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. |
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| 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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