Status
Ready
Created On
Updated On
A model to predict changes in the necessity of enteral nutrition for patients with acute stroke. Based on paper: Machine Learning in Acute Stroke Care: A Novel Model for Assessing the Need for Enteral Nutrition

More details

Use Cases Limitations Evidence Owner's Insight

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.

Prototype

Warning: Not intended for clinical use. Assume outputs are unsafe and unvalidated. Use carefully.


  • Favorites: 0
  • Executions: 56

  • Nutrition & Dietary Health

Owner

C Community Discovery

Member since