S. Sikdar, S. Guha, K. Ganguly, S. Bag, Sriya Sona Lenka, H. Barman
From the earliest times, the concept of ill health with its treatment is associated with the interpretation of multi-faceted health data. Large amount of information about a patient’s past medical records, symptomatology, diagnoses and responses to prescribe treatments and therapies needs to be collected as these informations are crucial to the clinical diagnostic process and that is why electronic health data are central to all medical care and research for large aggregate collection and analysis. Apart from the immediate clinical or diagnostic evaluation of these heterogeneous datasets, analysis of the same by utilizing software tools produces valuable information that leads to novel biomedical discoveries, improved diagnostics processes, advancements in epidemiology and biomedical research & education. Biomedical data science identifies the requirement of additional information and strategy formulation in understanding and controlling of specific health anomalies in a most effective manner. In fact, the model of biomedical data science is a simple reflection that reveals the fact that all medical care activities involve gathering, analyzing and storage of electronic health records. The paper is a retrospective study that provides a systematic review of the entire methodology, application and advances in Biomedical Data science which is a very promising and progressing section for advanced medical technology.
{"title":"A Retrospective Study on the Shifting Model in Clinical Diagnostics Integrating Data Science With Medical Technology","authors":"S. Sikdar, S. Guha, K. Ganguly, S. Bag, Sriya Sona Lenka, H. Barman","doi":"10.2139/ssrn.3503284","DOIUrl":"https://doi.org/10.2139/ssrn.3503284","url":null,"abstract":"From the earliest times, the concept of ill health with its treatment is associated with the interpretation of multi-faceted health data. Large amount of information about a patient’s past medical records, symptomatology, diagnoses and responses to prescribe treatments and therapies needs to be collected as these informations are crucial to the clinical diagnostic process and that is why electronic health data are central to all medical care and research for large aggregate collection and analysis. Apart from the immediate clinical or diagnostic evaluation of these heterogeneous datasets, analysis of the same by utilizing software tools produces valuable information that leads to novel biomedical discoveries, improved diagnostics processes, advancements in epidemiology and biomedical research & education. Biomedical data science identifies the requirement of additional information and strategy formulation in understanding and controlling of specific health anomalies in a most effective manner. In fact, the model of biomedical data science is a simple reflection that reveals the fact that all medical care activities involve gathering, analyzing and storage of electronic health records. The paper is a retrospective study that provides a systematic review of the entire methodology, application and advances in Biomedical Data science which is a very promising and progressing section for advanced medical technology.","PeriodicalId":350618,"journal":{"name":"ChemRN: Other Analytical Chemistry (Topic)","volume":"35 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116645298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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