Artificial intelligence in peri‐operative prediction model research: are we there yet?

医学 图书馆学 引用 经典 历史 计算机科学
作者
Akshay Shah,Paula Dhiman
出处
期刊:Anaesthesia [Wiley]
卷期号:79 (10): 1017-1022 被引量:1
标识
DOI:10.1111/anae.16315
摘要

AnaesthesiaEarly View Editorial Artificial intelligence in peri-operative prediction model research: are we there yet? Akshay Shah, Corresponding Author Akshay Shah [email protected] orcid.org/0000-0002-1869-2231 DocAShah Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK Correspondence to: Akshay Shah Email: [email protected]Search for more papers by this authorPaula Dhiman, Paula Dhiman orcid.org/0000-0002-0989-0623 pauladhiman Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, Centre for Statistics in Medicine, University of Oxford, Oxford, UKSearch for more papers by this author Akshay Shah, Corresponding Author Akshay Shah [email protected] orcid.org/0000-0002-1869-2231 DocAShah Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK Correspondence to: Akshay Shah Email: [email protected]Search for more papers by this authorPaula Dhiman, Paula Dhiman orcid.org/0000-0002-0989-0623 pauladhiman Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, Centre for Statistics in Medicine, University of Oxford, Oxford, UKSearch for more papers by this author First published: 15 May 2024 https://doi.org/10.1111/anae.16315 1 Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK 2 Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, Centre for Statistics in Medicine, University of Oxford, Oxford, UK This article accompanies an article by Xia et al., Anaesthesia 2024; 79: 399–409. https://doi.org/10.1111/anae.16194. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat References 1Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng 2018; 2: 719–731. https://doi.org/10.1038/s41551-018-0305-z. 10.1038/s41551-018-0305-z PubMedWeb of Science®Google Scholar 2Abramoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med 2018; 1: 39. https://doi.org/10.1038/s41746-018-0040-6. 10.1038/s41746-018-0040-6 PubMedWeb of Science®Google Scholar 3Benjamens S, Dhunnoo P, Mesko B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ Digit Med 2020; 3: 118. https://doi.org/10.1038/s41746-020-00324-0. 10.1038/s41746-020-00324-0 PubMedWeb of Science®Google Scholar 4Hashimoto DA, Witkowski E, Gao L, Meireles O, Rosman G. Artificial intelligence in anesthesiology: current techniques, clinical applications, and limitations. Anesthesiology 2020; 132: 379–394. https://doi.org/10.1097/ALN.0000000000002960. 10.1097/ALN.0000000000002960 PubMedWeb of Science®Google Scholar 5Xia M, Jin C, Zheng Y, et al. Deep learning-based facial analysis for predicting difficult videolaryngoscopy: a feasibility study. Anaesthesia 2024; 79: 399–409. https://doi.org/10.1111/anae.16194. 10.1111/anae.16194 CASPubMedWeb of Science®Google Scholar 6 NHS. AI Dictionary. https://nhsx.github.io/ai-dictionary?term=ai (accessed 10/02/2024). Google Scholar 7Bisgin H, Liu Z, Fang H, Xu X, Tong W. Mining FDA drug labels using an unsupervised learning technique - topic modeling. BMC Bioinformatics 2011; 12(Suppl 10): S11. https://doi.org/10.1186/1471-2105-12-S10-S11. 10.1186/1471-2105-12-S10-S11 PubMedWeb of Science®Google Scholar 8Goodfellow I, Bengio Y, Courville A. Deep Learning. Cambridge, MA: MIT Press, 2016. Google Scholar 9Chrimes N, Higgs A, Hagberg CA, et al. Preventing unrecognised oesophageal intubation: a consensus guideline from the Project for Universal Management of Airways and international airway societies. Anaesthesia 2022; 77: 1395–1415. https://doi.org/10.1111/anae.15817. 10.1111/anae.15817 CASPubMedWeb of Science®Google Scholar 10Hansel J, Rogers AM, Lewis SR, Cook TM, Smith AF. Videolaryngoscopy versus direct laryngoscopy for adults undergoing tracheal intubation: a Cochrane systematic review and meta-analysis update. Br J Anaesth 2022; 129: 612–623. https://doi.org/10.1016/j.bja.2022.05.027. 10.1016/j.bja.2022.05.027 PubMedWeb of Science®Google Scholar 11van der Ploeg T, Austin PC, Steyerberg EW. Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints. BMC Med Res Methodol 2014; 14: 137. https://doi.org/10.1186/1471-2288-14-137. 10.1186/1471-2288-14-137 PubMedWeb of Science®Google Scholar 12Riley RD, Ensor J, Snell KIE, et al. Calculating the sample size required for developing a clinical prediction model. BMJ 2020; 368: m441. https://doi.org/10.1136/bmj.m441. 10.1136/bmj.m441 PubMedGoogle Scholar 13van den Goorbergh R, van Smeden M, Timmerman D, Van Calster B. The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression. J Am Med Inform Assoc 2022; 29: 1525–1534. https://doi.org/10.1093/jamia/ocac093. 10.1093/jamia/ocac093 PubMedWeb of Science®Google Scholar 14Riley RD, Collins GS. Stability of clinical prediction models developed using statistical or machine learning methods. Biom J 2023; 65: e2200302. https://doi.org/10.1002/bimj.202200302. 10.1002/bimj.202200302 PubMedWeb of Science®Google Scholar 15Alba AC, Agoritsas T, Walsh M, et al. Discrimination and calibration of clinical prediction models: users' guides to the medical literature. JAMA 2017; 318: 1377–1384. https://doi.org/10.1001/jama.2017.12126. 10.1001/jama.2017.12126 PubMedWeb of Science®Google Scholar 16Van Calster B, McLernon DJ, van Smeden M, et al. Calibration: the Achilles heel of predictive analytics. BMC Med 2019; 17: 230. https://doi.org/10.1186/s12916-019-1466-7. 10.1186/s12916-019-1466-7 PubMedWeb of Science®Google Scholar 17Sauerbrei W, Perperoglou A, Schmid M, et al. State of the art in selection of variables and functional forms in multivariable analysis-outstanding issues. Diagn Progn Res 2020; 4: 3. https://doi.org/10.1186/s41512-020-00074-3. 10.1186/s41512-020-00074-3 PubMedGoogle Scholar 18Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol 2019; 110: 12–22. https://doi.org/10.1016/j.jclinepi.2019.02.004. 10.1016/j.jclinepi.2019.02.004 PubMedWeb of Science®Google Scholar 19Ruppert MM, Lipori J, Patel S, et al. ICU delirium-prediction models: a systematic review. Crit Care Explor 2020; 2: e0296. https://doi.org/10.1097/CCE.0000000000000296. 10.1097/CCE.0000000000000296 PubMedGoogle Scholar 20Smith LA, Oakden-Rayner L, Bird A, Zeng M, To MS, Mukherjee S, Palmer LJ. Machine learning and deep learning predictive models for long-term prognosis in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis. Lancet Digit Health 2023; 5: e872–e881. https://doi.org/10.1016/S2589-7500(23)00177-2. 10.1016/S2589-7500(23)00177-2 CASPubMedGoogle Scholar 21Gravesteijn BY, Nieboer D, Ercole A, et al. Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury. J Clin Epidemiol 2020; 122: 95–107. https://doi.org/10.1016/j.jclinepi.2020.03.005. 10.1016/j.jclinepi.2020.03.005 PubMedWeb of Science®Google Scholar 22Boutron I, Ravaud P. Misrepresentation and distortion of research in biomedical literature. Proc Natl Acad Sci U S A 2018; 115: 2613–2619. https://doi.org/10.1073/pnas.1710755115. 10.1073/pnas.1710755115 PubMedWeb of Science®Google Scholar 23Andaur Navarro CL, Damen JAA, Takada T, et al. Systematic review finds "spin" practices and poor reporting standards in studies on machine learning-based prediction models. J Clin Epidemiol 2023; 158: 99–110. https://doi.org/10.1016/j.jclinepi.2023.03.024. 10.1016/j.jclinepi.2023.03.024 PubMedWeb of Science®Google Scholar 24Dhiman P, Ma J, Andaur Navarro CL, et al. Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review. J Clin Epidemiol 2023; 157: 120–133. https://doi.org/10.1016/j.jclinepi.2023.03.012. 10.1016/j.jclinepi.2023.03.012 PubMedWeb of Science®Google Scholar 25Andaur Navarro CL, Damen JA, Ghannad M, et al. SPIN-PM: a consensus framework to evaluate the presence of spin in studies on prediction models. J Clin Epidemiol 2024. Epub 15 April. https://doi.org/10.1016/j.jclinepi.2024.111364. 10.1016/j.jclinepi.2024.111364 Google Scholar 26Andaur Navarro CL, Damen JAA, Takada T, et al. Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review. BMJ 2021; 375: n2281. https://doi.org/10.1136/bmj.n2281. 10.1136/bmj.n2281 PubMedGoogle Scholar 27Andaur Navarro CL, Damen JAA, Takada T, et al. Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review. BMC Med Res Methodol 2022; 22: 12. https://doi.org/10.1186/s12874-021-01469-6. 10.1186/s12874-021-01469-6 PubMedWeb of Science®Google Scholar 28Arina P, Kaczorek MR, Hofmaenner DA, et al. Prediction of complications and prognostication in perioperative medicine: a systematic review and PROBAST assessment of machine learning tools. Anesthesiology 2024; 140: 85–101. https://doi.org/10.1097/ALN.0000000000004764. 10.1097/ALN.0000000000004764 PubMedWeb of Science®Google Scholar 29Gijsberts CM, Groenewegen KA, Hoefer IE, et al. Race/ethnic differences in the associations of the Framingham risk factors with carotid intima-media thickness and cardiovascular events. PLoS One 2015; 10: e0132321. https://doi.org/10.1371/journal.pone.0132321. 10.1371/journal.pone.0132321 PubMedWeb of Science®Google Scholar 30Canto JG, Goldberg RJ, Hand MM, et al. Symptom presentation of women with acute coronary syndromes: myth vs reality. Arch Intern Med 2007; 167: 2405–2413. https://doi.org/10.1001/archinte.167.22.2405. 10.1001/archinte.167.22.2405 PubMedWeb of Science®Google Scholar 31McCarthy AM, Bristol M, Domchek SM, et al. Health care segregation, physician recommendation, and racial disparities in BRCA1/2 testing among women with breast cancer. J Clin Oncol 2016; 34: 2610–2618. https://doi.org/10.1200/JCO.2015.66.0019. 10.1200/JCO.2015.66.0019 CASPubMedWeb of Science®Google Scholar 32Halliday L, Shaw M, Kyzayeva A, Lawlor DA, Nelson SM, Kearns RJ. Socio-economic disadvantage and utilisation of labour epidural analgesia in Scotland: a population-based study. Anaesthesia 2024; 79: 473–485. https://doi.org/10.1111/anae.16236. 10.1111/anae.16236 CASPubMedWeb of Science®Google Scholar 33Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science 2019; 366: 447–453. https://doi.org/10.1126/science.aax2342. 10.1126/science.aax2342 CASPubMedWeb of Science®Google Scholar 34Collins GS, Moons KGM, Dhiman P, et al. Updated guideline for reporting clinical prediction models using regression or machine learning methods: TRIPOD+AI statement. BMJ 2024; 384: e078378. https://doi.org/10.1136/bmj-2023-078378. 10.1136/bmj?2023?078378 Google Scholar Early ViewOnline Version of Record before inclusion in an issue ReferencesRelatedInformation
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
lhy完成签到,获得积分10
刚刚
刚刚
AdulHjj完成签到 ,获得积分10
1秒前
2秒前
FF发布了新的文献求助10
2秒前
linhuafeng发布了新的文献求助10
3秒前
岑晓冰完成签到 ,获得积分10
3秒前
jy关闭了jy文献求助
3秒前
嘿ha完成签到,获得积分10
4秒前
空空完成签到,获得积分10
4秒前
4秒前
銭銭銭完成签到,获得积分20
4秒前
Joy完成签到,获得积分10
4秒前
4秒前
张巨锋完成签到,获得积分10
5秒前
Akim应助阳光元风采纳,获得10
5秒前
wyj完成签到,获得积分10
5秒前
6秒前
指东指西偏不指南的司南完成签到 ,获得积分10
6秒前
满意铁身完成签到,获得积分10
6秒前
沉迷科研完成签到,获得积分10
7秒前
康康完成签到,获得积分20
7秒前
7秒前
yu完成签到,获得积分10
8秒前
enolgoy完成签到,获得积分10
8秒前
野草发布了新的文献求助10
8秒前
8秒前
JY完成签到,获得积分20
9秒前
科研通AI2S应助追寻绮玉采纳,获得10
9秒前
梁老师的文献完成签到,获得积分10
9秒前
传奇3应助刘丰铭采纳,获得10
10秒前
tongge发布了新的文献求助10
10秒前
尊敬莺应助南华_陈采纳,获得10
10秒前
11秒前
11秒前
11秒前
ZhangChulun完成签到,获得积分10
12秒前
zz应助hck采纳,获得10
12秒前
典雅的人生完成签到,获得积分0
13秒前
凉拌苦瓜完成签到,获得积分10
13秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
AnnualResearch andConsultation Report of Panorama survey and Investment strategy onChinaIndustry 1000
卤化钙钛矿人工突触的研究 1000
Engineering for calcareous sediments : proceedings of the International Conference on Calcareous Sediments, Perth 15-18 March 1988 / edited by R.J. Jewell, D.C. Andrews 1000
Continuing Syntax 1000
Signals, Systems, and Signal Processing 610
2026 Hospital Accreditation Standards 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6264079
求助须知:如何正确求助?哪些是违规求助? 8085829
关于积分的说明 16897987
捐赠科研通 5334599
什么是DOI,文献DOI怎么找? 2839367
邀请新用户注册赠送积分活动 1816851
关于科研通互助平台的介绍 1670446