Machine learning applied to multi-sensor information to reduce false alarm rate in the ICU.
Studies reveal that the false alarm rate (FAR) demonstrated by intensive care unit (ICU) vital signs monitors ranges from 0.72 to 0.99. We applied machine learning (ML) to ICU multi-sensor information to imitate a medical specialist in diagnosing patient condition. We hypothesized that applying this...
| Publicado en: | Journal of Clinical Monitoring & Computing Vol. 34; no. 2; pp. 339 - 353 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2020
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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=142316639&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142316639 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13871307 OHC jtl: Journal of Clinical Monitoring & Computing issn: 13871307 maglogo: N pubinfo: dt: Apr2020 vid: 34 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142316639 142316639 NLM30955160 142316639 10.1007/s10877-019-00307-x NLM30955160 142316639 ppf: 339 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine learning applied to multi-sensor information to reduce false alarm rate in the ICU. aug: au: Hever, Gal Cohen, Liel O'Connor, Michael F. Matot, Idit Lerner, Boaz Bitan, Yuval affil: Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, POB 653, Beer Sheva, Israel sug: subj: Intensive Care Units Laboratory Equipment and Supplies Statistics and Numerical Data Information Science Decision Support Techniques Retrospective Design False Positive Results Monitoring, Physiologic Statistics and Numerical Data Expert Systems Critical Care Statistics and Numerical Data Knowledge Bases ab: Studies reveal that the false alarm rate (FAR) demonstrated by intensive care unit (ICU) vital signs monitors ranges from 0.72 to 0.99. We applied machine learning (ML) to ICU multi-sensor information to imitate a medical specialist in diagnosing patient condition. We hypothesized that applying this data-driven approach to medical monitors will help reduce the FAR even when data from sensors are missing. An expert-based rules algorithm identified and tagged in our dataset seven clinical alarm scenarios. We compared a random forest (RF) ML model trained using the tagged data, where parameters (e.g., heart rate or blood pressure) were (deliberately) removed, in detecting ICU signals with the full expert-based rules (FER), our ground truth, and partial expert-based rules (PER), missing these parameters. When all alarm scenarios were examined, RF and FER were almost identical. However, in the absence of one to three parameters, RF maintained its values of the Youden index (0.94-0.97) and positive predictive value (PPV) (0.98-0.99), whereas PER lost its value (0.54-0.8 and 0.76-0.88, respectively). While the FAR for PER with missing parameters was 0.17-0.39, it was only 0.01-0.02 for RF. When scenarios were examined separately, RF showed clear superiority in almost all combinations of scenarios and numbers of missing parameters. When sensor data are missing, specialist performance worsens with the number of missing parameters, whereas the RF model attains high accuracy and low FAR due to its ability to fuse information from available sensors, compensating for missing parameters. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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