Feasibility of Real-Time AI Models For Clinical Tasks Involving Tetanus: Study Protocol

Résumé fourni par la source

Early prediction of disease progression in tetanus enables timely intervention which can improve outcome. We have developed a machine learning model to predict transition to severe tetanus. The model uses continuous pulse plethysmography waveforms recorded from low-cost wearable pulse oximeters. Data from these monitors is read by the machine learning model in evaluating change over time. If a pre-specified threshold is reached, an alert is generated. To evaluate the feasibility of a clinical decision support system that incorporates our model, we describe a prospective study in adults with tetanus admitted to the intensive care unit in a tertiary hospital in Vietnam. The study aims to evaluate the frequency and accuracy of the alerts and potential clinical usefulness. For the purposes of this study, our machine-learning model runs in parallel with clinical care, and alerts are only seen by the study team who evaluate the patient status at the time of the alert.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.