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Machine learning time-to-event algorithms for predicting the duration of ventilation after cardiac surgery.

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Title: Machine learning time-to-event algorithms for predicting the duration of ventilation after cardiac surgery.
Authors: Chee ML; Royal Melbourne Hospital, Melbourne Health, Melbourne, Australia.; Port Lincoln Hospital, Eyre and Far North Local Health Network, Port Lincoln, South Australia, Australia., Gu A; Royal Melbourne Hospital, Melbourne Health, Melbourne, Australia., Karri R; Royal Victorian Eye & Ear Hospital, Melbourne, Victoria, Australia., Perry L; Victorian Cardiac Anaesthesia Research Laboratory, Victorian Heart Institute, Monash University, Melbourne, Australia.; Department of Anaesthesia, Victorian Heart Hospital, Monash Health, Melbourne, Australia., Smith JA; Department of Surgery (School of Clinical Sciences at Monash Health), Monash University, Melbourne, Australia.; Department of Cardiothoracic Surgery, Monash Health, Clayton, Victoria, Australia., Penny-Dimri J; Department of Surgery (School of Clinical Sciences at Monash Health), Monash University, Melbourne, Australia.
Source: JTCVS open [JTCVS Open] 2025 Nov 28; Vol. 29, pp. 101537. Date of Electronic Publication: 2025 Nov 28 (Print Publication: 2026).
Publication Type: Journal Article
Journal Info: Publisher: Elsevier Ltd Country of Publication: Netherlands NLM ID: 101768541 Publication Model: eCollection Cited Medium: Internet ISSN: 2666-2736 (Electronic) Linking ISSN: 26662736 NLM ISO Abbreviation: JTCVS Open Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
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  Data: Machine learning time-to-event algorithms for predicting the duration of ventilation after cardiac surgery.
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  Data: <searchLink fieldCode="AU" term="%22Chee+ML%22">Chee ML</searchLink>; Royal Melbourne Hospital, Melbourne Health, Melbourne, Australia.; Port Lincoln Hospital, Eyre and Far North Local Health Network, Port Lincoln, South Australia, Australia.<br /><searchLink fieldCode="AU" term="%22Gu+A%22">Gu A</searchLink>; Royal Melbourne Hospital, Melbourne Health, Melbourne, Australia.<br /><searchLink fieldCode="AU" term="%22Karri+R%22">Karri R</searchLink>; Royal Victorian Eye & Ear Hospital, Melbourne, Victoria, Australia.<br /><searchLink fieldCode="AU" term="%22Perry+L%22">Perry L</searchLink>; Victorian Cardiac Anaesthesia Research Laboratory, Victorian Heart Institute, Monash University, Melbourne, Australia.; Department of Anaesthesia, Victorian Heart Hospital, Monash Health, Melbourne, Australia.<br /><searchLink fieldCode="AU" term="%22Smith+JA%22">Smith JA</searchLink>; Department of Surgery (School of Clinical Sciences at Monash Health), Monash University, Melbourne, Australia.; Department of Cardiothoracic Surgery, Monash Health, Clayton, Victoria, Australia.<br /><searchLink fieldCode="AU" term="%22Penny-Dimri+J%22">Penny-Dimri J</searchLink>; Department of Surgery (School of Clinical Sciences at Monash Health), Monash University, Melbourne, Australia.
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  Data: <searchLink fieldCode="JN" term="%22101768541%22">JTCVS open</searchLink> [JTCVS Open] 2025 Nov 28; Vol. 29, pp. 101537. <i>Date of Electronic Publication: </i>2025 Nov 28 (<i>Print Publication: </i>2026).
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Elsevier+Ltd%22">Elsevier Ltd </searchLink><i>Country of Publication: </i>Netherlands <i>NLM ID: </i>101768541 <i>Publication Model: </i>eCollection <i>Cited Medium: </i>Internet <i>ISSN: </i>2666-2736 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2226662736%22">26662736 </searchLink><i>NLM ISO Abbreviation: </i>JTCVS Open <i>Subsets: </i>PubMed not MEDLINE
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        Value: 10.1016/j.xjon.2025.11.021
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        Text: English
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              Text: 2025 Nov 28
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