1.2 COMPUTER AND INFORMATION SCIENCE, 3. MEDICAL AND HEALTH SCIENCES
Accurate early predictions of a patient's likely cognitive improvement as a result of a stroke rehabilitation programme can assist clinicians in assembling more effective therapeutic programs. In addition, sufficient levels of explainability, which can justify these predictions, are a crucial requirement, as reported by clinicians. This article presents a machine learning (ML) prediction model targeting cognitive improvement after therapy for stroke surviving patients. The prediction model relies on electronic health records from 201 ischemic stroke surviving patients containing demographic information, cognitive assessments at admission from 24 different standardized neuropsychology tests (e.g., TMT, WAIS-III, Stroop, RAVLT, etc.), and therapy information collected during rehabilitation (72,002 entries collected between March 2007 and September 2019). The study population covered young-adult patients with a mean age of 49.51 years and only 4.47% above 65 years of age at the stroke event (no age filter applied).
Becerra Martinez, Helard; Cisek, Katryna; Garcia-Rudolph, Alejandro; Kelleher, John; and Hines, Andrew, "Understanding and Predicting Cognitive Improvement of Young Adults in Ischemic Stroke Rehabilitation Therapy" (2023). Articles. 192.
This research was partially funded by PRECISE4Q Personalized Medicine by Predictive Modelling in Stroke for better Quality of Life-European Union's Horizon 2020 research and innovation program under grant agreement No. 777107, and by the Science Foundation Ireland (SFI) under Grant Number 17/RC/2289_P2.
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