PrositNG - A Machine Learning Supported Disease Model Generation Software.
Decision models (DM), especially Markov Models, play an essential role in the economic evaluation of new medical interventions. The process of DM generation requires expert knowledge of the medical domain and is a timeconsuming task. Therefore, the authors propose a new model generation software Pro...
| Publicado en: | Studies in Health Technology & Informatics Vol. 272; pp. 151 - 155 |
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| Autores principales: | , , , |
| Formato: | pictorial Journal Article |
| Publicado: |
Sage Publications Inc.
2020
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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=144396616&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144396616 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2020 vid: 272 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 144396616 144396616 144396616 10.3233/SHTI200516 144396616 ppf: 151 ppct: 4 formats: tig: atl: PrositNG - A Machine Learning Supported Disease Model Generation Software. aug: au: POBIRUCHIN, Monika ZOWALLA, Richard KURSCHEIDT, Maximilian SCHRAMM, Wendelin affil: GECKO Institute, Heilbronn University, Heilbronn, Germany sug: subj: Technology, Medical Economics Data Management Methods Machine Learning Software Decision Support Techniques Electronic Health Records Algorithms ab: Decision models (DM), especially Markov Models, play an essential role in the economic evaluation of new medical interventions. The process of DM generation requires expert knowledge of the medical domain and is a timeconsuming task. Therefore, the authors propose a new model generation software PrositNG that is connectable to database systems of real-world routine care data. The structure of the model is derived from the entries in a database system by the help of Machine Learning algorithms. The software was implemented with the programming language Java. Two data sources were successfully utilized to demonstrate the value of PrositNG. However, a good understanding of the local documentation routine and software is paramount to use real-world data for model generation. pubtype: Academic Journal doctype: pictorial Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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