{"title":"新型冠状病毒肺炎患者自愿调查与精准医疗智能健康系统。","authors":"Zeeshan Ahmed","doi":"10.2217/pme-2021-0068","DOIUrl":null,"url":null,"abstract":"<p><p>Advancing frontiers of clinical research, we discuss the need for intelligent health systems to support a deeper investigation of COVID-19. We hypothesize that the convergence of the healthcare data and staggering developments in artificial intelligence have the potential to elevate the recovery process with diagnostic and predictive analysis to identify major causes of mortality, modifiable risk factors and actionable information that supports the early detection and prevention of COVID-19. However, current constraints include the recruitment of COVID-19 patients for research; translational integration of electronic health records and diversified public datasets; and the development of artificial intelligence systems for data-intensive computational modeling to assist clinical decision making. We propose a novel nexus of machine learning algorithms to examine COVID-19 data granularity from population studies to subgroups stratification and ensure best modeling strategies within the data continuum.</p>","PeriodicalId":19753,"journal":{"name":"Personalized medicine","volume":"18 6","pages":"573-582"},"PeriodicalIF":1.7000,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8544483/pdf/","citationCount":"3","resultStr":"{\"title\":\"Intelligent health system for the investigation of consenting COVID-19 patients and precision medicine.\",\"authors\":\"Zeeshan Ahmed\",\"doi\":\"10.2217/pme-2021-0068\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Advancing frontiers of clinical research, we discuss the need for intelligent health systems to support a deeper investigation of COVID-19. We hypothesize that the convergence of the healthcare data and staggering developments in artificial intelligence have the potential to elevate the recovery process with diagnostic and predictive analysis to identify major causes of mortality, modifiable risk factors and actionable information that supports the early detection and prevention of COVID-19. However, current constraints include the recruitment of COVID-19 patients for research; translational integration of electronic health records and diversified public datasets; and the development of artificial intelligence systems for data-intensive computational modeling to assist clinical decision making. We propose a novel nexus of machine learning algorithms to examine COVID-19 data granularity from population studies to subgroups stratification and ensure best modeling strategies within the data continuum.</p>\",\"PeriodicalId\":19753,\"journal\":{\"name\":\"Personalized medicine\",\"volume\":\"18 6\",\"pages\":\"573-582\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2021-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8544483/pdf/\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Personalized medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2217/pme-2021-0068\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2021/10/8 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"PHARMACOLOGY & PHARMACY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Personalized medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2217/pme-2021-0068","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/10/8 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
Intelligent health system for the investigation of consenting COVID-19 patients and precision medicine.
Advancing frontiers of clinical research, we discuss the need for intelligent health systems to support a deeper investigation of COVID-19. We hypothesize that the convergence of the healthcare data and staggering developments in artificial intelligence have the potential to elevate the recovery process with diagnostic and predictive analysis to identify major causes of mortality, modifiable risk factors and actionable information that supports the early detection and prevention of COVID-19. However, current constraints include the recruitment of COVID-19 patients for research; translational integration of electronic health records and diversified public datasets; and the development of artificial intelligence systems for data-intensive computational modeling to assist clinical decision making. We propose a novel nexus of machine learning algorithms to examine COVID-19 data granularity from population studies to subgroups stratification and ensure best modeling strategies within the data continuum.
期刊介绍:
Personalized Medicine (ISSN 1741-0541) translates recent genomic, genetic and proteomic advances into the clinical context. The journal provides an integrated forum for all players involved - academic and clinical researchers, pharmaceutical companies, regulatory authorities, healthcare management organizations, patient organizations and others in the healthcare community. Personalized Medicine assists these parties to shape thefuture of medicine by providing a platform for expert commentary and analysis.
The journal addresses scientific, commercial and policy issues in the field of precision medicine and includes news and views, current awareness regarding new biomarkers, concise commentary and analysis, reports from the conference circuit and full review articles.