Data-driven decision-making for precision diagnosis of digestive diseases

Abstract

Modern omics technologies can generate massive amounts of biomedical data, providing unprecedented opportunities for individualized precision medicine. However, traditional statistical methods cannot effectively process and utilize such big data. To meet this new challenge, machine learning algorithms have been developed and applied rapidly in recent years, which are capable of reducing dimensionality, extracting features, organizing data and forming automatable data-driven clinical decision systems. Data-driven clinical decision-making have promising applications in precision medicine and has been studied in digestive diseases, including early diagnosis and screening, molecular typing, staging and stratification of digestive malignancies, as well as precise diagnosis of Crohn's disease, auxiliary diagnosis of imaging and endoscopy, differential diagnosis of cystic lesions, etiology discrimination of acute abdominal pain, stratification of upper gastrointestinal bleeding (UGIB), and real-time diagnosis of esophageal motility function, showing good application prospects. Herein, we reviewed the recent progress of data-driven clinical decision making in precision diagnosis of digestive diseases and discussed the limitations of data-driven decision making after a brief introduction of methods for data-driven decision making.

Keywords: Data-driven decision; Deep learning; Digestive diseases; Machine learning; Omics data; Precise diagnosis.

Fig. 1

Data-driven precision diagnosis for digestive diseases. PCA principal component analysis, t-SNE t-distributed stochastic neighbor embedding, KNN k-nearest neighbor algorithm, LR logistic regression, SVM support vector machine, RF random forest, XGBoost extreme gradient-boosting, CNN convolutional neural networks, RNN recurrent neural networks, GANs generative adversarial networks, DRL deep reinforcement learning, UGIB upper gastrointestinal bleeding