Show simple item record

Clinical applications of artificial intelligence and machine learning- based methods in inflammatory bowel disease

dc.contributor.authorCohen‐mekelburg, Shirley
dc.contributor.authorBerry, Sameer
dc.contributor.authorStidham, Ryan W
dc.contributor.authorZhu, Ji
dc.contributor.authorWaljee, Akbar K
dc.date.accessioned2021-03-02T21:42:00Z
dc.date.available2022-03-02 16:41:59en
dc.date.available2021-03-02T21:42:00Z
dc.date.issued2021-02
dc.identifier.citationCohen‐mekelburg, Shirley ; Berry, Sameer; Stidham, Ryan W; Zhu, Ji; Waljee, Akbar K (2021). "Clinical applications of artificial intelligence and machine learning- based methods in inflammatory bowel disease." Journal of Gastroenterology and Hepatology 36(2): 279-285.
dc.identifier.issn0815-9319
dc.identifier.issn1440-1746
dc.identifier.urihttps://hdl.handle.net/2027.42/166331
dc.description.abstractOur objective was to review and exemplify how selected applications of artificial intelligence (AI) might facilitate and improve inflammatory bowel disease (IBD) care and to identify gaps for future work in this field. IBD is highly complex and associated with significant variation in care and outcomes. The application of AI to IBD has the potential to reduce variation in healthcare delivery and improve quality of care. AI refers to the ability of machines to mimic human intelligence. The range of AI’s ability to perform tasks that would normally require human intelligence varies from prediction to complex decision- making that more closely resembles human thought. Clinical applications of AI have been applied to study pathogenesis, diagnosis, and patient prognosis in IBD. Despite these advancements, AI in IBD is in its early development and has tremendous potential to transform future care.
dc.publisherWiley Periodicals, Inc.
dc.subject.otherGastroenterology, IBD: clinical trials
dc.subject.otherGastroenterology
dc.subject.otherGastroenterology, screening and diagnosis
dc.subject.otherGastroenterology, IBD: pre- clinical treatment and novel therapies
dc.subject.otherGastroenterology, IBD: genetics
dc.titleClinical applications of artificial intelligence and machine learning- based methods in inflammatory bowel disease
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelInternal Medicine and Specialties
dc.subject.hlbtoplevelHealth Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/166331/1/jgh15405_am.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/166331/2/jgh15405.pdf
dc.identifier.doi10.1111/jgh.15405
dc.identifier.sourceJournal of Gastroenterology and Hepatology
dc.identifier.citedreferenceGoldstein A, Kapelner A, Bleich J, Pitkin E. Peeking inside the black box: visualizing statistical learning with plots of individual conditional expectation. J. Comput. Graph. Stat. 2015; 24: 44 - 65.
dc.identifier.citedreferenceStidham RW, Enchakalody B, Waljee AK et al. Assessing small bowel stricturing and morphology in crohn’s disease using semi- automated image analysis. Inflamm. Bowel Dis. 2020; 26: 734 - 742.
dc.identifier.citedreferenceWang R, Cai Y, Lee IK et al. Evaluation of a convolutional neural network for ovarian tumor differentiation based on magnetic resonance imaging. Eur. Radiol. 2020: 1 - 12. https://doi.org/10.1007/s00330-020-07266-x
dc.identifier.citedreferenceWaljee AK, Joyce JC, Wang S et al. Algorithms outperform metabolite tests in predicting response of patients with inflammatory bowel disease to thiopurines. Clin. Gastroenterol. Hepatol. 2010; 8: 143 - 150.
dc.identifier.citedreferenceWaljee AK, Sauder K, Zhang Y, Zhu J, Higgins PDR. External validation of a thiopurine monitoring algorithm on the SONIC clinical trial dataset. Clin. Gastroenterol. Hepatol. 2018; 16: 449 - 451.
dc.identifier.citedreferenceWaljee AK, Liu B, Sauder K et al. Predicting corticosteroid- free endoscopic remission with vedolizumab in ulcerative colitis. Aliment. Pharmacol. Ther. 2018; 47: 763 - 772.
dc.identifier.citedreferenceWaljee AK, Wallace BI, Cohen- Mekelburg S et al. Development and validation of machine learning models in prediction of remission in patients with moderate to severe Crohn disease. JAMA Netw. Open 2019; 2: e193721.
dc.identifier.citedreferenceWaljee AK, Lipson R, Wiitala WL et al. Predicting hospitalization and outpatient corticosteroid use in inflammatory bowel disease patients using machine learning. Inflamm. Bowel Dis. 2018; 24: 45 - 53.
dc.identifier.citedreferenceCushing KC, McLean R, McDonald KG et al. Predicting risk of postoperative disease recurrence in Crohn’s disease: patients with indolent Crohn’s disease have distinct whole transcriptome profiles at the time of first surgery. Inflamm. Bowel Dis. 2019; 25: 180 - 193.
dc.identifier.citedreferenceHastie T, Tibshirani R, Friedman J. Random forests. In 2009. p. 587 - 604.
dc.identifier.citedreferenceFriedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat. 2001; 29: 1189 - 1232.
dc.identifier.citedreferenceCortez P, Embrechts MJ. Using sensitivity analysis and visualization techniques to open black box data mining models. Inf. Sci. (Ny). 2013; 225: 1 - 17.
dc.identifier.citedreferenceLundberg SM, Erion G, Chen H et al. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2020; 2: 56 - 67.
dc.identifier.citedreferenceLundberg SM, Nair B, Vavilala MS et al. Explainable machine- learning predictions for the prevention of hypoxaemia during surgery. Nat. Biomed. Eng. 2018; 2: 749 - 760.
dc.identifier.citedreferenceLundberg SM, Lee SI. A unified approach to interpreting model predictions. In: Advances in neural information processing systems. Neural information processing systems foundation; 2017. p. 4766 - 75.
dc.identifier.citedreferenceSingal AG, Higgins PDR, Waljee AK. A primer on effectiveness and efficacy trials. Clin. Transl. Gastroenterol. 2014; 5: e45.
dc.identifier.citedreferenceAnanthakrishnan AN, Cai T, Savova G et al. Improving case definition of Crohn’s disease and ulcerative colitis in electronic medical records using natural language processing: a novel informatics approach. Inflamm. Bowel Dis. 2013; 19: 1411 - 1420.
dc.identifier.citedreferenceZand A, Sharma A, Stokes Z et al. An exploration into the use of a chatbot for patients with inflammatory bowel diseases: retrospective cohort study. J. Med. Internet Res. 2020; 22: e15589.
dc.identifier.citedreferenceZhou L, Gao J, Li D, Shum H- Y. The design and implementation of XiaoIce, an empathetic social chatbot. Comput. Linguist. 2018; 46: 53 - 93.
dc.identifier.citedreferenceUnsupervised Deep Learning for Vertical Conversational Chatbots|by VC Ramesh|Chatbots Magazine [Internet].
dc.identifier.citedreferenceThe 7 Steps of Machine Learning. From detecting skin cancer, to sorting - ¦|by Yufeng G|Towards Data Science [Internet].
dc.identifier.citedreferenceHuang J, Zhou M, Yang D. Extracting chatbot knowledge from online discussion forums [Internet].
dc.identifier.citedreferenceLiu X, Rivera SC, Moher D, Calvert MJ, Denniston AK. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT- AI extension. The BMJ 2020; 370: m3210.
dc.identifier.citedreferenceCruz Rivera S, Liu X, Chan AW et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT- AI extension. Nat. Med. 2020; 26: 1351 - 1363.
dc.identifier.citedreferenceOlivera P, Danese S, Jay N, Natoli G, Peyrin- Biroulet L. Big data in IBD: a look into the future. Nat. Rev. Gastroenterol. Hepatol. 2019; 16: 312 - 321.
dc.identifier.citedreferenceAlatab S, Sepanlou SG, Ikuta K et al. The global, regional, and national burden of inflammatory bowel disease in 195 countries and territories, 1990- 2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Gastroenterol. Hepatol. 2020; 5: 17 - 30.
dc.identifier.citedreferenceMehta F. Report: economic implications of inflammatory bowel disease and its management. Am. J. Manag. Care 2016; 22: s51 - s60.
dc.identifier.citedreferenceBerry SK, Siegel CA, Melmed GY. Quality improvement initiatives in inflammatory bowel disease. Curr. Gastroenterol. Rep. 2017 Aug; 19: 1 - 5.
dc.identifier.citedreferenceFjelland R. Why general artificial intelligence will not be realized. Humanit Soc. Sci. Commun. 2020; 7: 1 - 9.
dc.identifier.citedreferenceRajkomar A, Dean J, Kohane I. Machine learning in medicine. N. Engl. J. Med. 2019; 380: 1347 - 1358.
dc.identifier.citedreferenceGanguli I, Gordon WJ, Lupo C et al. Machine Learning and the pursuit of high- value health care. NEJM Catalyst Innovations in Care Delivery 2020; 1.
dc.identifier.citedreferenceWaljee AK, Sauder K, Patel A et al. Machine learning algorithms for objective remission and clinical outcomes with thiopurines. J. Crohns Colitis 2017; 11: 801 - 810.
dc.identifier.citedreferenceBiasci D, Lee JC, Noor NM et al. A blood- based prognostic biomarker in IBD. Gut 2019; 68: 1386 - 1395.
dc.identifier.citedreferenceBottigliengo D, Berchialla P, Lanera C et al. The role of genetic factors in characterizing extra- intestinal manifestations in Crohn’s disease patients: are Bayesian machine learning methods improving outcome predictions? J. Clin. Med. 2019; 8: 865.
dc.identifier.citedreferenceStidham RW, Liu W, Bishu S et al. Performance of a deep learning model vs human reviewers in grading endoscopic disease severity of patients with ulcerative colitis. JAMA Netw. Open 2019; 2: e193963.
dc.identifier.citedreferenceIsakov O, Dotan I, Ben- Shachar S. Machine learning- based gene prioritization identifies novel candidate risk genes for inflammatory bowel disease. Inflamm. Bowel Dis. 2017; 23: 1516 - 1523.
dc.identifier.citedreferenceRomagnoni A, Jégou S, Van Steen K et al. Comparative performances of machine learning methods for classifying Crohn’s disease patients using genome- wide genotyping data. Sci. Rep. 2019; 9: 1 - 18.
dc.identifier.citedreferenceHirasawa T, Aoyama K, Tanimoto T et al. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer 2018; 21: 653 - 660.
dc.identifier.citedreferenceTakenaka K, Ohtsuka K, Fujii T et al. Development and validation of a deep neural network for accurate evaluation of endoscopic images from patients With ulcerative colitis. Gastroenterology 2020; 158: 2150 - 2157.
dc.identifier.citedreferenceBossuyt P, Vermeire S, Bisschops R. Scoring endoscopic disease activity in IBD: artificial intelligence sees more and better than we do. Gut 2020; 69: 788 - 789.
dc.identifier.citedreferenceYao H, Najarian K, Gryak J et al. Fully automated endoscopic disease activity assessment in ulcerative colitis. Gastrointest. Endosc. 2020; 0.
dc.working.doiNOen
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.