. Author manuscript; available in PMC: 2020 Aug 30.

Published in final edited form as: Dig Dis Interv. 2020 Mar;4(1):73–81. doi: 10.1055/s-0040-1705097

Table 3.

Machine learning applied to the treatment of gastrointestinal diseases

Authors	Summary	Primary data type	No. of patients (no. of samples^a)	Model type(s)	Best model performance
Abajian et al²⁴	Predicting treatment response of patients with HCC	Imaging	36	RF, LR	Accuracy = 78% Sensitivity = 62.5% Specificity=82.1%
Morilla et al²⁵	Predicting treatment response of patients with acute severe ulcerative colitis	Genetic, clinical	76	NN	Accuracy = 93% AUC = 0.91
Jin et al²⁶	Prediction of response after chemoradiation for esophageal cancer	Imaging	94	SVM, XGBoost	Accuracy = 0.708 AUC = 0.541
Riyahi et al²¹	Prediction of pathologic tumor response to chemoradiotherapy in esophageal cancer	Imaging	20	SVM-Lasso model	Sensitivity = 94.4% Specificity = 91.8% AUC = 0.94

Abbreviations: HCC, hepatocellular carcinoma; LR, logistic regression; RF, random forest; SVM, support vector machine; NN, neural network.

The number of samples derived from patients, not the number of unique patients participating in the study.