Prediction of Poststroke Depression Based on the Outcomes of Machine Learning Algorithms.
Yeong Hwan RyuSeo Young KimTae Uk KimSeong Jae LeeSoo Jun ParkHo-Youl JungJung Keun HyunPublished in: Journal of clinical medicine (2022)
Poststroke depression (PSD) is a major psychiatric disorder that develops after stroke; however, whether PSD treatment improves cognitive and functional impairments is not clearly understood. We reviewed data from 31 subjects with PSD and 34 age-matched controls without PSD; all subjects underwent neurological, cognitive, and functional assessments, including the National Institutes of Health Stroke Scale (NIHSS), the Korean version of the Mini-Mental Status Examination (K-MMSE), computerized neurocognitive test (CNT), the Korean version of the Modified Barthel Index (K-MBI), and functional independence measure (FIM) at admission to the rehabilitation unit in the subacute stage following stroke and 4 weeks after initial assessments. Machine learning methods, such as support vector machine, k-nearest neighbors, random forest, voting ensemble models, and statistical analysis using logistic regression were performed. PSD was successfully predicted using a support vector machine with a radial basis function kernel function (area under curve (AUC) = 0.711, accuracy = 0.700). PSD prognoses could be predicted using a support vector machine linear algorithm (AUC = 0.830, accuracy = 0.771). The statistical method did not have a better AUC than that of machine learning algorithms. We concluded that the occurrence and prognosis of PSD in stroke patients can be predicted effectively based on patients' cognitive and functional statuses using machine learning algorithms.
Keyphrases
- machine learning
- deep learning
- big data
- artificial intelligence
- mental health
- atrial fibrillation
- end stage renal disease
- emergency department
- public health
- ejection fraction
- chronic kidney disease
- risk assessment
- type diabetes
- cerebral ischemia
- convolutional neural network
- prognostic factors
- sleep quality
- upper limb
- health information
- electronic health record
- bipolar disorder
- psychometric properties
- blood brain barrier
- insulin resistance
- patient reported
- combination therapy
- preterm birth
- subarachnoid hemorrhage