A personalized prediction model for urinary tract infections in type 2 diabetes mellitus using machine learning.

Patients with type 2 diabetes mellitus (T2DM) are at higher risk for urinary tract infections (UTIs), which greatly impacts their quality of life. Developing a risk prediction model to identify high-risk patients for UTIs in those with T2DM and assisting clinical decision-making can help reduce the incidence of UTIs in T2DM patients. To construct the predictive model, potential relevant variables were first selected from the reference literature, and then data was extracted from the Hospital Information System (HIS) of the Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital for analysis. The data set was split into a training set and a test set in an 8:2 ratio. To handle the data and establish risk warning models, four imputation methods, four balancing methods, three feature screening methods, and eighteen machine learning algorithms were employed. A 10-fold cross-validation technique was applied to internally validate the training set, while the bootstrap method was used for external validation in the test set. The area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to evaluate the performance of the models. The contributions of features were interpreted using the SHapley Additive ExPlanation (SHAP) approach. And a web-based prediction platform for UTIs in T2DM was constructed by Flask framework. Finally, 106 variables were identified for analysis from a total of 119 literature sources, and 1340 patients were included in the study. After comprehensive data preprocessing, a total of 48 datasets were generated, and 864 risk warning models were constructed based on various balancing methods, feature selection techniques, and a range of machine learning algorithms. The receiver operating characteristic (ROC) curves were used to assess the performances of these models, and the best model achieved an impressive AUC of 0.9789 upon external validation. Notably, the most critical factors contributing to UTIs in T2DM patients were found to be UTIs-related inflammatory markers, medication use, mainly SGLT2 inhibitors, severity of comorbidities, blood routine indicators, as well as other factors such as length of hospital stay and estimated glomerular filtration rate (eGFR). Furthermore, the SHAP method was utilized to interpret the contribution of each feature to the model. And based on the optimal predictive model a user-friendly prediction platform for UTIs in T2DM was built to assist clinicians in making clinical decisions. The machine learning model-based prediction system developed in this study exhibited favorable predictive ability and promising clinical utility. The web-based prediction platform, combined with the professional judgment of clinicians, can assist to make better clinical decisions.

Develop an ADR prediction system of Chinese herbal injections containing Panax notoginseng saponin: a nested case-control study using machine learning.

OBJECTIVE: This study aimed to develop an adverse drug reactions (ADR) antecedent prediction system using machine learning algorithms to provide the reference for security usage of Chinese herbal injections containing Panax notoginseng saponin in clinical practice. DESIGN: A nested case-control study. SETTING: National Center for ADR Monitoring and the Electronic Medical Record (EMR) system. PARTICIPANTS: All patients were from five medical institutions in Sichuan Province from January 2010 to December 2018. MAIN OUTCOMES/MEASURES: Data of patients with ADR who used Chinese herbal injections containing Panax notoginseng saponin were collected from the National Center for ADR Monitoring. A nested case-control study was used to randomly match patients without ADR from the EMR system by the ratio of 1:4. Eighteen machine learning algorithms were applied for the development of ADR prediction models. Area under curve (AUC), accuracy, precision, recall rate and F1 value were used to evaluate the predictive performance of the model. An ADR prediction system was established by the best model selected from the 1080 models. RESULTS: A total of 530 patients from five medical institutions were included, and 1080 ADR prediction models were developed. Among these models, the AUC of the best capable one was 0.9141 and the accuracy was 0.8947. According to the best model, a prediction system, which can provide early identification of patients at risk for the ADR of Panax notoginseng saponin, has been established. CONCLUSION: The prediction system developed based on the machine learning model in this study had good predictive performance and potential clinical application.

Predictive models of medication non-adherence risks of patients with T2D based on multiple machine learning algorithms.

OBJECTIVE: Medication adherence plays a key role in type 2 diabetes (T2D) care. Identifying patients with high risks of non-compliance helps individualized management, especially for China, where medical resources are relatively insufficient. However, models with good predictive capabilities have not been studied. This study aims to assess multiple machine learning algorithms and screen out a model that can be used to predict patients' non-adherence risks. METHODS: A real-world registration study was conducted at Sichuan Provincial People's Hospital from 1 April 2018 to 30 March 2019. Data of patients with T2D on demographics, disease and treatment, diet and exercise, mental status, and treatment adherence were obtained by face-to-face questionnaires. The medication possession ratio was used to evaluate patients' medication adherence status. Fourteen machine learning algorithms were applied for modeling, including Bayesian network, Neural Net, support vector machine, and so on, and balanced sampling, data imputation, binning, and methods of feature selection were evaluated by the area under the receiver operating characteristic curve (AUC). We use two-way cross-validation to ensure the accuracy of model evaluation, and we performed a posteriori test on the sample size based on the trend of AUC as the sample size increase. RESULTS: A total of 401 patients out of 630 candidates were investigated, of which 85 were evaluated as poor adherence (21.20%). A total of 16 variables were selected as potential variables for modeling, and 300 models were built based on 30 machine learning algorithms. Among these algorithms, the AUC of the best capable one was 0.866±0.082. Imputing, oversampling and larger sample size will help improve predictive ability. CONCLUSIONS: An accurate and sensitive adherence prediction model based on real-world registration data was established after evaluating data filling, balanced sampling, and so on, which may provide a technical tool for individualized diabetes care.

Neuroprotective activity of two active chemical constituents from Tinospora hainanensis.

OBJECTIVE: To determine the chemical structure of the new compound and investigate the protective effects of Tinosporaic acid A and B towards in-vitro neuro. METHODS: The structures of two new compounds were established by analyzing its 1D and 2D NMR spectra as well as HRESIMS. Their neuroprotective effects with respect to the antioxidant properties were evaluated by radical scavenging tests and hydrogen peroxide-injured oxidative stress model in PC12 cell lines. Cell morphology of treated PC12 cells was observed by phase contrast microscopy. In-vitro MTT assay, lactate dehydrogenase activity assay and oxidative stress markers (intracellular ROS production, MDA level, and caspase-3 activity) were used to evaluate the protective effects against hydrogen peroxide induced cytotoxicity in PC12 cells. RESULTS: The two new compounds, named Tinosporaic acid A and B, were isolated and identified from the stem bark of Tinospora hainanensis. Cell viability studies identified a representative concentration for each extract that was subsequently used to measure oxidative stress markers. Both extracts were able to reverse the oxidative damage caused by hydrogen peroxide, thus promoting PC12 cells survival. The concentration of Tinosporaic acid A and B were 86.34 µg/mL and 22.06 µg/mL respectively, which is neuroprotective for EC50. The results indicated that both of them significantly attenuated hydrogen peroxide-induced neurotoxicity. CONCLUSION: The two new compounds isolated from ethanol extracts of Tinospora hainanensis are the promising natural ones with neuroprotective activity and needed for further research.