Application of Intellectual Monitoring Information Technology in Determining the Severity of the Condition of Patients With Inflammatory Bowel Diseases

Abstract

The aim: Was to evaluate the effectiveness of the use of information technology of intelligent monitoring in solving the problems of assessing the morbidity of a patient with IBD during treatment. Matherials and methods: 183 patients with IBD were observed. Among them 104(56.8%) patients suffered from Crohn’s disease and 79(43.1%) patients had ulcerative colitis. For each patient and each disease, the formation of a list of signs, the extraction of information and knowledge will be carried out according to an individual method. At the lower level, tasks are performed: determination of the list of patient morbidity conditions, the formation of a list of indicators of the patient morbidity conditions, their identification as classes for machine learning models; formation of a list of signs, which identify the state of the patient’s morbidity and whose characteristics obtained after results of medical tests. Results: The number of correctly classified points reached 92%. An analysis of the conditions of patients characterized by incorrectly classified points revealed the information content of this fact. In those cases when the classification results did not coincide with the expert assessment of the patient’s condition, additional factors were found that influenced his condition and whose characteristics were not taken into account in the structure of classifier models. Conclusions: The results of the testing of classifier models indicate the effectiveness of the use of information technology of intelligent monitoring to assess the condition of patients with IBD.

A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation

“…Three main clinical application areas were identified: diagnosis (23%), 15 , 20–36 disease course (28%), 15 , 30 , 37–56 and disease severity (21%). 19 , 57–71 Diagnosis classification tasks involved differentiating IBD patients (or one subtype) from controls. Studies of disease course examined relapse, remission, and surgery ML classifiers.…”

“… 23 , 29 , 45 , 59 , 61 , 66 , 73 Crohn’s disease data (only) was used in 27 studies, 12 , 17 , 19 , 26–29 , 32 , 37–39 , 41 , 44 , 47–50 , 58 , 60 , 63 , 65 , 74–79 and UC data (only) was used in 15 studies, 25 , 40 , 42 , 46 , 52 , 55 , 59 , 61 , 62 , 64 , 68–70 , 80 , 81 with the remainder ( n = 36) using a mix of CD and UC data, or IBD data as one class. 11 , 13–16 , 18 , 20–24 , 30 , 31 , 33–36 , 43 , 45 , 51 , 53 , 54 , 56 , 57 , 66 , 67 , 71–73 , 82–88 Half of the research using UC-only data focused on predicting disease activity with endoscopy data, whereas the aims of ML classifications on CD data were varied. A breakdown of the method and classification task can be found in Figure 3 , which can be customized here (isstafford.github.io/review_ml_ibd_2021/).…”