined as admission within 30 days of discharge from a hospital.
Decreasing readmission is a big focus of current efforts, since hospitals get paid twice.

12. Predicting risk of readmission upon discharge
Can we predict risk of readmission upon discharge?
Useful for understanding which patients we should follow up closely.
Important for risk-adjustment, to avoid unnecessarily penalizing hospitals which treat complex patients.

13. Predicting risk of readmission upon discharge
Experiment
26 M hospitalizations California & Florida
Linear SVM models for predicting readmission
Hospitalizations from California for training/validations
Hospitalizations from Florida as test set
Results
AUC scores 0.70 on test set
Variation in predictive ability across diagnoses
E.g. Readmission following appendectomy was difficult while readmission following diabetes was predictable.

14. Questions ?