The Florida Department of Health (FDOH) previously monitored Florida Poison Information Center (FPICN) data for timely detection of increases in carbon monoxide (CO) exposures before, during, and after hurricanes. Recent analyses have noted that CO poisonings have also increased with generator use and improper heating of homes during cold winter months in Florida. Similarly, increases in CO poisoning cases related to motor vehicles have been observed during summer months. CO is an odorless, colorless, poisonous gas causing sudden illness and death, if present in sufficient concentration in ambient air. The most common signs and symptoms include headache, nausea, lethargy/fatigue, weakness, abdominal discomfort/pain, confusion, and dizziness. This presentation summarizes Florida’s experience in identifying CO poisoning clusters using ESSENCE-based syndromic surveillance.
Detection
Homelessness in general is a major issue in the US today. The risk factors of homelessness are myriad, including inadequate income, lack of affordable housing, mental health and substance abuse issues, lack of social support, and nonadherence to treatment/follow-up appointments. Early identification of these factors from clinical documents may help detect or even predict homelessness cases, allowing adequate intervention and prevention measures.
Objective
We demonstrate a semi-automated approach to induce and curate lexical domain knowledge for identification of evidence and risk factors for homelessness found in VA clinical documents. This domain knowledge can be used to support training and evaluation of automated methods such as Natural Language Processing (NLP) systems for detection and prediction of homelessness among veterans. This could serve as a proxy for public health and other surveillance involving homeless individuals. Similar methods could be used to identify other conditions of interest.