A review of recent advances in data analytics for post-operative patient deterioration detection.
Most deaths occurring due to a surgical intervention happen postoperatively rather than during surgery. The current standard of care in many hospitals cannot fully cope with detecting and addressing post-surgical deterioration in time. For millions of patients, this deterioration is left unnoticed,...
| Publicado en: | Journal of Clinical Monitoring & Computing Vol. 32; no. 3; pp. 391 - 403 |
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| Autores principales: | , , |
| Formato: | pictorial review tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2018
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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=129595153&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 129595153 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13871307 OHC jtl: Journal of Clinical Monitoring & Computing issn: 13871307 maglogo: N pubinfo: dt: Jun2018 vid: 32 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 129595153 129595153 NLM28828569 129595153 10.1007/s10877-017-0054-7 NLM28828569 129595153 ppf: 391 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A review of recent advances in data analytics for post-operative patient deterioration detection. aug: au: Petit, Clemence Bezemer, Rick Atallah, Louis affil: Department of Electrical Engineering, Technical University of Eindhoven, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands sug: subj: Postoperative Complications Diagnosis Medical Informatics Methods Data Collection Information Science Risk Assessment Postoperative Period Models, Theoretical Diagnosis, Computer Assisted Comorbidity ab: Most deaths occurring due to a surgical intervention happen postoperatively rather than during surgery. The current standard of care in many hospitals cannot fully cope with detecting and addressing post-surgical deterioration in time. For millions of patients, this deterioration is left unnoticed, leading to increased mortality and morbidity. Postoperative deterioration detection currently relies on general scores that are not fully able to cater for the complex post-operative physiology of surgical patients. In the last decade however, advanced risk and warning scoring techniques have started to show encouraging results in terms of using the large amount of data available peri-operatively to improve postoperative deterioration detection. Relevant literature has been carefully surveyed to provide a summary of the most promising approaches as well as how they have been deployed in the perioperative domain. This work also aims to highlight the opportunities that lie in personalizing the models developed for patient deterioration for these particular post-surgical patients and make the output more actionable. The integration of pre- and intra-operative data, e.g. comorbidities, vitals, lab data, and information about the procedure performed, in post-operative early warning algorithms would lead to more contextualized, personalized, and adaptive patient modelling. This, combined with careful integration in the clinical workflow, would result in improved clinical decision support and better post-surgical care outcomes. pubtype: Academic Journal doctype: pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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