Understanding and Predicting Cognitive Improvement of Young Adults in Ischemic Stroke Rehabilitation Therapy
Martinez, Helard Becerra (University of College Dublin)
Cisek, Katryna (Technological University Dublin)
Garcia-Rudolph, Alejandro (Institut Germans Trias i Pujol. Fundació Lluita Contra les Infeccions)
Kelleher, John D. (Technological University Dublin)
Hines, Andrew (University of College Dublin)
Universitat Autònoma de Barcelona

Data:	2022
Resum:	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). Twenty different classification algorithms (from Python's Scikit-learn library) are trained and evaluated, varying their hyper-parameters and the number of features received as input. Best-performing models reported Recall scores around 0. 7 and F1 scores of 0. 6, showing the model's ability to identify patients with poor cognitive improvement. The study includes a detailed feature importance report that helps interpret the model's inner decision workings and exposes the most influential factors in the cognitive improvement prediction. The study showed that certain therapy variables (e. g. , the proportion of memory and orientation executed tasks) had an important influence on the final prediction of the cognitive improvement of patients at individual and population levels. This type of evidence can serve clinicians in adjusting the therapeutic settings (e. g. , type and load of therapy activities) and selecting the one that maximizes cognitive improvement.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	AI explainability ; Cognitive improvement ; Cognitive therapy ; Ischemic stroke ; Machine learning (ML) ; Predictive models ; Web-based therapy
Publicat a:	Frontiers in neurology, Vol. 13 (july 2022) , ISSN 1664-2295

DOI: 10.3389/fneur.2022.886477
PMID: 35911882

22 p, 4.1 MB

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Documents de recerca > Documents dels grups de recerca de la UAB > Centres i grups de recerca (producció científica) > Ciències de la salut i biociències > Institut d'Investigació en Ciencies de la Salut Germans Trias i Pujol (IGTP)
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