{"title":"临床数据分析的挑战和趋势","authors":"","doi":"10.46243/jst.2020.v5.i4.pp348-360","DOIUrl":null,"url":null,"abstract":":Today’s technological advancements facilitated the researcher in collecting and organizing various forms\nof healthcare data. Data is an integral part of health care analytics. Drug discovery for clinical data analytics forms\nan important breakthrough work in terms of computational approaches in health care systems. On the other hand,\nhealthcare analysis provides better value for money. The health care data management is very challenging as 80%\nof the data is unstructured as it includes handwritten documents, images; computer-generated clinical reports such\nas MRI, ECG, city scan, etc. The paper aims at providing a summary of work carried out by scientists and\nresearchers who worked in health care domains. More precisely the work focuses on clinical data analysis for the\nperiod 2013 to 2019. The organization of the work carried out is specifically with concerned to data sets,\nTechniques, and Methods used, Tools adopted, Key Findings in clinical data analysis. The overall objective is to\nidentify the current challenges, trends, and gaps in clinical data analysis. The pathway of the work is focused on\ncarrying out on the bibliometric survey and summarization of the key findings in a novel way.","PeriodicalId":23534,"journal":{"name":"Volume 5, Issue 4","volume":"9 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Challenges and Trends in Clinical Data Analytics\",\"authors\":\"\",\"doi\":\"10.46243/jst.2020.v5.i4.pp348-360\",\"DOIUrl\":null,\"url\":null,\"abstract\":\":Today’s technological advancements facilitated the researcher in collecting and organizing various forms\\nof healthcare data. Data is an integral part of health care analytics. Drug discovery for clinical data analytics forms\\nan important breakthrough work in terms of computational approaches in health care systems. On the other hand,\\nhealthcare analysis provides better value for money. The health care data management is very challenging as 80%\\nof the data is unstructured as it includes handwritten documents, images; computer-generated clinical reports such\\nas MRI, ECG, city scan, etc. The paper aims at providing a summary of work carried out by scientists and\\nresearchers who worked in health care domains. More precisely the work focuses on clinical data analysis for the\\nperiod 2013 to 2019. The organization of the work carried out is specifically with concerned to data sets,\\nTechniques, and Methods used, Tools adopted, Key Findings in clinical data analysis. The overall objective is to\\nidentify the current challenges, trends, and gaps in clinical data analysis. The pathway of the work is focused on\\ncarrying out on the bibliometric survey and summarization of the key findings in a novel way.\",\"PeriodicalId\":23534,\"journal\":{\"name\":\"Volume 5, Issue 4\",\"volume\":\"9 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-07-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Volume 5, Issue 4\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.46243/jst.2020.v5.i4.pp348-360\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Volume 5, Issue 4","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46243/jst.2020.v5.i4.pp348-360","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
:Today’s technological advancements facilitated the researcher in collecting and organizing various forms
of healthcare data. Data is an integral part of health care analytics. Drug discovery for clinical data analytics forms
an important breakthrough work in terms of computational approaches in health care systems. On the other hand,
healthcare analysis provides better value for money. The health care data management is very challenging as 80%
of the data is unstructured as it includes handwritten documents, images; computer-generated clinical reports such
as MRI, ECG, city scan, etc. The paper aims at providing a summary of work carried out by scientists and
researchers who worked in health care domains. More precisely the work focuses on clinical data analysis for the
period 2013 to 2019. The organization of the work carried out is specifically with concerned to data sets,
Techniques, and Methods used, Tools adopted, Key Findings in clinical data analysis. The overall objective is to
identify the current challenges, trends, and gaps in clinical data analysis. The pathway of the work is focused on
carrying out on the bibliometric survey and summarization of the key findings in a novel way.