Predicting state level suicide fatalities in the united states with realtime data and machine learning.

Digital trace data and machine learning techniques are increasingly being adopted to predict suicide-related outcomes at the individual level; however, there is also considerable public health need for timely data about suicide trends at the population level. Although significant geographic variation in suicide rates exist by state within the United States, national systems for reporting state suicide trends typically lag by one or more years. We developed and validated a deep learning based approach to utilize real-time, state-level online (Mental Health America web-based depression screenings; Google and YouTube Search Trends), social media (Twitter), and health administrative data (National Syndromic Surveillance Program emergency department visits) to estimate weekly suicide counts in four participating states. Specifically, per state, we built a long short-term memory (LSTM) neural network model to combine signals from the real-time data sources and compared predicted values of suicide deaths from our model to observed values in the same state. Our LSTM model produced accurate estimates of state-specific suicide rates in all four states (percentage error in suicide rate of -2.768% for Utah, -2.823% for Louisiana, -3.449% for New York, and -5.323% for Colorado). Furthermore, our deep learning based approach outperformed current gold-standard baseline autoregressive models that use historical death data alone. We demonstrate an approach to incorporate signals from multiple proxy real-time data sources that can potentially provide more timely estimates of suicide trends at the state level. Timely suicide data at the state level has the potential to improve suicide prevention planning and response tailored to the needs of specific geographic communities.

Respiratory syncytial virus-associated hospitalizations in Louisiana.

Respiratory syncytial virus (RSV) is the most common cause of bronchiolitis and pneumonia in children under one year of age worldwide. Records indicative of RSV cases were pulled from The Louisiana Inpatient Hospital Discharge Data based on RSV diagnosis codes to describe the burden of RSV infections in Louisiana from 1999 to 2010. Two thousand to three thousand hospitalized RSV cases occurred each year, with rates ranging from 37.2 to 71.4 hospitalizations per 100,000 population and the majority of cases (79%) being diagnosed with bronchiolitis. The vast majority of cases occurred in children under one year of age, and within that group, 44% of the cases occurred in children ages 0 to 3 months. The RSV season was found to occur from November to March, and immunoprophylaxis for high-risk infants should be given according to that season. Hospital-acquired versus community-acquired infections were also examined and most (96.1%) cases were community-acquired.