• disease flares
• inflammatory bowel disease
• lifestyle factors
• nutrition
• personalized treatment
• predictive models
• psychological health
• risk factors
• sleep quality

• crohn's disease
• prediction of relapse
• time-aggregated study
• machine learning

• Humans
• Crohn Disease/diagnosis
• Crohn Disease/blood
• Male
• Retrospective Studies
• Female
• Machine Learning
• Recurrence
• Child
• Adolescent
• Time Factors
• ROC Curve
• C-Reactive Protein/analysis
• Severity of Illness Index
• Prognosis
• Predictive Value of Tests

• Ministry of Health and Welfare

• AQP9
• Acute myocardial infarction
• Diagnostic gene identification
• Immune cell correlation
• Machine learning
• SOCS3

• Humans
• Leukocytes
• Myocardial Infarction/genetics
• Monocytes
• Coronary Artery Disease/genetics
• Machine Learning
• RNA

• Suzhou Science & Technology Development Plan
• Zhangjiagang science and technology plan project
• Youth science and technology project of Zhangjiagang Municipal Health Commission

• artificial intelligence
• complex systems
• digital health
• variability

• Humans
• Female
• Middle Aged
• Male
• COVID-19/epidemiology
• COVID-19/prevention & control
• COVID-19/complications
• Inflammatory Bowel Diseases/epidemiology
• Crohn Disease/complications
• Colitis, Ulcerative/complications
• United Kingdom/epidemiology

• Department of Health

• FCN3
• FRZB
• SERPINA3
• diagnosis value
• dilated cardiomyopathy
• machine learning

Ulcerative colitis (UC) is a chronic relapsing inflammatory bowel disease with an increasing incidence and prevalence worldwide. The diagnosis for UC mainly relies on clinical symptoms and laboratory examinations. As some previous studies have revealed that there is an association between gene expression signature and disease severity, we thereby aim to assess whether genes can help to diagnose UC and predict its correlation with immune regulation. A total of ten eligible microarrays (including 387 UC patients and 139 healthy subjects) were included in this study, specifically with six microarrays (GSE48634, GSE6731, GSE114527, GSE13367, GSE36807, and GSE3629) in the training group and four microarrays (GSE53306, GSE87473, GSE74265, and GSE96665) in the testing group. After the data processing, we found 87 differently expressed genes. Furthermore, a total of six machine learning methods, including support vector machine, least absolute shrinkage and selection operator, random forest, gradient boosting machine, principal component analysis, and neural network were adopted to identify potentially useful genes. The synthetic minority oversampling (SMOTE) was used to adjust the imbalanced sample size for two groups (if any). Consequently, six genes were selected for model establishment. According to the receiver operating characteristic, two genes of OLFM4 and C4BPB were finally identified. The average values of area under curve for these two genes are higher than 0.8, either in the original datasets or SMOTE-adjusted datasets. Besides, these two genes also significantly correlated to six immune cells, namely Macrophages M1, Macrophages M2, Mast cells activated, Mast cells resting, Monocytes, and NK cells activated (P  <  0.05). OLFM4 and C4BPB may be conducive to identifying patients with UC. Further verification studies could be conducted.

• Air pollution
• Frozen embyro transfer
• In vitro fertilization
• Random forest

• Air Pollutants/analysis
• Air Pollution/adverse effects
• Air Pollution/analysis
• China
• Embryo Transfer/methods
• Female
• Humans
• Machine Learning
• Nitrogen Dioxide
• Particulate Matter/toxicity
• Pregnancy
• Retrospective Studies

• artificial intelligence
• deep learning
• experimental diabetes mellitus
• gastric emptying
• orocaecal transit

• Animals
• Cecum
• Diabetes Mellitus, Experimental
• Gastric Emptying
• Gastrointestinal Transit
• Neural Networks, Computer
• Rats

• Artificial intelligence
• Artificial neural networks
• Gastroenterology
• Hepatology
• Machine learning
• Support vector machine

• Artificial Intelligence
• Gastroenterology
• Humans
• Liver Diseases
• Machine Learning
• Prognosis

• Inflammatory bowel disease
• artificial intelligence
• machine learning
• neural networks

• Crohn’s disease
• Machine learning
• big data
• prediction
• ulcerative colitis

• artificial intelligence
• inflammatory bowel diseases
• machine learning
• predictive modeling

Inflammatory bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn’s disease (CD), is an idiopathic condition related to a dysregulated immune response to commensal intestinal microflora in a genetically susceptible host. As a global disease, the morbidity of IBD reached a rate of 84.3 per 100,000 persons and reflected a continued gradual upward trajectory. The medical cost of IBD is also notably extremely high. For example, in Europe, it has €3,500 in CD and €2,000 in UC per patient per year, respectively. In addition, taking into account the work productivity loss and the reduced quality of life, the indirect costs are incalculable. In modern times, the diagnosis of IBD is still a subjective judgment based on laboratory tests and medical images. Its early diagnosis and intervention is therefore a challenging goal and also the key to control its progression. Artificial intelligence (AI)-assisted diagnosis and prognosis prediction has proven effective in many fields including gastroenterology. In this study, support vector machines were utilized to distinguish the significant features in IBD. As a result, the reliability of IBD diagnosis due to its impressive performance in classifying and addressing region problems was improved. Convolutional neural networks are advanced image processing algorithms that are currently in existence. Digestive endoscopic images can therefore be better understood by automatically detecting and classifying lesions. This study aims to summarize AI application in the area of IBD, objectively evaluate the performance of these methods, and ultimately understand the algorithm–dataset combination in the studies.

• Crohn’s disease
• artificial intelligence
• inflammatory bowel disease
• machine learning
• ulcerative colitis

Driven by the tremendous availability of data, artificial intelligence (AI) using deep learning has emerged as a breakthrough computer technology in the last few decades and has recently been acknowledged by the Task Force on AI as a golden opportunity for research. With its ability to understand, learn from and build on non-linear relationships, AI aims to individualize medical care in an attempt to save time, cost, effort and improve patient’s safety. AI has been applied in multiple medical fields with substantial progress made in gastroenterology mainly to facilitate accurate detection of pathology in different disease processes, among which inflammatory bowel disease (IBD) seems to drag significant attention, specifically by interpreting imaging studies, endoscopic images and videos and -to a lesser extent- disease genomics. Moreover, models have been built to predict IBD occurrence, flare ups, persistence of histological inflammation, disease-related structural abnormalities as well as disease remission. In this article, we will review the applications of AI in IBD in the present medical literature at multiple points of IBD timeline, starting from disease prediction via genomic assessment, diagnostic phase via interpretation of radiological studies and AI-assisted endoscopy, and the role of AI in the evaluation of therapy response and prognosis of IBD patients.

• Artificial intelligence
• Machine learning
• Inflammatory bowel disease

• Artificial neural network
• Diagnosis
• Endoscopy
• Gastrointestinal disease
• Prognosis
• Treatment

• Artificial Intelligence
• Gastroenterologists
• Humans
• Neural Networks, Computer
• Prognosis

• Crohn’s disease
• seasonal variation
• seasonality
• surgery
• treatment
• ulcerative colitis

Ulcerative colitis is a type of inflammatory bowel disease characterized by chronic and recurrent nonspecific inflammation of the intestinal tract. To find susceptibility genes and develop a novel predictive model of ulcerative colitis, two sets of cases and a control group containing the ulcerative colitis gene expression profile (training set GSE109142 and validation set GSE92415) were downloaded and used to identify differentially expressed genes. A total of 781 upregulated and 127 downregulated differentially expressed genes were identified in GSE109142. The random forest algorithm was introduced to determine 1 downregulated and 29 upregulated differentially expressed genes contributing highest to ulcerative colitis occurrence. Expression data of these 30 genes were transformed into gene expression scores, and an artificial neural network model was developed to calculate differentially expressed genes weights to ulcerative colitis. We established a universal molecular prognostic score (mPS) based on the expression data of the 30 genes and verified the mPS system with GSE92415. Prediction results agreed with that of an independent data set (ROC-AUC=0.9506/PR-AUC=0.9747). Our research creates a reliable predictive model for the diagnosis of ulcerative colitis, and provides an alternative marker panel for further research in disease early screening

• artificial neural network
• predictive model
• random forest
• susceptibility genes
• ulcerative colitis

• artificial intelligence
• digital medicine
• medical technologies
• mobile health
• monitoring

• Deep Learning
• Digestive System
• Machine Learning
• Neural Network

• Crohn’s disease
• azathioprine
• back-propagation neural network
• machine learning
• maintenance therapy
• medication adherence
• support vector machine

Forecasting the seasonality and trend of pulmonary tuberculosis is important for the rational allocation of health resources; however, this foresting is often hampered by inappropriate prediction methods. In this study, we performed validation research by comparing the accuracy of the autoregressive integrated moving average (ARIMA) model and the back-propagation neural network (BPNN) model in a southeastern province of China.

We applied the data from 462,214 notified pulmonary tuberculosis cases registered from January 2005 to December 2015 in Jiangsu Province to modulate and construct the ARIMA and BPNN models. Cases registered in 2016 were used to assess the prediction accuracy of the models. The root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and mean error rate (MER) were used to evaluate the model fitting and forecasting effect.

During 2005–2015, the annual pulmonary tuberculosis notification rate in Jiangsu Province was 56.35/100,000, ranging from 40.85/100,000 to 79.36/100,000. Through screening and comparison, the ARIMA (0, 1, 2) (0, 1, 1)₁₂ and BPNN (3-9-1) were defined as the optimal fitting models. In the fitting dataset, the RMSE, MAPE, MAE and MER were 0.3901, 6.0498, 0.2740 and 0.0608, respectively, for the ARIMA (0, 1, 2) (0, 1, 1)₁₂ model, 0.3236, 6.0113, 0.2508 and 0.0587, respectively, for the BPNN model. In the forecasting dataset, the RMSE, MAPE, MAE and MER were 0.1758, 4.6041, 0.1368 and 0.0444, respectively, for the ARIMA (0, 1, 2) (0, 1, 1)₁₂ model, and 0.1382, 3.2172, 0.1018 and 0.0330, respectively, for the BPNN model.

Both the ARIMA and BPNN models can be used to predict the seasonality and trend of pulmonary tuberculosis in the Chinese population, but the BPNN model shows better performance. Applying statistical techniques by considering local characteristics may enable more accurate mathematical modeling.

• ARIMA
• BPNN
• forecasting
• incidence
• tuberculosis

• Artificial intelligence
• Computer-assisted
• Convolutional neural network
• Deep-learning
• Endoscopy
• Gastroenterology

• Big Data
• Datasets as Topic
• Deep Learning
• Endoscopy, Gastrointestinal/methods
• Gastroenterology/methods
• Gastrointestinal Diseases/diagnosis
• Gastrointestinal Diseases/therapy
• Gastrointestinal Tract/diagnostic imaging
• Humans
• Image Processing, Computer-Assisted/methods
• Prognosis
• Ultrasonography/methods

• Climate change
• Cold spell
• Crohn disease
• Inflammatory bowel disease
• Ulcerative colitis