Patient journey through cases of depression from claims database using machine learning algorithms
| Autor: | Masakazu Fujiwara, Yoshitake Kitanishi, Bruce Binkowitz |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Computer science Physiology Disease computer.software_genre Medical care Inflammatory bowel disease Machine Learning Database and Informatics Methods 0302 clinical medicine Medicine and Health Sciences 030212 general & internal medicine Claims database Medical diagnosis Precision Medicine Depression (differential diagnoses) Data Management Multidisciplinary Depression Prognosis Gastritis Medicine Algorithms Research Article Computer and Information Sciences Drug Research and Development Science MEDLINE Gastroenterology and Hepatology Machine learning Research and Analysis Methods 03 medical and health sciences Artificial Intelligence Mental Health and Psychiatry medicine Humans Structure (mathematical logic) Pharmacology business.industry Mood Disorders Inflammatory Bowel Disease Biology and Life Sciences medicine.disease Precision medicine 030104 developmental biology Artificial intelligence business Physiological Processes Sleep computer |
| Zdroj: | PLoS ONE PLoS ONE, Vol 16, Iss 2, p e0247059 (2021) |
| ISSN: | 1932-6203 |
| Popis: | Health insurance and acute hospital-based claims have recently become available as real-world data after marketing in Japan and, thus, classification and prediction using the machine learning approach can be applied to them. However, the methodology used for the analysis of real-world data has been hitherto under debate and research on visualizing the patient journey is still inconclusive. So far, to classify diseases based on medical histories and patient demographic background and to predict the patient prognosis for each disease, the correlation structure of real-world data has been estimated by machine learning. Therefore, we applied association analysis to real-world data to consider a combination of disease events as the patient journey for depression diagnoses. However, association analysis makes it difficult to interpret multiple outcome measures simultaneously and comprehensively. To address this issue, we applied the Topological Data Analysis (TDA) Mapper to sequentially interpret multiple indices, thus obtaining a visual classification of the diseases commonly associated with depression. Under this approach, the visual and continuous classification of related diseases may contribute to precision medicine research and can help pharmaceutical companies provide appropriate personalized medical care. |
| Databáze: | OpenAIRE |
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