Joe Walsh

Predictive Modeling for Public Health: Preventing Childhood Lead Poisoning

Lead poisoning is a major public health problem that affects hundreds of thousands of children in the United States every year. A common approach to identifying lead hazards is to test all children for elevated blood lead levels and then investigate and remediate the homes of children with elevated tests. This can prevent exposure to lead of future residents, but only after a child has been poisoned. This paper describes joint work with the Chicago Department of Public Health (CDPH) in which we build a model that predicts the risk of a child to being poisoned so that an intervention can take place before that happens. Using two decades of blood lead level tests, home lead inspections, property value assessments, and census data, our model allows inspectors to prioritize houses on an intractably long list of potential hazards and identify children who are at the highest risk. This work has been described by CDPH as pioneering in the use of machine learning and predictive analytics in public health and has the potential to have a significant impact on both health and economic outcomes for communities across the US.

Early Intervention Systems: Predicting Adverse Interactions Between Police and the Public:

Adverse interactions between police and the public hurt police legitimacy, cause harm to both officers and the public, and result in costly litigation. Early intervention systems (EISs) that flag officers considered most likely to be involved in one of these adverse events are an important tool for police supervision and for targeting interventions such as counseling or training. However, the EISs that exist are not data-driven and based on supervisor intuition. We have developed a data-driven EIS that uses a diverse set of data sources from the Charlotte-Mecklenburg Police Department and machine learning techniques to more accurately predict the officers who will have an adverse event. Our approach is able to significantly improve accuracy compared with their existing EIS: Preliminary results indicate a 20% reduction in false positives and a 75% increase in true positives.

The Legislative Influence Detector: Finding Text Reuse in State Legislation

State legislatures introduce at least 45,000 bills each year. However, we lack a clear understanding of who is actually writing those bills. As legislators often lack the time and staff to draft each bill, they frequently copy text written by other states or interest groups. However, existing approaches to detect text reuse are slow, biased, and incomplete. Journalists or researchers who want to know where a particular bill originated must perform a largely manual search. Watchdog organizations even hire armies of volunteers to monitor legislation for matches. Given the time-consuming nature of the analysis, journalists and researchers tend to limit their analysis to a subset of topics (e.g. abortion or gun control) or a few interest groups. This paper presents the Legislative Influence Detector (LID). LID uses the Smith-Waterman local alignment algorithm to detect sequences of text that occur in model legislation and state bills. As it is computationally too expensive to run this algorithm on a large corpus of data, we use a search engine built using Elasticsearch to limit the number of comparisons. We show how system has found 45,405 instances of bill-to-bill text reuse and 14,137 instances of model-legislation-to-bill text reuse. System reduces the time it takes to manually find text reuse from days to seconds.

Identifying Police Officers at Risk of Adverse Events

Adverse events between police and the public, such as deadly shootings or instances of racial profiling, can cause serious or deadly harm, damage police legitimacy, and result in costly litigation. Evidence suggests these events can be prevented by targeting interventions based on an Early Intervention System (EIS) that flags police officers who are at a high risk for involvement in such adverse events. Todays EIS are not data-driven and typically rely on simple thresholds based entirely on expert intuition. In this paper, we describe our work with the Charlotte-Mecklenburg Police Department (CMPD) to develop a machine learning model to predict which officers are at risk for an adverse event. Our approach significantly outperforms CMPDs existing EIS, increasing true positives by ~12% and decreasing false positives by ~32%. Our work also sheds light on features related to officer characteristics, situational factors, and neighborhood factors that are predictive of adverse events. This work provides a starting point for police departments to take a comprehensive, data-driven approach to improve policing and reduce harm to both officers and members of the public.

Deploying Machine Learning Models for Public Policy: A Framework

Machine learning research typically focuses on optimization and testing on a few criteria, but deployment in a public policy setting requires more. Technical and non-technical deployment issues get relatively little attention. However, for machine learning models to have real-world benefit and impact, effective deployment is crucial. In this case study, we describe our implementation of a machine learning early intervention system (EIS) for police officers in the Charlotte-Mecklenburg (North Carolina) and Metropolitan Nashville (Tennessee) Police Departments. The EIS identifies officers at high risk of having an adverse incident, such as an unjustified use of force or sustained complaint. We deployed the same code base at both departments, which have different underlying data sources and data structures. Deployment required us to solve several new problems, covering technical implementation, governance of the system, the cost to use the system, and trust in the system. In this paper we describe how we addressed and solved several of these challenges and provide guidance and a framework of important issues to consider for future deployments.

Reducing Incarceration through Prioritized Interventions

The most vulnerable individuals in society often struggle with long-lasting, multi-faceted challenges like mental illness, substance abuse, chronic health conditions, and homelessness. Individuals experiencing these difficulties tend to interact with public services and departments frequently, but many communities are struggling to identify those individuals, let alone meet their needs in meaningful and cost-effective ways. In this paper, we describe our work with Johnson County, Kansas, that uses machine learning to prioritize outreach to individuals most at risk of being booked into jail within the next year. For the first time, we brought together Johnson Countys jail, emergency medical, and mental health data, identified individuals who touch multiple systems, and built a model to predict individual jail bookings. Our system significantly outperformed both a random baseline and several simple heuristics that domain experts are likely to use and implement. By focusing on 200 individuals (which is the intervention capacity of Johnson County) who had interacted with both mental health services and the criminal justice system, we predicted jail bookings in the following year with 51% precision, which outperforms a baseline heuristic model by 1.5 times, and is 4.6 times better than a random baseline. This work provides a framework and prototype system for Johnson County as well as many other jurisdictions that are part of the Data Driven Justice Initiative as they develop intervention models to proactively connect social and mental health workers with individuals in need of care to avoid incarceration.

Identifying Earmarks in Congressional Bills

Earmarks are legislative provisions that direct federal funds to specific projects, circumventing the competitive grant-making process of federal agencies. Identifying and cataloging earmarks is a tedious, time-consuming process carried out by experts from public interest groups. In this paper, we present a machine learning system for automatically extracting earmarks from congressional bills and reports. We first describe a table-parsing algorithm for extracting budget allocations from appropriations tables in congressional bills. We then use machine learning classifiers to identify budget allocations as earmarked objects with an out of sample ROC AUC score of 0.89. Using this system, we construct the first publicly available database of earmarks dating back to 1995. Our machine learning approach adds transparency, accuracy, and speed to the congressional appropriations process.

Rural transport health and safety in sub-Saharan Africa: Online survey snapshot of expert opinion

ABSTRACT Rural transportation in sub-Saharan Africa is a complicated and often contradictory endeavour. This article presents the results of an Internet-based survey deployed to elicit expert input on rural transport health and safety issues. The survey was specifically aimed at capturing priorities and opinions with regard to potential research needs. A total of 65 responses to the survey were received from transport and public health professionals from 25 countries across five continents. Descriptive analysis of the responses revealed varying concerns and priorities across different issues reflecting underlying themes of poverty and gender. Cluster analyses showed the complexities of interrelationships among issues. The results can form the basis for future studies and discussions needed to continue addressing the myriad transport-related issues impeding development in sub-Saharan Africa.