Cargando…
Patient journey through cases of depression from claims database using machine learning algorithms
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...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7886120/ https://www.ncbi.nlm.nih.gov/pubmed/33592062 http://dx.doi.org/10.1371/journal.pone.0247059 |
_version_ | 1783651731627835392 |
---|---|
author | Kitanishi, Yoshitake Fujiwara, Masakazu Binkowitz, Bruce |
author_facet | Kitanishi, Yoshitake Fujiwara, Masakazu Binkowitz, Bruce |
author_sort | Kitanishi, Yoshitake |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7886120 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-78861202021-02-23 Patient journey through cases of depression from claims database using machine learning algorithms Kitanishi, Yoshitake Fujiwara, Masakazu Binkowitz, Bruce PLoS One Research Article 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. Public Library of Science 2021-02-16 /pmc/articles/PMC7886120/ /pubmed/33592062 http://dx.doi.org/10.1371/journal.pone.0247059 Text en © 2021 Kitanishi et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Kitanishi, Yoshitake Fujiwara, Masakazu Binkowitz, Bruce Patient journey through cases of depression from claims database using machine learning algorithms |
title | Patient journey through cases of depression from claims database using machine learning algorithms |
title_full | Patient journey through cases of depression from claims database using machine learning algorithms |
title_fullStr | Patient journey through cases of depression from claims database using machine learning algorithms |
title_full_unstemmed | Patient journey through cases of depression from claims database using machine learning algorithms |
title_short | Patient journey through cases of depression from claims database using machine learning algorithms |
title_sort | patient journey through cases of depression from claims database using machine learning algorithms |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7886120/ https://www.ncbi.nlm.nih.gov/pubmed/33592062 http://dx.doi.org/10.1371/journal.pone.0247059 |
work_keys_str_mv | AT kitanishiyoshitake patientjourneythroughcasesofdepressionfromclaimsdatabaseusingmachinelearningalgorithms AT fujiwaramasakazu patientjourneythroughcasesofdepressionfromclaimsdatabaseusingmachinelearningalgorithms AT binkowitzbruce patientjourneythroughcasesofdepressionfromclaimsdatabaseusingmachinelearningalgorithms |