RESUMO
BACKGROUND: Emergency departments (ED) are becoming increasingly overwhelmed, increasing poor outcomes. Triage scores aim to optimize the waiting time and prioritize the resource usage. Artificial intelligence (AI) algorithms offer advantages for creating predictive clinical applications. OBJECTIVE: Evaluate a state-of-the-art machine learning model for predicting mortality at the triage level and, by validating this automatic tool, improve the categorization of patients in the ED. DESIGN: An institutional review board (IRB) approval was granted for this retrospective study. Information of consecutive adult patients (ages 18-100) admitted at the emergency department (ED) of one hospital were retrieved (January 1, 2012-December 31, 2018). Features included the following: demographics, admission date, arrival mode, referral code, chief complaint, previous ED visits, previous hospitalizations, comorbidities, home medications, vital signs, and Emergency Severity Index (ESI). The following outcomes were evaluated: early mortality (up to 2 days post ED registration) and short-term mortality (2-30 days post ED registration). A gradient boosting model was trained on data from years 2012-2017 and examined on data from the final year (2018). The area under the curve (AUC) for mortality prediction was used as an outcome metric. Single-variable analysis was conducted to develop a nine-point triage score for early mortality. KEY RESULTS: Overall, 799,522 ED visits were available for analysis. The early and short-term mortality rates were 0.6% and 2.5%, respectively. Models trained on the full set of features yielded an AUC of 0.962 for early mortality and 0.923 for short-term mortality. A model that utilized the nine features with the highest single-variable AUC scores (age, arrival mode, chief complaint, five primary vital signs, and ESI) yielded an AUC of 0.962 for early mortality. CONCLUSION: The gradient boosting model shows high predictive ability for screening patients at risk of early mortality utilizing data available at the time of triage in the ED.
Assuntos
Inteligência Artificial , Triagem , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Serviço Hospitalar de Emergência , Mortalidade Hospitalar , Humanos , Aprendizado de Máquina , Pessoa de Meia-Idade , Estudos Retrospectivos , Adulto JovemRESUMO
Incongruity between emotional experience and its outwardly expression is one of the prominent symptoms in schizophrenia. Though widely reported and used in clinical evaluation, this symptom is inadequately defined in the literature and may be confused with mere affect flattening. In this study we used structured-light depth camera and dedicated software to automatically measure facial activity of schizophrenia patients and healthy individuals during an emotionally evocative task. We defined novel measures for the congruence of emotional experience and emotional expression and for Flat Affect, compared them between patients and controls, and examined their consistency with clinical evaluation. We found incongruity in schizophrenia to be manifested in a less specific range of facial expressions in response to similar emotional stimuli, while the emotional experience remains intact. Our study also suggests that when taking into consideration affect flatness, no contextually inappropriate facial expressions are evident.