Development and validation of a novel diagnostic model for initially clinical diagnosed gastrointestinal stromal tumors using an extreme gradient-boosting machine

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

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.

Prediction of Surgical Approach in Mitral Valve Disease by XGBoost Algorithm Based on Echocardiographic Features

In this study, we aimed to develop a prediction model to assist surgeons in choosing an appropriate surgical approach for mitral valve disease patients. We retrospectively analyzed a total of 143 patients who underwent surgery for mitral valve disease. The XGBoost algorithm was used to establish a predictive model to decide a surgical approach (mitral valve repair or replacement) based on the echocardiographic features of the mitral valve apparatus, such as leaflets, the annulus, and sub-valvular structures. The results showed that the accuracy of the predictive model was 81.09% in predicting the appropriate surgical approach based on the patient's preoperative echocardiography. The result of the predictive model was superior to the traditional complexity score (81.09% vs. 75%). Additionally, the predictive model showed that the three main factors affecting the choice of surgical approach were leaflet restriction, calcification of the leaflet, and perforation or cleft of the leaflet. We developed a novel predictive model using the XGBoost algorithm based on echocardiographic features to assist surgeons in choosing an appropriate surgical approach for patients with mitral valve disease.

Logistic Model and Gradient Boosting Machine Model for Physical Therapy of Lumbar Disc Herniation

Objective. Physical therapy is a common clinical treatment for patients with lumbar disc herniation. The study is aimed at exploring the feasibility of mathematical expression and curative effect prediction of physical therapy in patients with lumbar disc herniation using a logistic model and gradient boosting machine (GBM). Methods. A total of 142 patients with lumbar disc herniation were treated with physical therapy. The pain was evaluated by the visual analogue scale (VAS) before each treatment. The logistic model was used to conduct a global regression analysis on patients with lumbar disc herniation. The final results of the whole course of treatment were predicted by the measured values of 2-9 times of treatment. The GBM model was used to predict and analyze the curative effect of physical therapy. Results. The mathematical expression ability of the logistic regression model for patients with lumbar disc herniation undergoing physical therapy was sufficient, and the global determination coefficient was 0.721. The results would be better for more than five measurements. The AUC of GBM mode logistic regression analysis was 0.936 and 0.883, and the prediction effect is statistically significant. Conclusion. Both the logistic and GBM model can fully express the changes in patients with lumbar disc herniation during physical therapy.