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Background Since the comprehensive implementation of the contracted family doctor service in 2016, the willingness of residents to have their first contact in primary care has gradually increased, but the effectiveness evaluation of contracted residents' first contact in primary care needs to be further explored. Objective To understand the current status of contracted residents' first contact evaluation, explore its influencing factors and propose improvement strategies. Methods A multi-stage stratified random sampling method was used to select 54 community health service institutions/township health centers in three cities in Shandong Province, with a total of 4 000 contracted residents. The first contact dimension questionnaire of the Chinese version of the Primary Care Quality Assessment Scale (PCAT) was used to conduct the survey, comparing the first contact dimension scores of contracted residents with different characteristics and using multiple linear regression to analyse the factors affecting the first contact evaluation of contracted residents. Results A total of 3 859 valid questionnaires were collected, with the effective recovery rate of 96.48%. Among them, 2 086 (54.1%) were residents of township health centers and 1 773 (45.9%) were residents of community health centers; the total score of the first contact dimension in the three regions was 3.49, with the highest score being "Is your contracted institution open on Saturdays and Sundays?" (3.74) ; the item with the lowest score was "During non-business hours, if you were sick at night, would your contracted facility have a doctor or nurse available?" (3.05). The results of the multiple linear regression analysis showed that the contracted facility, personal monthly income, education level, marital status, occupation, physical health and diagnosis of chronic disease were the factors influencing the scores of the first contact dimension of contracted residents (P

Objective: The objective of this study was to identify the risk factors that influence metastasis and prognosis in patients with nodular melanoma (NM), as well as to develop and validate a prognostic model using artificial intelligence (AI) algorithms. Methods: The Surveillance, Epidemiology, and End Results (SEER) database was queried for 4,727 patients with NM based on the inclusion/exclusion criteria. Their clinicopathological characteristics were retrospectively reviewed, and logistic regression analysis was utilized to identify risk factors for metastasis. This was followed by employing Multilayer Perceptron (MLP), Adaptive Boosting (AB), Bagging (BAG), logistic regression (LR), Gradient Boosting Machine (GBM), and eXtreme Gradient Boosting (XGB) algorithms to develop metastasis models. The performance of the six models was evaluated and compared, leading to the selection and visualization of the optimal model. Through integrating the prognostic factors of Cox regression analysis with the optimal models, the prognostic prediction model was constructed, validated, and assessed. Results: Logistic regression analyses identified that marital status, gender, primary site, surgery, radiation, chemotherapy, system management, and N stage were all independent risk factors for NM metastasis. MLP emerged as the optimal model among the six models (AUC = 0.932, F1 = 0.855, Accuracy = 0.856, Sensitivity = 0.878), and the corresponding network calculator (https://shimunana-nm-distant-m-nm-m-distant-8z8k54.streamlit.app/) was developed. The following were examined as independent prognostic factors: MLP, age, marital status, sequence number, laterality, surgery, radiation, chemotherapy, system management, T stage, and N stage. System management and surgery emerged as protective factors (HR < 1). To predict 1-, 3-, and 5-year overall survival (OS), a nomogram was created. The validation results demonstrated that the model exhibited good discrimination and consistency, as well as high clinical usefulness. Conclusion: The developed prediction model more effectively reflects the prognosis of patients with NM and differentiates between the risk level of patients, serving as a useful supplement to the classical American Joint Committee on Cancer (AJCC) staging system and offering a reference for clinically stratified individualized treatment and prognosis prediction. Furthermore, the model enables clinicians to quantify the risk of metastasis in NM patients, assess patient survival, and administer precise treatments. [ABSTRACT FROM AUTHOR]

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]

Problem: Chest radiography is a crucial tool for diagnosing thoracic disorders, but interpretation errors and a lack of qualified practitioners can cause delays in treatment. Aim: This study aimed to develop a reliable multi-classification artificial intelligence (AI) tool to improve the accuracy and efficiency of chest radiograph diagnosis. Methods: We developed a convolutional neural network (CNN) capable of distinguishing among 26 thoracic diagnoses. The model was trained and externally validated using 795,055 chest radiographs from 13 datasets across 4 countries. Results: The CNN model achieved an average area under the curve (AUC) of 0.961 across all 26 diagnoses in the testing set. COVID-19 detection achieved perfect accuracy (AUC 1.000, [95% confidence interval {CI}, 1.000 to 1.000]), while effusion or pleural effusion detection showed the lowest accuracy (AUC 0.8453, [95% CI, 0.8417 to 0.8489]). In external validation, the model demonstrated strong reproducibility and generalizability within the local dataset, achieving an AUC of 0.9634 for lung opacity detection (95% CI, 0.9423 to 0.9702). The CNN outperformed both radiologists and nonradiological physicians, particularly in trans-device image recognition. Even for diseases not specifically trained on, such as aortic dissection, the AI model showed considerable scalability and enhanced diagnostic accuracy for physicians of varying experience levels (all P < 0.05). Additionally, our model exhibited no gender bias ( P > 0.05). Conclusion: The developed AI algorithm, now available as professional web-based software, substantively improves chest radiograph interpretation. This research advances medical imaging and offers substantial diagnostic support in clinical settings., Competing Interests: Competing interests: The authors declare that they have no competing interests., (Copyright © 2024 Zeye Liu et al.)

Background: Large language model (LLM)-powered chatbots have become increasingly prevalent in healthcare, while their capacity in oncology remains largely unknown. To evaluate the performance of LLM-powered chatbots compared to oncology physicians in addressing to colorectal cancer queries., Methods: This study was conducted between August 13, 2023, and January 5, 2024. A total of 150 questions were designed, and each question was submitted three times to eight chatbots: ChatGPT-3.5, ChatGPT-4, ChatGPT-4 Turbo, Doctor GPT, Llama-2-70B, Mixtral-8x7B, Bard, and Claude 2.1. No feedback was provided to these chatbots. The questions were also answered by nine oncology physicians, including three residents, three fellows, and three attendings. Each answer was scored based on its consistency with guidelines, with a score of 1 for consistent answers and 0 for inconsistent answers. The total score for each question was based on the number of corrected answers, ranging from 0 to 3. The accuracy and scores of the chatbots were compared to those of the physicians., Results: Claude 2.1 demonstrated the highest accuracy, with an average accuracy of 82.67%, followed by Doctor GPT at 80.45%, ChatGPT-4 Turbo at 78.44%, ChatGPT-4 at 78%, Mixtral-8x7B at 73.33%, Bard at 70%, ChatGPT-3.5 at 64.89%, and Llama-2-70B at 61.78%. Claude 2.1 outperformed residents, fellows, and attendings. Doctor GPT outperformed residents and fellows. Additionally, Mixtral-8x7B outperformed residents. In terms of scores, Claude 2.1 outperformed residents and fellows. Doctor GPT, ChatGPT-4 Turbo and ChatGPT-4 outperformed residents., Conclusions: This study shows that LLM-powered chatbots can provide more accurate medical information compared to oncology physicians., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)