Artificial Intelligence for Inflammatory Bowel Diseases (IBD); Accurately Predicting Adverse Outcomes Using Machine Learning.

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 used LASSO and Ridge regressions, Support Vector Machines, Random Forests and Neural Networks to balance between complexity and interpretability and analyzed their relative performances and reported the strongest predictors to the respective models. The participants in our study were patients with IBD selected from The OptumLabs® Data Warehouse (OLDW), a longitudinal, real-world data asset with de-identified administrative claims and electronic health record (EHR) data. RESULTS: We included 72,178 and 69,165 patients in the training and validation set, respectively. In total, 4.1% of patients in the validation set were hospitalized, 2.9% needed IBD-related surgeries, 17% used long-term steroids and 13% of patients were initiated with biological therapy. Of the AI models we tested, the Random Forest and LASSO resulted in high accuracies (AUCs 0.70-0.92). Our artificial neural network performed similarly well in most of the models (AUCs 0.61-0.90). CONCLUSIONS: This study demonstrates feasibility of accurately predicting adverse outcomes using complex and novel AI models on large longitudinal data sets of patients with IBD. These models could be applied for risk stratification and implementation of preemptive measures to avoid adverse outcomes in a clinical setting.

Using Differential Evolution to Design Optimal Experiments.

Differential Evolution (DE) has become one of the leading metaheuristics in the class of Evolutionary Algorithms, which consists of methods that operate off of survival-of-the-fittest principles. This general purpose optimization algorithm is viewed as an improvement over Genetic Algorithms, which are widely used to find solutions to chemometric problems. Using straightforward vector operations and random draws, DE can provide fast, efficient optimization of any real, vector-valued function. This article reviews the basic algorithm and a few of its modifications with various enhancements. We provide guidance for practitioners, discuss implementation issues and give illustrative applications of DE with the corresponding R codes to find different types of optimal designs for various statistical models in chemometrics that involve the Arrhenius equation, reaction rates, concentration measures and chemical mixtures.

An Exploration Into the Use of a Chatbot for Patients With Inflammatory Bowel Diseases: Retrospective Cohort Study.

BACKGROUND: The emergence of chatbots in health care is fast approaching. Data on the feasibility of chatbots for chronic disease management are scarce. OBJECTIVE: This study aimed to explore the feasibility of utilizing natural language processing (NLP) for the categorization of electronic dialog data of patients with inflammatory bowel diseases (IBD) for use in the development of a chatbot. METHODS: Electronic dialog data collected between 2013 and 2018 from a care management platform (UCLA eIBD) at a tertiary referral center for IBD at the University of California, Los Angeles, were used. Part of the data was manually reviewed, and an algorithm for categorization was created. The algorithm categorized all relevant dialogs into a set number of categories using NLP. In addition, 3 independent physicians evaluated the appropriateness of the categorization. RESULTS: A total of 16,453 lines of dialog were collected and analyzed. We categorized 8324 messages from 424 patients into seven categories. As there was an overlap in these categories, their frequencies were measured independently as symptoms (2033/6193, 32.83%), medications (2397/6193, 38.70%), appointments (1518/6193, 24.51%), laboratory investigations (2106/6193, 34.01%), finance or insurance (447/6193, 7.22%), communications (2161/6193, 34.89%), procedures (617/6193, 9.96%), and miscellaneous (624/6193, 10.08%). Furthermore, in 95.0% (285/300) of cases, there were minor or no differences in categorization between the algorithm and the three independent physicians. CONCLUSIONS: With increased adaptation of electronic health technologies, chatbots could have great potential in interacting with patients, collecting data, and increasing efficiency. Our categorization showcases the feasibility of using NLP in large amounts of electronic dialog for the development of a chatbot algorithm. Chatbots could allow for the monitoring of patients beyond consultations and potentially empower and educate patients and improve clinical outcomes.

Patient Experiences and Outcomes of a Telehealth Clinical Care Pathway for Postoperative Inflammatory Bowel Disease Patients.

Background:Despite advancements in treatment for inflammatory bowel disease (IBD), surgery remains inevitable for patients and IBD management is costly.Introduction:Frequent postoperative monitoring is needed for early detection of both short-term complications and long-term disease recurrence. We developed a care pathway for postoperative home monitoring of IBD patients using telehealth applications.Materials and Methods:We performed a retrospective cohort study with a matched control group to assess the efficacy of the Tight Control Surgery Scenario (TCSS), a 4-week postoperative care pathway. IBD patients aged 18 or older who underwent an IBD-related intestinal operation between October 2013 and December 2015 were eligible. Enrolled participants submitted postsurgical questionnaires and wound photos through e-mail. We measured patient satisfaction with the care pathway and assessed its impact on 30-day postoperative hospital readmission rates, emergency department (ED) visits, and gastroenterologist (GI)-related office visits.Results:Sixty-four (n) cases were enrolled in TCSS and matched to 64 historic controls. Patients who completed the additional evaluation survey expressed overall satisfaction. Readmissions, 30-day ED rates, and GI visits were numerically higher in cases compared with controls, but this difference was not statistically significant.Discussion:TCSS demonstrates the feasibility of implementing a telehealth care coordination platform for postsurgery IBD management. Patients with more complications may have sent in more photos due to greater concern for maintaining their health.Conclusions:Implementation of TCSS for easy home monitoring is feasible. While we did not see reductions in ED visits, GI follow-up visits, or readmissions, patient satisfaction was high, thus demonstrating its feasibility for telehealth applications.