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Interpreting Big Data: Meaningful models for Healthcare, Criminology, and Beyond

 

We are designing machine learning algorithms whose results are meaningful and intuitive to human experts, yet have predictive accuracy on par with the state-of-the-art machine learning algorithms. These models are parsimonious (sparse), as humans can handle only a handful of cognitive entities at one time, and are in the forms of a decision list or linear scoring system.
 
These algorithms have been used to design predictive models for: 
 
i) Medical condition prediction: predicting the next medical condition before the patient experiences it.
 
A Hierarchical Model for Association Rule Mining of Sequential Events: An Approach to Automated Medical Symptom Prediction. Annals of Applied Statistics.
 
ii) Stroke prediction in patients with atrial fibrillation: An improvement over the CHADS_2 score
 
An Interpretable Stroke Prediction Model using Rules and Bayesian Analysis. Proceedings of AAAI Late Breaking Track.
 
iii) Predicting violent crime in youth raised in out-of-home care.
 
iv) Highly imbalanced classification
http://arxiv.org/abs/1403.3378
 
Supersparse Linear Integer Models for Predictive Scoring Systems. Proceedings of AAAI Late Breaking Track.
 
Investigators: