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.

No abstract

No abstract

This prognostic study validates a machine learning (random forest) prediction model of elevated blood lead levels by comparing with a parsimonious logistic regression among children in a Women, Infants, and Children cohort.

consistent medical care among people living with HiV is essential for both individual and public health. HIV-positive individuals who are 'retained in care' are more likely to be prescribed antiretroviral medication and achieve HIV viral suppression, effectively eliminating the risk of transmitting HIV to others. However, in the United States, less than half of HIV-positive individuals are retained in care. Interventions to improve retention in care are resource intensive, and there is currently no systematic way to identify patients at risk for falling out of care who would benefit from these interventions. We developed a machine learning model to identify patients at risk for dropping out of care in an urban HIV care clinic using electronic medical records and geospatial data. The machine learning model has a mean positive predictive value of 34.6% [SD: 0.15] for flagging the top 10% highest risk patients as needing interventions, performing better than the previous state-of-the-art logistic regression model (PPV of 17% [SD: 0.06]) and the baseline rate of 11.1% [SD: 0.02]. Machine learning methods can improve the prediction ability in HIV care clinics to proactively identify patients at risk for not returning to medical care. Consistent medical care is essential for the health of people living with HIV. HIV-positive individuals who receive regular medical care are more likely to receive antiretroviral therapy, less likely to develop Acquired Immune Deficiency Syndrome (AIDS), and have improved survival rates compared to HIV-positive individuals who do not receive regular medical care 1-3. In the field of HIV medicine, patients who receive regular medical care are considered 'retained in care. ' Retention in care is not only important for the individual health of people living with HIV, but also for public health. HIV-positive individuals who are retained in care and taking antiretroviral therapy are able to suppress the HIV viral level in their serum to undetectable levels, effectively eliminating the risk of transmitting HIV to others. Accordingly, retention in care is a critical pillar of public health agency plans to eliminate HIV transmission in the United States 4-6. Despite the clear benefits of retention in care for individual and public health, less than half of individuals living with HIV in the U.S. are retained in care. Lack of access to healthcare is one reason that patients may not be retained in care 7. However, for patients who lack health insurance, state and federal programs such as the Ryan White HIV/AIDS Program provide funding to pay for HIV care visits and antiretroviral medications. Despite these programs, many patients living with HIV still do not regularly attend medical appointments. Additional barriers to retention in care remain, including mental illness, substance use, insecure housing, poverty, neighborhood violence, and stigma 8-16. Interventions that are effective for improving retention in care include intensive case management, peer navigation, and multi-faceted outreach progra...