Development and Implementation of the Data Science Learning Platform for Research Physician.
Data analysis and their application are the unavoidable factors in the activities analyses in health care. Unfortunately, the acquisition of data from large available medical databases is a complex process and requires deep knowledge of computer science and especially knowledge of tools for data man...
| Publicado en: | Studies in Health Technology & Informatics Vol. 299; pp. 196 - 202 |
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| Autores principales: | , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Sage Publications Inc.
2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160135221&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160135221 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2022 vid: 299 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 160135221 10.3233/SHTI220983 160135221 ppf: 196 ppct: 6 formats: tig: atl: Development and Implementation of the Data Science Learning Platform for Research Physician. aug: au: FAZLIC, Lejla BEGIC SCHACHT, Marvin MORGEN, Marlies SCHMEINK, Anke LIPP, Robert MARTIN, Lukas VOLLMER, Thomas WINTER, Stefan DARTMANN, Guido affil: ISS, Trier University of Applied Sciences, Trier, Germany. sug: ab: Data analysis and their application are the unavoidable factors in the activities analyses in health care. Unfortunately, the acquisition of data from large available medical databases is a complex process and requires deep knowledge of computer science and especially knowledge of tools for data management. According to the European General Data Protection Regulation, the problem becomes much more complex. Recognizing these problems and difficulties, we have developed a Data Science Learning Platform (DSLP) that primarily targets practitioners and researchers but also the computer science students. Using our proposed tool chain together with the developed graphical user interface, data scientists and research physicians will be able to use available medical databases, apply and analyze different anonymization methods, analyze data according to the patient’s risk and quickly formulate new studies to target a disease in a complex data model. This article presents a clinical research discovery toolbox that implements and demonstrates tools for data anonymization, patient data visualization, NLP-tools for guideline search and data science learning tools. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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