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Sharon Lee utilise la bonne technique
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C'est le moment de prendre soin de sa peau, de ses cheveux et de ses ongles après les longues heures d'exposition au soleil

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La quatrième génération de la série Galaxy Z vient changer notre rapport aux smartphones en repoussant les limites de la personnalisation, de l’expérience Flex...

Ooredoo, sponsor officiel du Festival International de Carthage continue à propager la joie et dessiner le sourire sur les visages des enfants et leur...

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CMAJ



v.193(44); 2021 Nov 8



PMC8584368






CMAJ. 2021 Nov 8; 193(44): E1708–E1715.
Réseau hospitalier Unity Health de Toronto (Verma, Murray, Straus, Pou-Prom, Mamdani); Institut du savoir Li Ka Shing de l’Hôpital St. Michael (Verma, Straus, Pou-Prom, Mamdani); Département de médecine (Verma, Shojania, Straus, Mamdani) et Institut des politiques, de la gestion et de l’évaluation de la santé (Verma, Mamdani) et Département de statistique (Murray), Université de Toronto, Toronto, Ont.; Université de l’Alberta (Greiner); Institut d’intelligence machine de l’Alberta (Greiner), Edmonton, Alb.; Institut des algorithmes d’apprentissage de Montréal (Cohen), Montréal, Qc.; Centre pour l’amélioration de la qualité et la sécurité des patients (Shojania), Université de Toronto; Centre des sciences de la santé Sunnybrook (Shojania); Institut Vecteur (Ghassemi, Mamdani) et Département des sciences informatiques (Ghassemi); Faculté de pharmacie Leslie Dan (Mamdani), Université de Toronto, Toronto, Ont.; Département de radiologie, Université Stanford (Cohen), Stanford, Calif.
Droit d'auteur © 2021 CMA Joule Inc. or its licensors
Il s’agit d’un article en libre accès distribué conformément aux modalités de la licence Creative Commons Attributions (CC BY-NC-ND 4.0), qui permet l’utilisation, la diffusion et la reproduction dans tout médium à la condition que la publication originale soit adéquatement citée, que l’utilisation se fasse à des fins non commerciales (c.-à-d., recherche ou éducation) et qu’aucune modification ni adaptation n’y soit apportée. Voir: https://creativecommons.org/licenses/by-nc-nd/4.0/

L’apprentissage machine a le potentiel de transformer le domaine de la santé, mais ses applications en médecine clinique sont pour le moment limitées.

Les partenariats multidisciplinaires entre experts techniques et utilisateurs (professionnels de la santé, gestionnaires, patients et membres de la famille) sont essentiels au développement et à la mise en œuvre des solutions fondées sur l’apprentissage machine en santé.

Un cadre en 3 phases peut être utilisé pour décrire le développement et l’adoption des solutions fondées sur l’apprentissage machine: une phase d’exploration pour comprendre le problème à régler et l’environnement de déploiement, une phase de conception de la solution pour le développement de modèles fondés sur l’apprentissage machine et d’outils conviviaux, et une phase de mise en œuvre et d’évaluation pour le déploiement de la solution et l’évaluation de ses retombées.
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Articles from CMAJ : Canadian Medical Association Journal are provided here courtesy of Canadian Medical Association
1. Liu Y, Chen PHC, Krause J, et al.. How to read articles that use machine learning: users’ guides to the medical literature . JAMA
2019; 322 :1806–16. [ PubMed ] [ Google Scholar ] [ Ref list ]
2. Sinsky C, Colligan L, Li L, et al.. Allocation of physician time in ambulatory practice: a time and motion study in 4 specialties . Ann Intern Med
2016; 165 :753–60. [ PubMed ] [ Google Scholar ] [ Ref list ]
3. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence . Nat Med
2019; 25 :44–56. [ PubMed ] [ Google Scholar ] [ Ref list ]
4. Ben-Israel D, Jacobs WB, Casha S, et al.. The impact of machine learning on patient care: a systematic review . Artif Intell Med
2020; 103 :101785. [ PubMed ] [ Google Scholar ] [ Ref list ]
5. Cohen JP, Cho T, Viviano JD, et al.. Problems in the deployment of machine-learned models in health care . CMAJ
2021.
Aug.
30 [cyberpublication avant impression]. doi: 10.1503/cmaj.202066. [ Article PMC gratuit ] [ PubMed ] [ CrossRef ] [ Ref list ]
6. Antoniou T, Mamdani MM. Evaluation of machine learning solutions in medicine . CMAJ
2021.
Aug.
30 [cyberpublication avant impression]. doi: 10.1503/cmaj.210036. [ Article PMC gratuit ] [ PubMed ] [ CrossRef ] [ Ref list ]
7. Chapman P, Clinton J, Kerber R, et al.. CRISP-DM 1.0: a step-by-step data mining guide . Armonk (NY): SPPS; 2000. Accessible ici: https://www.the-modeling-agency.com/crisp-dm.pdf (consulté le 18 mai 2021). [ Google Scholar ] [ Ref list ]
8. How to improve . Boston: Institute for Healthcare Improvement. Accessible ici: http://www.ihi.org/resources/Pages/HowtoImprove/default.aspx (consulté le 18 mai 2021). [ Google Scholar ] [ Ref list ]
9. Graham ID, Logan J, Harrison MB, et al.. Lost in knowledge translation: time for a map?
J Contin Educ Health Prof
2006; 26 :13–24. [ PubMed ] [ Google Scholar ] [ Ref list ]
10. van Galen LS, Struik PW, Driesen BEJM, et al.. Delayed recognition of deterioration of patients in general wards is mostly caused by human related monitoring failures: A root cause analysis of unplanned ICU admissions . PLoS One
2016; 11 :e0161393. doi: 10.1371/journal.pone.0161393. [ Article PMC gratuit ] [ PubMed ] [ CrossRef ] [ Google Scholar ] [ Ref list ]
11. Burch VC, Tarr G, Morroni C. Modified early warning score predicts the need for hospital admission and in-hospital mortality . Emerg Med J
2008; 25 :674–8. [ PubMed ] [ Google Scholar ] [ Ref list ]
12. McGinley A, Pearse RM. A national early warning score for acutely ill patients . BMJ
2012; 345 :e5310. [ PubMed ] [ Google Scholar ] [ Ref list ]
13. Linnen DT, Escobar GJ, Hu X, et al.. Statistical modeling and aggregate-weighted scoring systems in prediction of mortality and ICU transfer: a systematic review . J Hosp Med
2019; 14 :161–9. [ Article PMC gratuit ] [ PubMed ] [ Google Scholar ] [ Ref list ]
14. Verma AA, Guo Y, Kwan JL, et al.. Patient characteristics, resource use and outcomes associated with general internal medicine hospital care: the General Medicine Inpatient Initiative (GEMINI) retrospective cohort study . CMAJ Open
2017; 5 :E842–9. [ Article PMC gratuit ] [ PubMed ] [ Google Scholar ] [ Ref list ]
15. Quality Improvement Essentials Toolkit . Boston: Institute for Healthcare Improvement; 2021. Accessible ici: www.ihi.org/resources/Pages/Tools/Quality-Improvement-Essentials-Toolkit.aspx (consulté le 18 mai 2021). [ Google Scholar ] [ Ref list ]
16. Etchells E, Ho M, Shojania KG. Value of small sample sizes in rapid-cycle quality improvement projects . BMJ Qual Saf
2016; 25 :202–6. [ PubMed ] [ Google Scholar ] [ Ref list ]
17. Escobar GJ, Liu VX, Schuler A, et al.. Automated identification of adults at risk for in-hospital clinical deterioration . N Engl J Med
2020; 383 :1951–60. [ Article PMC gratuit ] [ PubMed ] [ Google Scholar ] [ Ref list ]
19. Verma AA, Pasricha SV, Jung HY, et al.. Assessing the quality of clinical and administrative data extracted from hospitals: the General Medicine Inpatient Initiative (GEMINI) experience . J Am Med Inform Assoc
2021; 28 :578–87. [ Article
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