Machine learning based framework to predict cardiac arrests in a paediatric intensive care unit : Prediction of cardiac arrests.
A cardiac arrest is a life-threatening event, often fatal. Whilst clinicians classify some of the cardiac arrests as potentially predictable, the majority are difficult to identify even in a post-incident analysis. Changes in some patients' physiology when analysed in detail can however be predictiv...
| Publicado en: | Journal of Clinical Monitoring & Computing Vol. 33; no. 4; pp. 713 - 725 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Aug2019
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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=137276598&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137276598 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13871307 OHC jtl: Journal of Clinical Monitoring & Computing issn: 13871307 maglogo: N pubinfo: dt: Aug2019 vid: 33 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137276598 137276598 NLM30264218 10.1007/s10877-018-0198-0 NLM30264218 137276598 ppf: 713 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine learning based framework to predict cardiac arrests in a paediatric intensive care unit : Prediction of cardiac arrests. aug: au: Matam, B. R. Duncan, Heather Lowe, David affil: Arden University, Coventry, UK sug: subj: Intensive Care Units, Pediatric Critical Care Methods Monitoring, Physiologic Equipment and Supplies Heart Arrest Diagnosis Systole Reproducibility of Results Software Signal Processing, Computer Assisted Respiratory Rate Monitoring, Physiologic Methods Critical Illness Blood Pressure Infant, Newborn Child Oxygen Metabolism Child, Preschool Heart Rate Infant Sensitivity and Specificity Scales Infant, Newborn: birth-1 month Child: 6-12 years Child, Preschool: 2-5 years Infant: 1-23 months ab: A cardiac arrest is a life-threatening event, often fatal. Whilst clinicians classify some of the cardiac arrests as potentially predictable, the majority are difficult to identify even in a post-incident analysis. Changes in some patients' physiology when analysed in detail can however be predictive of acute deterioration leading to cardiac or respiratory arrests. This paper seeks to exploit the causally-related changing patterns in signals such as heart rate, respiration rate, systolic blood pressure and peripheral cutaneous oxygen saturation to evaluate the predictability of cardiac arrests in critically ill paediatric patients in intensive care. In this paper we report the results of a framework constituting feature space embedding and time series forecasting methods to build an automated prediction system. The results were compared with clinical assessment of predictability. A sensitivity of 71% and specificity of 69% was obtained when the maximum value of Anomaly Index (12) in the 50 min (starting one hour and ending 10 min) before the arrest was considered for the case patients and a random 50 min of data was considered for the control set patients. A positive predictive value of 11% and negative predictive value of 98% was obtained with a prevalence of 5% by our method of prediction. While clinicians predicted 4 out of the 69 cardiac arrests (6%), the prediction system predicted 63 (91%) cardiac arrests. Prospective validation of the automated system remains. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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