Analyzing Patient Trajectories With Artificial Intelligence.
In digital medicine, patient data typically record health events over time (eg, through electronic health records, wearables, or other sensing technologies) and thus form unique patient trajectories. Patient trajectories are highly predictive of the future course of diseases and therefore facilitate...
| Publicado en: | Journal of Medical Internet Research Vol. 23; no. 12 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
JMIR Publications Inc.
Dec2021
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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=154477058&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154477058 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14394456 DNC jtl: Journal of Medical Internet Research issn: 14394456 maglogo: N pubinfo: dt: Dec2021 vid: 23 iid: 12 pid: 21567 pub: JMIR Publications Inc. place: Toronto, Ontario artinfo: ui: 154477058 154477058 NLM34870606 154477058 10.2196/29812 NLM34870606 154477058 ppct: 1 formats: tig: atl: Analyzing Patient Trajectories With Artificial Intelligence. aug: au: Allam, Ahmed Feuerriegel, Stefan Rebhan, Michael Krauthammer, Michael affil: 1 Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland sug: subj: Artificial Intelligence Human Comparative Studies Multicenter Studies Evaluation Research Validation Studies ab: In digital medicine, patient data typically record health events over time (eg, through electronic health records, wearables, or other sensing technologies) and thus form unique patient trajectories. Patient trajectories are highly predictive of the future course of diseases and therefore facilitate effective care. However, digital medicine often uses only limited patient data, consisting of health events from only a single or small number of time points while ignoring additional information encoded in patient trajectories. To analyze such rich longitudinal data, new artificial intelligence (AI) solutions are needed. In this paper, we provide an overview of the recent efforts to develop trajectory-aware AI solutions and provide suggestions for future directions. Specifically, we examine the implications for developing disease models from patient trajectories along the typical workflow in AI: problem definition, data processing, modeling, evaluation, and interpretation. We conclude with a discussion of how such AI solutions will allow the field to build robust models for personalized risk scoring, subtyping, and disease pathway discovery. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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