Automated Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Deep Learning Methods and Sequential Data
Depression and anxiety are common comorbidities of stroke. Research has shown that about 30% of stroke survivors develop depression and about 20% develop anxiety. Stroke survivors with such adverse mental outcomes are often attributed to poorer health outcomes, such as higher mortality rates. The ob...
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MDPI AG
2025-05-01
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| author | Chien Wei Oei Eddie Yin Kwee Ng Matthew Hok Shan Ng Yam Meng Chan Vinithasree Subbhuraam Lai Gwen Chan U. Rajendra Acharya |
| author_facet | Chien Wei Oei Eddie Yin Kwee Ng Matthew Hok Shan Ng Yam Meng Chan Vinithasree Subbhuraam Lai Gwen Chan U. Rajendra Acharya |
| author_sort | Chien Wei Oei |
| collection | DOAJ |
| description | Depression and anxiety are common comorbidities of stroke. Research has shown that about 30% of stroke survivors develop depression and about 20% develop anxiety. Stroke survivors with such adverse mental outcomes are often attributed to poorer health outcomes, such as higher mortality rates. The objective of this study is to use deep learning (DL) methods to predict the risk of a stroke survivor experiencing post-stroke depression and/or post-stroke anxiety, which is collectively known as post-stroke adverse mental outcomes (PSAMO). This study studied 179 patients with stroke, who were further classified into PSAMO versus no PSAMO group based on the results of validated depression and anxiety questionnaires, which are the industry’s gold standard. This study collected demographic and sociological data, quality of life scores, stroke-related information, medical and medication history, and comorbidities. In addition, sequential data such as daily lab results taken seven consecutive days after admission are also collected. The combination of using DL algorithms, such as multi-layer perceptron (MLP) and long short-term memory (LSTM), which can process complex patterns in the data, and the inclusion of new data types, such as sequential data, helped to improve model performance. Accurate prediction of PSAMO helps clinicians make early intervention care plans and potentially reduce the incidence of PSAMO. |
| format | Article |
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| institution | OA Journals |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Bioengineering |
| spelling | doaj-art-c40240dad8ce4c0a982af844e331ad3b2025-08-20T01:56:25ZengMDPI AGBioengineering2306-53542025-05-0112551710.3390/bioengineering12050517Automated Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Deep Learning Methods and Sequential DataChien Wei Oei0Eddie Yin Kwee Ng1Matthew Hok Shan Ng2Yam Meng Chan3Vinithasree Subbhuraam4Lai Gwen Chan5U. Rajendra Acharya6Management Information Department, Office of Clinical Epidemiology, Analytics and kNowledge (OCEAN), Tan Tock Seng Hospital, Singapore 308433, SingaporeSchool of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, SingaporeRehabilitation Research Institute of Singapore, Nanyang Technological University, Singapore 308232, SingaporeDepartment of General Surgery, Vascular Surgery Service, Tan Tock Seng Hospital, Singapore 308433, SingaporeThe Digital Health Hub, Austin, TX 78944, USADepartment of Psychiatry, Tan Tock Seng Hospital, Singapore 308433, SingaporeSchool of Mathematics, Physics and Computing, University of Southern Queensland, Brisbane, QLD 4305, AustraliaDepression and anxiety are common comorbidities of stroke. Research has shown that about 30% of stroke survivors develop depression and about 20% develop anxiety. Stroke survivors with such adverse mental outcomes are often attributed to poorer health outcomes, such as higher mortality rates. The objective of this study is to use deep learning (DL) methods to predict the risk of a stroke survivor experiencing post-stroke depression and/or post-stroke anxiety, which is collectively known as post-stroke adverse mental outcomes (PSAMO). This study studied 179 patients with stroke, who were further classified into PSAMO versus no PSAMO group based on the results of validated depression and anxiety questionnaires, which are the industry’s gold standard. This study collected demographic and sociological data, quality of life scores, stroke-related information, medical and medication history, and comorbidities. In addition, sequential data such as daily lab results taken seven consecutive days after admission are also collected. The combination of using DL algorithms, such as multi-layer perceptron (MLP) and long short-term memory (LSTM), which can process complex patterns in the data, and the inclusion of new data types, such as sequential data, helped to improve model performance. Accurate prediction of PSAMO helps clinicians make early intervention care plans and potentially reduce the incidence of PSAMO.https://www.mdpi.com/2306-5354/12/5/517artificial intelligencedeep learningmachine learningneural networkpost-stroke anxietypost-stroke depression |
| spellingShingle | Chien Wei Oei Eddie Yin Kwee Ng Matthew Hok Shan Ng Yam Meng Chan Vinithasree Subbhuraam Lai Gwen Chan U. Rajendra Acharya Automated Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Deep Learning Methods and Sequential Data Bioengineering artificial intelligence deep learning machine learning neural network post-stroke anxiety post-stroke depression |
| title | Automated Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Deep Learning Methods and Sequential Data |
| title_full | Automated Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Deep Learning Methods and Sequential Data |
| title_fullStr | Automated Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Deep Learning Methods and Sequential Data |
| title_full_unstemmed | Automated Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Deep Learning Methods and Sequential Data |
| title_short | Automated Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Deep Learning Methods and Sequential Data |
| title_sort | automated risk prediction of post stroke adverse mental outcomes using deep learning methods and sequential data |
| topic | artificial intelligence deep learning machine learning neural network post-stroke anxiety post-stroke depression |
| url | https://www.mdpi.com/2306-5354/12/5/517 |
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