The Use of Deep Learning to Predict Stroke Patient Mortality
Bibliographic Data
| ID | 15495449 |
|---|---|
| Authors | Song-Hee Cheon (0000-0003-1414-5430, Youngsan University), Songhee Cheon (Youngsan University), Jungyoon Kim (0000-0002-2396-9514, Kent State University), Jihye Lim (0000-0003-3819-5192, Youngsan University, corresponding author) |
| Year | 2019 |
| Volume | 16 |
| Issue | 11 |
| Pages | 1876-1876 |
| Publication date | 2019-05-28 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph16111876 |
| PMID | 31141892 |
| OpenAlex | W2946916415 |
| Language | EN |
| Citations received | 3 |
| References cited | 44 |
The increase in stroke incidence with the aging of the Korean population will rapidly impose an economic burden on society. Timely treatment can improve stroke prognosis. Awareness of stroke warning signs and appropriate actions in the event of a stroke improve outcomes. Medical service use and health behavior data are easier to collect than medical imaging data. Here, we used a deep neural network to detect stroke using medical service use and health behavior data; we identified 15,099 patients with stroke. Principal component analysis (PCA) featuring quantile scaling was used to extract relevant background features from medical records; we used these to predict stroke. We compared our method (a scaled PCA/deep neural network [DNN] approach) to five other machine-learning methods. The area under the curve (AUC) value of our method was 83.48%; hence; it can be used by both patients and doctors to prescreen for possible stroke
Artificial neural network · Deep learning · Environmental health · Incidence (geometry · Machine learning · Physical medicine and rehabilitation · Population · Stroke (engine · Acute Ischemic Stroke Management · Artificial Intelligence in Healthcare · Computer Science · Medicine · Traditional Chinese Medicine Studies · Artificial Intelligence · Emergency Medicine
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| Citations per year | 0.6 |
|---|---|
| Citation span | 2021 - 2023 (3) |
| Highly cited | No |