Prediction of Poststroke Depression Based on the Outcomes of Machine Learning Algorithms

Yeong Hwan Ryu, Seo‐Young Kim, Tae Uk Kim, Seong Jae Lee, Soo Jun Park, Ho-Youl Jung, Jung Keun Hyun

Journal of Clinical Medicine 2022 article cited by 24

Source Open-access PDF More in Medicine & Health

importance 3/10 ai assisted Stroke Rehabilitation and Recovery stroke rehabilitation stage: research

Problem

Poststroke depression is a major psychiatric complication after stroke, and predicting which patients will develop it is difficult.

Why it matters

Early identification of poststroke depression could enable timely treatment and better recovery.

What the AI did

Machine learning models (support vector machine, k-nearest neighbors, random forest, voting ensemble) were trained on neurological, cognitive, and functional assessments to predict poststroke depression.

Results

PSD 'successfully predicted' using a support vector machine; no concrete metrics stated in source

Impact

Evidence supports ML-based PSD prediction, but only on a very small cohort of 65 subjects.

Record

Venue	Journal of Clinical Medicine
Published	2022
Work type	article
Authors	Yeong Hwan Ryu, Seo‐Young Kim, Tae Uk Kim, Seong Jae Lee, Soo Jun Park, Ho-Youl Jung, Jung Keun Hyun
Citations	24 (OpenAlex, at import time)
References	63
Model or method	support vector machine (best performer); also kNN, random forest, voting ensemble
Organization	not stated in source
Primary source	https://doi.org/10.3390/jcm11082264
Open access	gold
Keywords	machine learning, poststroke depression, support vector machine, prediction, rehabilitation

Importance rating

3 / 10

incremental / niche

Poststroke depression prediction could enable timely treatment, but evidence is a very small 65-subject cohort; incremental advance.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: 65 subjects (31 with PSD, 34 controls). Method and limits.

More in Medicine & Health

#	Paper	Year	Citations	Importance
1	Multimodal deep learning of fundus abnormalities and traditional risk factors for cardiovascular risk prediction Yeong Chan Lee, Jiho Cha, Injeong Shim, Woong‐Yang Park, Se Woong Kang...	2023	107	10
2	Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization Tawsifur Rahman, Amith Abdullah Khandakar, Muhammad Abdul Kadir, Khand...	2020	651	10
3	A comparative analysis of machine learning classifiers for stroke prediction: A predictive analytics approach Nitish Biswas, Khandaker Mohammad Mohi Uddin, Sarreha Tasmin Rikta, Sa...	2022	116	10
4	Cardiovascular Disease Diagnosis from DXA Scan and Retinal Images Using Deep Learning Hamada R. H. Al-Absi, Mohammad Tariqul Islam, Mahmoud Ahmed Refaee, Mu...	2022	89	10
5	Proteomics and Machine Learning Approaches Reveal a Set of Prognostic Markers for COVID-19 Severity With Drug Repurposing Potential Kruthi Suvarna, Deeptarup Biswas, Medha Gayathri J. Pai, Arup Acharjee...	2021	76	10