Prediction of Periventricular Leukomalacia in Neonates after Cardiac Surgery Using Machine Learning Algorithms.
Periventricular leukomalacia (PVL) is brain injury that develops commonly in neonates after cardiac surgery. Earlier identification of patients who are at higher risk for PVL may improve clinicians’ ability to optimize care for these challenging patients. The aim of this study was to apply machine l...
| Published in: | Journal of Medical Systems Vol. 42; no. 10; pp. 1 - 2 |
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| Main Authors: | , , , , |
| Format: | algorithm equations & formulas research tables/charts tracings Journal Article |
| Published: |
Springer Nature
Oct2018
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=132085505&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 132085505 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Oct2018 vid: 42 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 132085505 132085505 132085505 10.1007/s10916-018-1029-z 132085505 ppf: 1 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Prediction of Periventricular Leukomalacia in Neonates after Cardiac Surgery Using Machine Learning Algorithms. aug: au: Jalali, Ali Simpao, Allan F. Gálvez, Jorge A. Licht, Daniel J. Nataraj, Chandrasekhar affil: Department of Health Informatics, Johns Hopkins All Children's Hospital, 501 6th Avenue South, 33701, St. Petersburg, FL, USA sug: subj: Machine Learning Algorithms Electrocardiography Methods Cardiac Surgery Brain Diseases Pathology Human Decision Support Systems, Clinical Infant, Newborn Heart Defects, Congenital Vital Signs Hypoplastic Left Heart Syndrome Laboratory Test Panels Pennsylvania Retrospective Design Nonexperimental Studies Pulse Oximetry Treatment Outcomes Infant, Newborn: birth-1 month ab: Periventricular leukomalacia (PVL) is brain injury that develops commonly in neonates after cardiac surgery. Earlier identification of patients who are at higher risk for PVL may improve clinicians’ ability to optimize care for these challenging patients. The aim of this study was to apply machine learning algorithms and wavelet analysis to vital sign and laboratory data obtained from neonates immediately after cardiac surgery to predict PVL occurrence. We analyzed physiological data of patients with and without hypoplastic left heart syndrome (HLHS) during the first 12 h after cardiac surgery. Wavelet transform was applied to extract time-frequency information from the data. We ranked the extracted features to select the most discriminative features, and the support vector machine with radial basis function as a kernel was selected as the classifier. The classifier was optimized via three methods: (1) mutual information, (2) modified mutual information considering the reliability of features, and (3) modified mutual information with reliability index and maximizing set’s mutual information. We assessed the accuracy of the classifier at each time point. A total of 71 neonates met the study criteria. The rates of PVL occurrence were 33% for all patients, with 41% in the HLHS group and 25% in the non-HLHS group. The F-score results for HLHS patients and non-HLHS patients were 0.88 and 1.00, respectively. Using maximizing set’s mutual information improved the classifier performance in the all patient groups from 0.69 to 0.81. The novel application of a modified mutual information ranking system with the reliability index in a PVL prediction model provided highly accurate identification. This tool is a promising step for improving the care of neonates who are at higher risk for developing PVL following cardiac surgery. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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