Machine Learning Algorithms for Objective Remission and Clinical Outcomes with Thiopurines

Waljee, Akbar K.; Sauder, Kay; Patel, Anand; Segar, Sandeep; Liu, Boang; Zhang, Yiwei; Zhu, Ji; Stidham, Ryan W.; Balis, Ulysses; Higgins, Peter

doi:10.1093/ecco-jcc/jjx014

Cited by 57 publications

(42 citation statements)

References 18 publications

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“…This ML model outperformed measurement of 6-TGN levels, identifying patients in remission with an AUROC curve of 0.79 vs 0.49 for the 6-TGN assay. 95 An ML model was developed to analyze data from a phase 3 trial of vedolizumab in patients with ulcerative colitis. The model predicted which patients would be in corticosteroid-free endoscopic remission at week 52 with an AUROC curve of 0.73, through week 6, vs an AUROC curve of 0.71 for level of fecal calprotectin.…”

Section: Inflammatory and Other Nonmalignant Lesionsmentioning

confidence: 99%

Application of Artificial Intelligence to Gastroenterology and Hepatology

et al. 2020

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Since 2010, substantial progress has been made in artificial intelligence (AI) and its application to medicine. AI is explored in gastroenterology for endoscopic analysis of lesions, in detection of cancer, and to facilitate the analysis of inflammatory lesions or gastrointestinal bleeding during wireless capsule endoscopy. AI is also tested to assess liver fibrosis and to differentiate patients with pancreatic cancer from those with pancreatitis. AI might also be used to establish prognoses of patients or predict their response to treatments, based on multiple factors. We review the ways in which AI may help physicians make a diagnosis or establish a prognosis and discuss its limitations, knowing that further randomized controlled studies will be required before the approval of AI techniques by the health authorities.

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Section: Inflammatory and Other Nonmalignant Lesionsmentioning

confidence: 99%

Application of Artificial Intelligence to Gastroenterology and Hepatology

et al. 2020

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“…Disease progression and outcome was a focus for 27 studies. Other considered issues were disease severity [72][73][74][75][76][77][78] in psoriasis, RA, IBD and coeliac disease; treatment response [79][80][81][82][83][84][85][86][87] in IBD, RA and primary biliary cirrhosis (PBC); and survival prediction [88][89][90] in PBC, RA and SLE. Other models focused on improved image segmentation to aid prognoses [91][92][93][94][95][96] for IBD and MS. Disease progression and outcome was the second-most prevalent area for model development.…”

Section: Disease Progression and Outcomementioning

confidence: 99%

“…Two reviewers completed screening independently, and where consensus could not be reached, a third reviewer assessed these articles and decided whether they were included or excluded. [20][21][22]26,27,31,32,[40][41][42][46][47][48] [33][34][35][36]43,57,69,73,79,[83][84][85][86] 58,87,90,194,195 55,113,196,197 autoimmune disease risk. By far the most prevalent type of data is the use of clinical and laboratory data.…”

Section: Validation and Independent Testingmentioning

confidence: 99%

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

et al. 2020

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Autoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML), a branch of the wider field of artificial intelligence, it is possible to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management. Here, we surveyed the use of ML methods to address clinical problems in autoimmune disease. A systematic review was conducted using MEDLINE, embase and computers and applied sciences complete databases. Relevant papers included "machine learning" or "artificial intelligence" and the autoimmune diseases search term(s) in their title, abstract or key words. Exclusion criteria: studies not written in English, no real human patient data included, publication prior to 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and review papers. 169 (of 702) studies met the criteria for inclusion. Support vector machines and random forests were the most popular ML methods used. ML models using data on multiple sclerosis, rheumatoid arthritis and inflammatory bowel disease were most common. A small proportion of studies (7.7% or 13/169) combined different data types in the modelling process. Cross-validation, combined with a separate testing set for more robust model evaluation occurred in 8.3% of papers (14/169). The field may benefit from adopting a best practice of validation, cross-validation and independent testing of ML models. Many models achieved good predictive results in simple scenarios (e.g. classification of cases and controls). Progression to more complex predictive models may be achievable in future through integration of multiple data types.

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“…The proposed model has an AUC of 0.85, in contrast to the conventional model with an AUC of 0.59 104. Subsequent work has shown significant clinical benefits, including decreased steroid prescriptions, hospitalisations and surgeries 105…”

Section: Current Paradigm Of Ibd Disease Management and Its Limitationsmentioning

confidence: 98%

Big data in IBD: big progress for clinical practice

Tabib

Madgwick

Sudhakar³

et al. 2020

Gut

131

108

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IBD is a complex multifactorial inflammatory disease of the gut driven by extrinsic and intrinsic factors, including host genetics, the immune system, environmental factors and the gut microbiome. Technological advancements such as next-generation sequencing, high-throughput omics data generation and molecular networks have catalysed IBD research. The advent of artificial intelligence, in particular, machine learning, and systems biology has opened the avenue for the efficient integration and interpretation of big datasets for discovering clinically translatable knowledge. In this narrative review, we discuss how big data integration and machine learning have been applied to translational IBD research. Approaches such as machine learning may enable patient stratification, prediction of disease progression and therapy responses for fine-tuning treatment options with positive impacts on cost, health and safety. We also outline the challenges and opportunities presented by machine learning and big data in clinical IBD research.

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Machine Learning Algorithms for Objective Remission and Clinical Outcomes with Thiopurines

Abstract: A machine learning algorithm was able to identify IBD patients on thiopurines with algorithm-predicted objective remission, a state associated with significant clinical benefits, including decreased steroid prescriptions, hospitalisations, and surgeries.

Cited by 57 publications

References 18 publications

Application of Artificial Intelligence to Gastroenterology and Hepatology

Application of Artificial Intelligence to Gastroenterology and Hepatology

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

Big data in IBD: big progress for clinical practice

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