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INDIVIDUAL-BASED RISK MODELS FOR CRIME PREVENTION AND MEDICAL PROGNOSIS
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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Mothers’ Vaccination Decision: The Relation Between Science Skepticism, Social Networks, Vaccination Beliefs, and Fear of ASD
Title
Mothers’ Vaccination Decision: The Relation Between Science Skepticism, Social Networks, Vaccination Beliefs, and Fear of ASD
Creator
Lockwood, Maria Izabel Kugelmas Guarita
Date
2021
Description
Vaccines are instrumental in stopping the spread of disease, yet some parents choose to not vaccinate their children. Despite scientific...
Show moreVaccines are instrumental in stopping the spread of disease, yet some parents choose to not vaccinate their children. Despite scientific evidence that childhood vaccines are safe, there is an increasing number of children in the United States and the United Kingdom who are not getting vaccinated. The current study investigates different factors that may be associated with mothers’ decision to vaccinate their children. This study examines the relations between skepticism in science, vaccination beliefs, fear of having a child with Autism Spectrum Disorder (ASD), social network recommendations, and maternal decision to vaccinate. Participants included 293 expectant mothers in the United States and the United Kingdom. Results indicated that mothers who are pro-vaccine and mothers who are vaccine-hesitant have different score profiles across scales that measure skepticism in science, vaccination beliefs, and fear of having a child with ASD. Specifically, we found that relative to mothers who are vaccine-hesitant, mothers who are pro-vaccine: (1) indicated less skepticism in science; (2) had fewer anti-vaccination beliefs; (3) did not statistically differ on their fear of having a child with ASD; and (4) had a smaller percentage of their social network advocating against vaccination. Thus, the study adds to the research literature, as it illustrates that mothers who are vaccine-hesitant differ from mothers who are pro-vaccine on key factors.
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