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Title

INDIVIDUAL-BASED RISK MODELS FOR CRIME PREVENTION AND MEDICAL PROGNOSIS

Creator

Haro Alonso, David

Date

2018, 2018-05

Description

Parallel trends are currently taking place in the fields of crime and medicine, in which the focus is shifting from a reactive stance to a...
Show moreParallel trends are currently taking place in the fields of crime and medicine, in which the focus is shifting from a reactive stance to a proactive one. Both fields have traditionally been reactive, with police responding to 911 calls after a crime has occurred, and patients seeking medical care after symptoms have already appeared. In the field of crime, social-services programs, law-enforcement agencies, sociologists, and criminologists are studying ways to prevent crime, instead of merely reacting to it. A similar trend, known as preventive medicine, is concerned with addressing the causes of disease and not just focusing on treatment of disease that has already emerged. If crime and disease are to be prevented, it is important to understand the early warning signs of risk, to anticipate and treat problems before they occur. This can be accomplished via mathematical risk models that can evaluate an individual’s risk based on leading indicators. In this thesis I develop such models for two real-world problems in crime prevention and one in preventive medicine. A major focus of this thesis is to emphasize the accuracy of the ranking of risk for situations in which the allocation of resources must be prioritized to the highest-risk individuals. This is especially true in a social-services program designed to reduce crime, where the number of available social workers may be limited. In the first part of the thesis, I describe a novel method of risk modeling based on the probabilistic framework of a conditional random field, in which a machine-learning regressor is embedded. This is applicable in situations where an individual’s risk of an adverse outcome is partly dependent on the risk levels of others. We have applied this technique to develop a model that assesses an individual’s near-term risk of becoming a victim or arrestee in a shooting or homicide in Chicago. The model was developed as an informational tool for a pilot crime-prevention program that aims to offer social services to at-risk persons with the aim of providing opportunities for life changes that may reduce their crime risk. In the second part of the thesis, I describe a new model with a similar goal—to identify individuals at risk of involvement in crime—but aims to provide information for use in smaller cities that have a more typical array of crime concerns than Chicago. We developed the model as part of a current partnership with the Elgin Police Department, where a social-services intervention program under development will incorporate our model in identifying persons who might benefit from assistance. In the last part of the thesis, I describe a risk assessment algorithm for the medical field, which we developed in partnership with Cedars-Sinai Medical Center, Los Angeles, CA. In this work, we sought to demonstrate to the cardiology field (and the broader medical field) that machine learning can provide a better framework for risk stratification in medicine than traditional statistical methods such as logistic regression, which are the norm in that field. We also showed that, contrary to concerns by medical practitioners, machine learning can provide a solution that is easy to interpret.
Ph.D. in Electrical Engineering, May 2018
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