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Background: Both HIV and TB are chronic infectious diseases requiring long-term treatment and follow-up, resulting in extensive electronic medical records. With the exponential growth of health and medical big data, effectively extracting and analyzing these data has become the research hotspot. As a fundamental aspect of artificial intelligence, machine learning has been extensively applied in medical research, encompassing diagnosis, treatment, patient monitoring, drug development, and epidemiological investigations. This significantly enhances medical information systems and facilitates the interoperability of medical data. Methods: In our study, we analyzed longitudinal data from the electronic health records of 4540 patients, gathered from the National Clinical Research Center for Infectious Diseases in Shenzhen, China, spanning from 2017 to 2021. Initially, we employed the fine-tuned ChatGLM to structure the electronic medical records. Subsequently, we utilized a multi-layer perceptron to classify each patient and determined the presence of tuberculosis in HIV patients. Using machine learning-based natural language processing, we structured these records to build a specialized database for HIV and TB co-infection. We studied the epidemiological characteristics, focusing on incidence patterns, patient characteristics, and influencing factors, to uncover the transmission characteristics of these diseases in Shenzhen. Additionally, we used Long Short-Term Memory to create a predictive model for TB co-infection among HIV patients, based on their medical records. This model predicted the risk of TB co-infection, providing scientific evidence for clinical decision-making and enabling early detection and precise intervention. Results: Based on the refined ChatGLM model tailored for structured electronic health records, the accuracy of symptom extraction consistently surpassed 0.95 precision. Key symptoms such as diarrhea and normal showed precision rates exceeding 0.90. High scores were also achieved in recall and F1 scores. Among 4540 HIV patients, 758 were diagnosed with concurrent tuberculosis, indicating a 16.7% co-infection rate, while syphilis co-infection affected 25.1%, underscoring the prevalence of concurrent infections among HIV patients. Utilizing electronic health records, a Multilayer Perceptron classifier was developed as a benchmark against Long Short-Term Memory to predict high-risk groups for HIV and tuberculosis co-infections. The Multilayer Perceptron classifier demonstrated predictive ability with AUROC values ranging from 0.616 to 0.682 on the test set, suggesting opportunities for further optimization and generalization despite its accuracy in identifying HIV-TB co-infections. In tuberculosis intelligent diagnosis based on laboratory results, the Long Short-Term Memory showed consistent performance across 5-fold cross-validation, with AUROC values ranging from 0.827 to 0.850, indicating reliability and consistency in tuberculosis prediction. Furthermore, by optimizing classification thresholds, the model achieved an overall accuracy of 81.18% in distinguishing HIV co-infected tuberculosis from simple HIV infection. Conclusion: Combining the Multilayer Perceptron classifier with Long Short-Term Memory represented an advanced approach for effectively extracting electronic health records and utilizing it for disease prediction. This underscored the superior performance of deep learning techniques in managing both structured and unstructured medical data. Models leveraging laboratory time-series data demonstrated notably better performance compared to those relying solely on electronic health records for predicting tuberculosis incidence. This emphasized the benefits of deep learning in handling intricate medical data and provided valuable insights for healthcare providers exploring the use of deep learning in disease prediction and management. [ABSTRACT FROM AUTHOR]

Objective: Prediction of lymph node metastasis (LNM) for intrahepatic cholangiocarcinoma (ICC) is critical for the treatment regimen and prognosis. We aim to develop and validate machine learning (ML)-based predictive models for LNM in patients with ICC. Methods: A total of 345 patients with clinicopathological characteristics confirmed ICC from Jan 2007 to Jan 2019 were enrolled. The predictors of LNM were identified by the least absolute shrinkage and selection operator (LASSO) and logistic analysis. The selected variables were used for developing prediction models for LNM by six ML algorithms, including Logistic regression (LR), Gradient boosting machine (GBM), Extreme gradient boosting (XGB), Random Forest (RF), Decision tree (DT), Multilayer perceptron (MLP). We applied 10-fold cross validation as internal validation and calculated the average of the areas under the receiver operating characteristic (ROC) curve to measure the performance of all models. A feature selection approach was applied to identify importance of predictors in each model. The heat map was used to investigate the correlation of features. Finally, we established a web calculator using the best-performing model. Results: In multivariate logistic regression analysis, factors including alcoholic liver disease (ALD), smoking, boundary, diameter, and white blood cell (WBC) were identified as independent predictors for LNM in patients with ICC. In internal validation, the average values of AUC of six models ranged from 0.820 to 0.908. The XGB model was identified as the best model, the average AUC was 0.908. Finally, we established a web calculator by XGB model, which was useful for clinicians to calculate the likelihood of LNM. Conclusion: The proposed ML-based predicted models had a good performance to predict LNM of patients with ICC. XGB performed best. A web calculator based on the ML algorithm showed promise in assisting clinicians to predict LNM and developed individualized medical plans. [ABSTRACT FROM AUTHOR]

To find the most‐likely‐failure scenarios given a certain operation domain, a critical‐scenario‐based test is supposed as an effective method. However, for the state of the art, critical‐scenario‐generation approaches commonly based on random‐search and take amounts of computing resource, some of them are also inapplicable in real time. Moreover, the approaches sometimes fail to obtain critical results, which are strongly relevant to the choice of initial condition. In order to address the above challenges, the authors proposed a Real‐time Critical‐scenario‐generation framework in this paper. The authors proposed an aggressive‐driving algorithm based on the model predictive control method to lead the agent vehicle. The agent vehicle will be controlled to directly create critical scenarios for a black‐box target under test, and the real‐time critical‐scenario test can be brought into reality. A specially designed cost function is presented that guides scenarios to evolve towards the interested conditions, and a self‐adaptive coefficient iteration is designed that enables the approach to be applied within a wider range of initial conditions. The authors carried out both simulation and Vehicle‐in‐the‐Loop (VIL) test; in the VIL test, the authors' approach improves 15.45% criticality of scenarios with around 9.7 times of efficiency, or improves 38.67% criticality with still around 1.7 times of efficiency with further iterations. [ABSTRACT FROM AUTHOR]

Background: Cholangiocarcinoma (CCA) is a highly malignant and easily metastatic bile duct tumor with poor prognosis. We aimed at studying the associated risk factors affecting distal metastasis of CCA and using nomogram to guide clinicians in predicting distal metastasis of CCA. Methods: Based on inclusion and exclusion criteria, 345 patients with CCA were selected from the Fifth Medical Center of Chinese PLA General Hospital and were divided into distal metastases (N = 21) and non-distal metastases (N = 324). LASSO regression models were used to screen for relevant parameters and to compare basic clinical information between the two groups of patients. Risk factors for distal metastasis were identified based on the results of univariate and multivariate logistic regression analyses. The nomogram was established based on the results of multivariate logistic regression, and we drawn the corresponding correlation heat map. The predictive accuracy of the nomogram was evaluated by receiver operating characteristic (ROC) curves and calibration plots. The utility of the model in clinical applications was illustrated by applying decision curve analysis (DCA), and overall survival(OS) analysis was performed using the method of Kaplan-meier. Results: This study identified 4 independent risk factors for distal metastasis of CCA, including CA199, cholesterol, hypertension and margin invasion, and developed the nomogram based on this. The result of validation showed that the model had significant accuracy for diagnosis with the area under ROC (AUC) of 0.882 (95% CI: 0.843-0.914). Calibration plots and DCA showed that the model had high clinical utility. Conclusions: This study established and validated a model of nomogram for predicting distal metastasis in patients with CCA. Based on this, it could guide clinicians to make better decisions and provide more accurate prognosis and treatment for patients with CCA. [ABSTRACT FROM AUTHOR]

Objective. To establish and verify the clinical prediction model of lung metastasis in renal cancer patients. Method. Kidney cancer patients from January 1, 2010, to December 31, 2017, in the SEER database were enrolled in this study. In the first section, LASSO method was adopted to select variables. Independent influencing factors were identified after multivariate logistic regression analysis. In the second section, machine learning (ML) algorithms were implemented to establish models and 10-foldcross-validation was used to train the models. Finally, receiver operating characteristic curves, probability density functions, and clinical utility curve were applied to estimate model's performance. The final model was shown by a website calculator. Result. Lung metastasis was confirmed in 7.43% (3171 out of 42650) of study population. In multivariate logistic regression, bone metastasis, brain metastasis, grade, liver metastasis, N stage, T stage, and tumor size were independent risk factors of lung metastasis in renal cancer patients. Primary site and sequence number were independent protection factors of LM in renal cancer patients. The above 9 impact factors were used to develop the prediction models, which included random forest (RF), naive Bayes classifier (NBC), decision tree (DT), xgboost (XGB), gradient boosting machine (GBM), and logistic regression (LR). In 10-foldcross-validation, the average area under curve (AUC) ranked from 0.907 to 0.934. In ROC curve analysis, AUC ranged from 0.879–0.922. We found that the XGB model performed best, and a Web-based calculator was done according to XGB model. Conclusion. This study provided preliminary evidence that the ML algorithm can be used to predict lung metastases in patients with kidney cancer. This low cost, noninvasive and easy to implement diagnostic method is useful for clinical work. Of course this model still needs to undergo more real-world validation. [ABSTRACT FROM AUTHOR]

Background. Lung metastasis greatly affects medical therapeutic strategies in osteosarcoma. This study aimed to develop and validate a clinical prediction model to predict the risk of lung metastasis among osteosarcoma patients based on machine learning (ML) algorithms. Methods. We retrospectively collected osteosarcoma patients from the Surveillance Epidemiology and End Results (SEER) database and from four hospitals in China. Six ML algorithms, including logistic regression (LR), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), random forest (RF), decision tree (DT), and multilayer perceptron (MLP), were applied to build predictive models for predicting lung metastasis using patient's demographics, clinical characteristics, and therapeutic variables from the SEER database. The model was internally validated using 10-fold cross-validation to calculate the mean area under the curve (AUC) and the model was externally validated using the Chinese multicenter osteosarcoma data. Relative importance ranking of predictors was plotted to understand the importance of each predictor in different ML algorithms. The correlation heat map of predictors was plotted to understand the correlation of each predictor, selecting the 10-fold cross-validation with the highest AUC value in the external validation ROC curve to build a web calculator. Results. Of all enrolled patients from the SEER database, 17.73% (194/1094) developed lung metastasis. The multiple logistic regression analysis showed that sex, N stage, T stage, surgery, and bone metastasis were all independent risk factors for lung metastasis. In predicting lung metastasis, the mean AUCs of the six ML algorithms ranged from 0.711 to 0.738 in internal validation and 0.697 to 0.729 in external validation. Among the six ML algorithms, the extreme gradient boosting (XGBoost) model had the highest AUC value with an average internal AUC of 0.738 and an external AUC of 0.729. The best performing ML algorithm model was used to build a web calculator to facilitate clinicians to calculate the risk of lung metastasis for each patient. Conclusions. The XGBoost model may have the best prediction effect and the online calculator based on this model can help doctors to determine the lung metastasis risk of osteosarcoma patients and help to make individualized medical strategies. [ABSTRACT FROM AUTHOR]

Purpose. Distal metastasis in chondrosarcoma is associated with a poor prognosis. The aim of this study was to develop and validate columnar maps to predict the risk of distal metastasis in patients with chondrosarcoma, thereby contributing to clinical diagnosis and treatment. Methods. Data from chondrosarcoma patients obtained from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2016 were then screened by univariate and multifactorial logistic regressions to construct a predictive distal metastasis risk. The model discrimination of nomogram was assessed by calibration plots, while the predictive accuracy and clinical values were measured by decision curve analysis (DCA). In addition, predictive column line plots were validated in an in-house test set. Results. A total of 1,290 patients were included and randomized in a 7 to 3 ratio into a training group (n = 906) and a test group (n = 384). After logistic regression analysis, the significant variables were gender, tumor pathological grade, laterality, primary tumor stage, regional lymph node metastasis, surgical treatment, and chemotherapy. Calibration curves showed agreement between column line graph predictions and actual observations, while DCA showed the clinical utility of the nomogram. In addition, ROC showed good discrimination and calibration in the training (AUC = 0.937, 95% CI 0.919–0.952) and validation groups (AUC = 0.91, 95% CI 0.877–0.937). Conclusions. The nomogram for distal metastasis risk in patients with chondrosarcoma can effectively predict the individualized risk of distal metastasis and provide clinicians with enlightening information to optimize treatment options. [ABSTRACT FROM AUTHOR]

Background. This study aimed at establishing and validating a quantitative and visual prognosis model of Ewing Sarcoma (E.S.) via a nomogram. This model was developed to predict the risk of lung metastasis (L.M.) in patients with E.S. to provide a practical tool and help in clinical diagnosis and treatment. Methods. Data of all patients diagnosed with Ewing sarcoma between 2010 and 2016 were retrospectively retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. A training dataset from the enrolled cohorts was built (n = 929). Predictive factors for L.M. were identified based on the results of multivariable logistic regression analyses. A nomogram model and a web calculator were constructed based on those key predictors. A multicenter dataset from four medical institutions was established for model validation (n = 51). The predictive ability of the nomogram model was evaluated by the receiver operating characteristic (ROC) curve and calibration plot. Decision curve analysis (DCA) was applied to explain the accuracy of the nomogram model in clinical practice. Results. Five independent factors, including survival time, surgery, tumor (T) stage, node (N) stage, and bone metastasis, were identified to develop a nomogram model. Internal and external validation indicated significant predictive discrimination: the area under the ROC curve (AUC) value was 0.769 (95% CI: 0.740 to 0.795) in the training cohort and 0.841 (95% CI: 0.712 to 0.929) in the validation cohort, respectively. Calibration plots and DCA presented excellent performance of the nomogram model with great clinical utility. Conclusions. In this study, a nomogram model was constructed and validated to predict L.M. in patients with E.S. for medical human-computer interface—a web calculator (https://drliwenle.shinyapps.io/LMESapp/). This practical tool could help clinicians make better decisions to provide precision prognosis and treatment for patients with E.S. [ABSTRACT FROM AUTHOR]

Background. Sudden cardiac death is a leading cause of death from coronary heart disease (CHD). The risk of sudden cardiac death (SCD) increases with age, and sudden arrhythmic death remains a major cause of mortality in elderly individuals, especially ventricular arrhythmias (VA). We developed a risk prediction model by combining ECG and other clinical noninvasive indexes including biomarkers and echocardiology for VA in elderly patients with CHD. Method. In the retrospective study, a total of 2231 consecutive elderly patients (≥60 years old) with CHD hospitalized were investigated, and finally 1983 patients were enrolled as the model group. The occurrence of VA within 12 months was mainly collected. Study parameters included clinical characteristics (age, gender, height, weight, BMI, and past medical history), ECG indexes (QTcd, Tp-e/QT, and HRV indexes), biomarker indexes (NT-proBNP, Myo, cTnT, CK-MB, CRP, K+, and Ca2+), and echocardiology indexes. In the respective study, 406 elderly patients (≥60 years old) with CHD were included as the verification group to verify the model in terms of differentiation and calibration. Results. In the multiparameter model, seven independent predictors were selected: LVEF, LAV, HLP, QTcd, sex, Tp-e/QT, and age. Increased HLP, Tp-e/QT, QTcd, age, and LAV were risk factors (RR > 1), while female and increased LVEF were protective factors (RR < 1). This model can well predict the occurrence of VA in elderly patients with CHD (for model group, AUC: 0.721, 95% CI: 0.669∼0.772; for verification group, AUC: 0.73, 95% CI: 0.648∼0.818; Hosmer–Lemeshow χ 2 = 13.541, P = 0.095). After adjusting the predictors, it was found that the combination of clinical indexes and ECG indexes could predict VA more efficiently than using clinical indexes alone. Conclusions. LVEF, LAV, QTcd, Tp-e/QT, gender, age, and HLP were independent predictors of VA risk in elderly patients with CHD. Among these factors, the echocardiology indexes LVEF and LAV had the greatest influence on the predictive efficiency of the model, followed by ECG indexes, QTcd and Tp-e/QT. After verification, the model had a good degree of differentiation and calibration, which can provide a certain reference for clinical prediction of the VA occurrence in elderly patients with CHD. [ABSTRACT FROM AUTHOR]

Background: The prognosis of lung metastasis (LM) in patients with chondrosarcoma was poor. The aim of this study was to construct a prognostic nomogram to predict the risk of LM, which was imperative and helpful for clinical diagnosis and treatment.Methods: Data of all chondrosarcoma patients diagnosed between 2010 and 2016 was queried from the Surveillance, Epidemiology, and End Results (SEER) database. In this retrospective study, a total of 944 patients were enrolled and randomly splitting into training sets (n = 644) and validation cohorts(n = 280) at a ratio of 7:3. Univariate and multivariable logistic regression analyses were performed to identify the prognostic nomogram. The predictive ability of the nomogram model was assessed by calibration plots and receiver operating characteristics (ROCs) curve, while decision curve analysis (DCA) and clinical impact curve (CIC) were applied to measure predictive accuracy and clinical practice. Moreover, the nomogram was validated by the internal cohort.Results: Five independent risk factors including age, sex, marital, tumor size, and lymph node involvement were identified by univariate and multivariable logistic regression. Calibration plots indicated great discrimination power of nomogram, while DCA and CIC presented that the nomogram had great clinical utility. In addition, receiver operating characteristics (ROCs) curve provided a predictive ability in the training sets (AUC = 0.789, 95% confidence interval [CI] 0.789-0.808) and the validation cohorts (AUC = 0.796, 95% confidence interval [CI] 0.744-0.841).Conclusion: In our study, the nomogram accurately predicted risk factors of LM in patients with chondrosarcoma, which may guide surgeons and oncologists to optimize individual treatment and make a better clinical decisions.Trial Registration: JOSR-D-20-02045, 29 Dec 2020. [ABSTRACT FROM AUTHOR]

Purpose. Since the prognosis of renal cell carcinoma (RCC) patients with bone metastasis (BM) is poor, this study is aimed at using big data to build a machine learning (ML) model to predict the risk of BM in RCC patients. Methods. A retrospective study was conducted on 40,355 RCC patients in the SEER database from 2010 to 2017. LASSO regression and multivariate logistic regression analysis was performed to determine independent risk factors of RCC-BM. Six ML algorithm models, including LR, GBM, XGB, RF, DT, and NBC, were used to establish risk models for predicting RCC-BM. The prediction performance of ML models was weighed by 10-fold cross-validation. Results. The study investigated 40,355 patients diagnosed with RCC in the SEER database, where 1,811 (4.5%) were BM patients. Independent risk factors for BM were tumor grade, T stage, N stage, liver metastasis, lung metastasis, and brain metastasis. Among the RCC-BM risk prediction models established by six ML algorithms, the XGB model showed the best prediction performance (AUC = 0.891). Therefore, a network calculator based on the XGB model was established to individually assess the risk of BM in patients with RCC. Conclusion. The XGB risk prediction model based on the ML algorithm performed a good prediction effect on BM in RCC patients. [ABSTRACT FROM AUTHOR]