Predicting inpatient mortality in patients with inflammatory bowel disease: A machine learning approach

Charilaou, Paris; Mohapatra, Sonmoon; Doukas, Sotirios G.; Kohli, Maanit; Radadiya, Dhruvil; Devani, Kalpit; Broder, Arkady; Elemento, Olivier; Lukin, Dana J.; Battat, Robert

doi:10.1111/jgh.16029

Cited by 5 publications

(1 citation statement)

References 22 publications

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“…In terms of treatment and prediction, Charilaou et al [39] used traditional logistic regression (cLR) as a reference model and compared it with more complex ML models. They constructed multiple IM prediction models and surgical queue models, converting the best-performing QLattice model (symbolic regression equation) into a network-based calculator (IM-IBD calculator), which achieved good validation in stratifying the risk of inpatient mortality and predicting surgical sub-queues.…”

Section: Ibdmentioning

confidence: 99%

Advancements in AI Research for Digestive System Diseases

Feng,

Li,

Tan

et al. 2023

JBM

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With the "boom" of AI, researchers have made significant progress in assisting clinical disease diagnosis, prediction, and treatment. This article provides an overview of models built using both traditional machine learning methods and deep learning methods, as well as research progress on robotics in digestive system diseases, aiming to provide references for further studies. An application has been developed by domestic and foreign scholars that allows users to upload images of stool samples, which are then analyzed using big data to provide a score for bowel preparation, thereby improving the quality of bowel preparation. In some gastrointestinal diseases, such as Hp infection, Barrett's esophagus and esophageal cancer, chronic atrophic gastritis and gastric cancer, IBD, etc., artificial intelligence possesses diagnostic capabilities comparable to those of professional endoscopists, and some applications can achieve real-time diagnosis. In the field of liver, gallbladder, and pancreatic diseases, artificial intelligence can assist in preoperative diagnosis using imaging or pathology, and robotic remote operations can be performed during surgery, predicting postoperative risk levels, and more. Different scholars have compared and analyzed various algorithm networks for different diseases to find the best-performing models. On this basis, methods such as the MCA attention mechanism, feature selection, gradient descent, and ensemble models can be introduced to further improve the diagnostic performance of the models. In the future, AI can not only help patients self-manage single or multiple diseases, monitor and manage their own diseases in a standardized and reasonable manner, but also predict and treat digestive system diseases at the genetic level.

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Section: Ibdmentioning

confidence: 99%

Advancements in AI Research for Digestive System Diseases

Feng,

Li,

Tan

et al. 2023

JBM

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Enhancing the Accuracy for Predicting Nutritious Food Recommendations for Patients using Ensemble Random Forest over Artificial Neural Network

Akilandeswari

2024

2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies

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Meta‐Analysis: Prevalence of Frailty and Associated Adverse Events in Inflammatory Bowel Diseases

Carbery,

Todd,

Hale

et al. 2024

Aliment Pharmacol Ther

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BackgroundThe number of adults aged over 60 years with inflammatory bowel disease (IBD) is increasing. Frailty, rather than chronological age, may be a better predictor of adverse health outcomes.AimsTo summarise current knowledge about frailty in adults with IBD including the prevalence and associations of frailty and IBD‐related adverse outcomes.MethodsWe performed an electronic search of MEDLINE, EMBASE and EMBASE Classic databases using search terms for IBD and frailty from inception to 14 February 2024. All studies involving adults aged ≥ 16 with a confirmed diagnosis of IBD that included a frailty assessment were eligible for inclusion.ResultsWe included 23 observational studies involving 1,893,448 adults. Risk of bias was low for 18 studies and moderate for five. Twelve methods of frailty assessment were used, the most common being the Hospital Frailty Risk Score. Pooled prevalence of frailty in IBD patients was 18% (95% confidence interval (CI) 12.4%–25.6%). Meta‐analysis of unadjusted events data demonstrated that frailty increased the risk of infection‐related admissions following treatment in two studies (relative risk (RR) 1.9; 95% CI 1.2–3.0), post‐operative morbidity in three (RR 2.0; 95% CI 1.4–2.7) and mortality in seven (RR 4.3; 95% CI 2.6–7.4).ConclusionsFrailty is common in patients with IBD and is associated with IBD‐related adverse outcomes including infection‐related admissions following treatment, post‐operative morbidity and death. Future work should focus on developing risk assessment tools to better support decision making for older people with frailty and IBD.

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Predicting inpatient mortality in patients with inflammatory bowel disease: A machine learning approach

Cited by 5 publications

References 22 publications

Advancements in AI Research for Digestive System Diseases

Advancements in AI Research for Digestive System Diseases

Enhancing the Accuracy for Predicting Nutritious Food Recommendations for Patients using Ensemble Random Forest over Artificial Neural Network

Meta‐Analysis: Prevalence of Frailty and Associated Adverse Events in Inflammatory Bowel Diseases

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