AIMC Topic: Inflammatory Bowel Diseases

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Gut microbiome-based supervised machine learning for clinical diagnosis of inflammatory bowel diseases.

American journal of physiology. Gastrointestinal and liver physiology
Despite the availability of various diagnostic tests for inflammatory bowel diseases (IBD), misdiagnosis of IBD occurs frequently, and thus, there is a clinical need to further improve the diagnosis of IBD. As gut dysbiosis is reported in patients wi...

Can natural language processing help differentiate inflammatory intestinal diseases in China? Models applying random forest and convolutional neural network approaches.

BMC medical informatics and decision making
BACKGROUND: Differentiating between ulcerative colitis (UC), Crohn's disease (CD) and intestinal tuberculosis (ITB) using endoscopy is challenging. We aimed to realize automatic differential diagnosis among these diseases through machine learning alg...

Deep in the Bowel: Highly Interpretable Neural Encoder-Decoder Networks Predict Gut Metabolites from Gut Microbiome.

BMC genomics
BACKGROUND: Technological advances in next-generation sequencing (NGS) and chromatographic assays [e.g., liquid chromatography mass spectrometry (LC-MS)] have made it possible to identify thousands of microbe and metabolite species, and to measure th...

What's new in IBD therapy: An "omics network" approach.

Pharmacological research
The industrial revolution that began in the late 1800s has resulted in dramatic changes in the environment, human lifestyle, dietary habits, social structure, and so on. Almost certainly because this rapid evolution has outpaced the ability of the bo...

Big data in IBD: big progress for clinical practice.

Gut
IBD is a complex multifactorial inflammatory disease of the gut driven by extrinsic and intrinsic factors, including host genetics, the immune system, environmental factors and the gut microbiome. Technological advancements such as next-generation se...

Found In Translation: a machine learning model for mouse-to-human inference.

Nature methods
Cross-species differences form barriers to translational research that ultimately hinder the success of clinical trials, yet knowledge of species differences has yet to be systematically incorporated in the interpretation of animal models. Here we pr...

Identify and monitor clinical variation using machine intelligence: a pilot in colorectal surgery.

Journal of clinical monitoring and computing
Standardized clinical pathways are useful tool to reduce variation in clinical management and may improve quality of care. However the evidence supporting a specific clinical pathway for a patient or patient population is often imperfect limiting ado...

Phylogenetic convolutional neural networks in metagenomics.

BMC bioinformatics
BACKGROUND: Convolutional Neural Networks can be effectively used only when data are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of pixels in images. We introduce here Ph-CNN, a novel deep learning architectu...

Elevation of serum pyruvate kinase M2 (PKM2) in IBD and its relationship to IBD indices.

Clinical biochemistry
OBJECTIVES: Endoscopy remains the gold standard to diagnose and evaluate inflammatory bowel disease (IBD) activity. Current biomarkers or their combinations cannot adequately predict IBD risk, diagnosis, progression or relapse, and response to therap...