{"title":"1999年至2014年美国的死亡原因。","authors":"Hanyu Jiang, Hang Wu, May Dongmei Wang","doi":"10.1109/bhi.2017.7897234","DOIUrl":null,"url":null,"abstract":"<p><p>Statistical methods have been widely used in studies of public health. Although useful in clinical research and public health policy making, these methods could not find correlation among health conditions automatically, or capture the temporal evolution of causes of death correctly. To cope with two challenges above, we implement an unsupervised machine learning model, termed topic models, to investigate the mortality data of the United States. Our model successfully groups morbidities based on their correlation, and reveals the temporal evolution of these groups from 1999 to 2014, which are also validated by existing literature. This work could provide a novel view for clinical practitioners to provide more accurate healthcare service, and for public health policymakers to make better policy.</p>","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/bhi.2017.7897234","citationCount":"2","resultStr":"{\"title\":\"Causes of death in the United States, 1999 to 2014.\",\"authors\":\"Hanyu Jiang, Hang Wu, May Dongmei Wang\",\"doi\":\"10.1109/bhi.2017.7897234\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Statistical methods have been widely used in studies of public health. Although useful in clinical research and public health policy making, these methods could not find correlation among health conditions automatically, or capture the temporal evolution of causes of death correctly. To cope with two challenges above, we implement an unsupervised machine learning model, termed topic models, to investigate the mortality data of the United States. Our model successfully groups morbidities based on their correlation, and reveals the temporal evolution of these groups from 1999 to 2014, which are also validated by existing literature. This work could provide a novel view for clinical practitioners to provide more accurate healthcare service, and for public health policymakers to make better policy.</p>\",\"PeriodicalId\":72024,\"journal\":{\"name\":\"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1109/bhi.2017.7897234\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/bhi.2017.7897234\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2017/4/13 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/bhi.2017.7897234","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2017/4/13 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
Causes of death in the United States, 1999 to 2014.
Statistical methods have been widely used in studies of public health. Although useful in clinical research and public health policy making, these methods could not find correlation among health conditions automatically, or capture the temporal evolution of causes of death correctly. To cope with two challenges above, we implement an unsupervised machine learning model, termed topic models, to investigate the mortality data of the United States. Our model successfully groups morbidities based on their correlation, and reveals the temporal evolution of these groups from 1999 to 2014, which are also validated by existing literature. This work could provide a novel view for clinical practitioners to provide more accurate healthcare service, and for public health policymakers to make better policy.