Evaluation of Machine Learning Models for Clinical Prediction Problems
- PMID: 35583620
- PMCID: PMC9177058
- DOI: 10.1097/PCC.0000000000002942
Evaluation of Machine Learning Models for Clinical Prediction Problems
Conflict of interest statement
Dr. Bennett’s institution received funding from the National Institute of Child Health and Human Development, the National Center for Advancing Translational Sciences, and the National Heart, Lung, and Blood Institute; he received support for article research from the National Institutes of Health. Dr. Sanchez-Pinto has disclosed that he does not have any potential conflicts of interest.
Comment on
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Dynamic Mortality Risk Predictions for Children in ICUs: Development and Validation of Machine Learning Models.Pediatr Crit Care Med. 2022 May 1;23(5):344-352. doi: 10.1097/PCC.0000000000002910. Epub 2022 May 5. Pediatr Crit Care Med. 2022. PMID: 35190501 Free PMC article.
References
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- Légaré F, Stacey D, Turcotte S, Cossi MJ, Kryworuchko J, Graham ID, et al. Interventions for improving the adoption of shared decision making by healthcare professionals. Cochrane database of systematic reviews. 2014(9). - PubMed
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- Jacobsen M, O’Connor A. Population needs assessment: A workbook for assessing patients’ and practitioners’ decision making needs. Ottawa: University of Ottawa. 2006.
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- Bennett TD, Marsh R, Maertens JA, Rutebemberwa A, Morris MA, Hankinson TC, et al. Decision-making about intracranial pressure monitor placement in children with traumatic brain injury. Pediatr Crit Care Med. 2019;20(7):645–51. - PubMed
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