WHOMEVER APPROVED THIS NEEDS TO BE FIRED! Predicting failure to recover is useless! Do the goddamned research that delivers recovery protocols! They'll want those recovery protocols
when they are the 1 in 4 per WHO that has a stroke!
Understanding and Predicting Cognitive Improvement of Young Adults in Ischemic Stroke Rehabilitation Therapy
Helard Becerra Martinez 1*, Katryna Cisek 2, Alejandro García-Rudolph 3,4,5,
John D. Kelleher 2 and Andrew Hines 1
1 School of Computer Science, University of College Dublin, Dublin, Ireland, 2 Information, Communication and Entertainment
Research Institute, Technological University Dublin, Dublin, Ireland, 3 Institut Guttmann Hospital de Neurorehabilitacio,
Badalona, Spain, 4 Universitat Autónoma de Barcelona, Cerdanyola del Vallés, Spain, 5 Fundació Institut d’Investigació en
Ciéncies de la Salut Germans Trias i Pujol, Badalona, Spain
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.
John D. Kelleher 2 and Andrew Hines 1
1 School of Computer Science, University of College Dublin, Dublin, Ireland, 2 Information, Communication and Entertainment
Research Institute, Technological University Dublin, Dublin, Ireland, 3 Institut Guttmann Hospital de Neurorehabilitacio,
Badalona, Spain, 4 Universitat Autónoma de Barcelona, Cerdanyola del Vallés, Spain, 5 Fundació Institut d’Investigació en
Ciéncies de la Salut Germans Trias i Pujol, Badalona, Spain
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.
No comments:
Post a Comment