This application simplifies the evaluation of enteral nutrition necessity in acute stroke patients for healthcare professionals. Users input Motor Functional Independence Measure (FIM_m), Cognitive Functional Independence Measure (FIM_c), and Speech Intelligibility (SI) values, triggering a logistic regression model to swiftly predict nutritional requirements. Clinicians benefit from rapid, data-driven insights, aiding in timely interventions and personalized care plans tailored to individual patient needs. This tool optimizes decision-making processes, enhancing patient care outcomes in stroke management.

See owner's GitHub repository for more information: https://github.com/kokamoto46/speech_audiology_prediction

Okamoto, K., Irie, K., Hoyano, K., & Matsushita, I. (2024). Machine Learning in Acute Stroke Care: A Novel Model for Assessing the Need for Enteral Nutrition. medRxiv, 2024.03.11.24304069. https://doi.org/10.1101/2024.03.11.24304069

This application was not uploaded by the author, but through their publicly available Github repository, https://github.com/kokamoto46/speech_audiology_prediction.