Predicting falls based on a 2-minute walk test

Falls are the leading cause of injuries in older adults. Identifying older adults with risk for falls prior to discharge home from the Emergency Department (ED) could help direct fall prevention interventions, yet ED-based tools to assist risk stratification are under-developed. The aim of this study was to compare the performance of our proposed machine learning algorithms with existing screening tools to predict future falls in the 90-days post ED discharge for 150 older adults aged 65 years and older.

Faculty Supervisor:

Ervin Sejdic

Student:

Partner:

VHA Home Healthcare

Discipline:

Engineering

Sector:

Health and Related Sciences & Technology

University:

University of Toronto

Program:

Accelerate

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