2022
DOI: 10.1007/s10620-022-07506-8
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Artificial Intelligence for Inflammatory Bowel Diseases (IBD); Accurately Predicting Adverse Outcomes Using Machine Learning

Abstract: Background Inflammatory Bowel Diseases with its complexity and heterogeneity could benefit from the increased application of Artificial Intelligence in clinical management. Aim To accurately predict adverse outcomes in patients with IBD using advanced computational models in a nationally representative dataset for potential use in clinical practice. Methods We built a training model cohort and validated our result in a separate cohort. We use… Show more

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Cited by 17 publications
(13 citation statements)
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“…70 In recent studies, AI models with insurance claims data were capable of accurately predicting IBD-related hospitalization and steroid use within a 6-month period in patients with IBD, while AI models with gene-based data outperformed more costly biomarker analyses for predicting outcomes. 71 Additionally, AI confirmation of investigator scoring without the need for central reading may lead to potential cost savings on drug development. Through consistent evaluation of endoscopic disease severity, AI can relieve the time burden of extensive procedure assessments while also improving quality of IBD endoscopy and patient care, a theme congruent to the information burden borne by physicians resulting from the dramatic increase in medical literature.…”
Section: Barriers To Ai Implementationmentioning
confidence: 99%
“…70 In recent studies, AI models with insurance claims data were capable of accurately predicting IBD-related hospitalization and steroid use within a 6-month period in patients with IBD, while AI models with gene-based data outperformed more costly biomarker analyses for predicting outcomes. 71 Additionally, AI confirmation of investigator scoring without the need for central reading may lead to potential cost savings on drug development. Through consistent evaluation of endoscopic disease severity, AI can relieve the time burden of extensive procedure assessments while also improving quality of IBD endoscopy and patient care, a theme congruent to the information burden borne by physicians resulting from the dramatic increase in medical literature.…”
Section: Barriers To Ai Implementationmentioning
confidence: 99%
“…To the best of our knowledge, in one study, an ML-based human trial was conducted among 70 000 patients each for the testing and validation phase, which was a highly accurate model to predict clinical outcomes, future health status, and risk score in IBD patients. 118 As the whole world is now moving toward the dimension of AI, the advantageous features of ML could be utilized in probiotics clinical research to increase the efficacy The therapeutic potential of probiotics relies on various factors, such as metabolomic fingerprinting, pharmaceutical excipients, and the viability of probiotics. AI/ML also reveals the gut microbial profile to increase the probiotic therapeutic value.…”
Section: Ai/ml Utilizes the Molecular Characteristics Of Bacteriamentioning
confidence: 99%
“…However, AI/ML-based probiotic clinical trial studies are very few and must be explored. To the best of our knowledge, in one study, an ML-based human trial was conducted among 70 000 patients each for the testing and validation phase, which was a highly accurate model to predict clinical outcomes, future health status, and risk score in IBD patients . As the whole world is now moving toward the dimension of AI, the advantageous features of ML could be utilized in probiotics clinical research to increase the efficacy and specificity of the research by reducing the efforts, human errors, and time duration.…”
Section: Ai/ml-based Precision Probiotic Selection For Advanced Healt...mentioning
confidence: 99%
“…In short, the algorithm builds many trees on randomly bootstrapped copies of the training data and then uses the aggregated predictions from all the trees as the final output. 21 The predictive performance of RF models is significantly improved when compared to DT models, but the model’s interpretability is decreased as a result of the aggregation process. Many RF-based applications have been developed and have proven to be particularly useful in assisting clinical decision-making, including postoperative outcomes of head and neck surgery.…”
Section: Algorithmsmentioning
confidence: 99%