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Although new technologies have increased the efficiency and convenience of medical care, patients still struggle to identify specialized outpatient departments in Chinese tertiary hospitals due to a lack of medical knowledge. The objective of our study was to develop a precise and subdividable outpatient triage system to improve the experiences and convenience of patient care. We collected 395,790 electronic medical records (EMRs) and 500 medical dialogue groups. The EMRs were divided into 3 data sets to design and train the triage model (n=387,876, 98%) and test (n=3957, 1%) and validate (n=3957, 1%) it. The triage system was altered based on the current BERT (Bidirectional Encoder Representations from Transformers) framework and evaluated by recommendation accuracies in Xinhua Hospital using the cancellation rates in 2021 and 2022, from October 29 to December 5. Finally, a prospective observational study containing 306 samples was conducted to compare the system's performance with that of triage nurses, which was evaluated by calculating precision, accuracy, recall of the top 3 recommended departments (recall@3), and time consumption. With 3957 (1%) records each, the testing and validation data sets achieved an accuracy of 0.8945 and 0.8941, respectively. Implemented in Xinhua Hospital, our triage system could accurately recommend 79 subspecialty departments and reduce the number of registration cancellations from 16,037 (3.83%) of the total 418,714 to 15,338 (3.53%) of the total 434200 (P<.05). In comparison to the triage system, the performance of the triage nurses was more accurate (0.9803 vs 0.9153) and precise (0.9213 vs 0.9049) since the system could identify subspecialty departments, whereas triage nurses or even general physicians can only recommend main departments. In addition, our triage system significantly outperformed triage nurses in recall@3 (0.6230 vs 0.5266; P<.001) and time consumption (10.11 vs 14.33 seconds; P<.001). The triage system demonstrates high accuracy in outpatient triage of all departments and excels in subspecialty department recommendations, which could decrease the cancellation rate and time consumption. It also improves the efficiency and convenience of clinical care to fulfill better the usage of medical resources, expand hospital effectiveness, and improve patient satisfaction in Chinese tertiary hospitals.

Large language models (LLMs) have achieved great progress in natural language processing tasks and demonstrated the potential for use in clinical applications. Despite their capabilities, LLMs in the medical domain are prone to generating hallucinations (not fully reliable responses). Hallucinations in LLMs' responses create substantial risks, potentially threatening patients' physical safety. Thus, to perceive and prevent this safety risk, it is essential to evaluate LLMs in the medical domain and build a systematic evaluation. We developed a comprehensive evaluation system, MedGPTEval, composed of criteria, medical data sets in Chinese, and publicly available benchmarks. First, a set of evaluation criteria was designed based on a comprehensive literature review. Second, existing candidate criteria were optimized by using a Delphi method with 5 experts in medicine and engineering. Third, 3 clinical experts designed medical data sets to interact with LLMs. Finally, benchmarking experiments were conducted on the data sets. The responses generated by chatbots based on LLMs were recorded for blind evaluations by 5 licensed medical experts. The evaluation criteria that were obtained covered medical professional capabilities, social comprehensive capabilities, contextual capabilities, and computational robustness, with 16 detailed indicators. The medical data sets include 27 medical dialogues and 7 case reports in Chinese. Three chatbots were evaluated: ChatGPT by OpenAI; ERNIE Bot by Baidu, Inc; and Doctor PuJiang (Dr PJ) by Shanghai Artificial Intelligence Laboratory. Dr PJ outperformed ChatGPT and ERNIE Bot in the multiple-turn medical dialogues and case report scenarios. Dr PJ also outperformed ChatGPT in the semantic consistency rate and complete error rate category, indicating better robustness. However, Dr PJ had slightly lower scores in medical professional capabilities compared with ChatGPT in the multiple-turn dialogue scenario. MedGPTEval provides comprehensive criteria to evaluate chatbots by LLMs in the medical domain, open-source data sets, and benchmarks assessing 3 LLMs. Experimental results demonstrate that Dr PJ outperforms ChatGPT and ERNIE Bot in social and professional contexts. Therefore, such an assessment system can be easily adopted by researchers in this community to augment an open-source data set.

Machine learning (ML) has been increasingly applied to cervical cancer (CC) research. However, few studies have combined both clinical parameters and imaging data. At the same time, there remains an urgent need for more robust and accurate preoperative assessment of parametrial invasion and lymph node metastasis, as well as postoperative prognosis prediction. The objective of this study is to develop an integrated ML model combining clinicopathological variables and magnetic resonance image features for (1) preoperative parametrial invasion and lymph node metastasis detection and (2) postoperative recurrence and survival prediction. Retrospective data from 250 patients with CC (2014-2022; 2 tertiary hospitals) were analyzed. Variables were assessed for their predictive value regarding parametrial invasion, lymph node metastasis, survival, and recurrence using 7 ML models: K-nearest neighbor (KNN), support vector machine, decision tree, random forest (RF), balanced RF, weighted DT, and weighted KNN. Performance was assessed via 5-fold cross-validation using accuracy, sensitivity, specificity, precision, F1-score, and area under the receiver operating characteristic curve (AUC). The optimal models were deployed in an artificial intelligence-assisted contouring and prognosis prediction system. Among 250 women, there were 11 deaths and 24 recurrences. (1) For preoperative evaluation, the integrated model using balanced RF achieved optimal performance (sensitivity 0.81, specificity 0.85) for parametrial invasion, while weighted KNN achieved the best performance for lymph node metastasis (sensitivity 0.98, AUC 0.72). (2) For postoperative prognosis, weighted KNN also demonstrated high accuracy for recurrence (accuracy 0.94, AUC 0.86) and mortality (accuracy 0.97, AUC 0.77), with relatively balanced sensitivity of 0.80 and 0.33, respectively. (3) An artificial intelligence-assisted contouring and prognosis prediction system was developed to support preoperative evaluation and postoperative prognosis prediction. The integration of clinical data and magnetic resonance images provides enhanced diagnostic capability to preoperatively detect parametrial invasion and lymph node metastasis detection and prognostic capability to predict recurrence and mortality for CC, facilitating personalized, precise treatment strategies.

Myopia, strabismus, and ptosis are common pediatric eye diseases, which have a negative impact on children and adolescents in terms of visual function, mental health, and health-related quality of life (HRQoL). Therefore, this study focused on those pediatric eye diseases by analyzing their risk factors and HRQoL for the comprehensive management of myopia, strabismus, and ptosis. A total of 363 participants (2-18 years old) were included in this study for risk factors analysis of myopia, strabismus, and ptosis. We collected demographic characteristics, lifestyle habits and eye care habits of these children and analyzed them by using univariable and multivariable logistic regression. In addition, we applied the Chinese version of Pediatric Quality of Life Inventory-Version 4.0 (PedsQL 4.0) to assess HRQoL in 256 children with strabismus and ptosis. Univariable and multivariable linear regression models were applied to evaluate potential influencing factors of HRQoL. Of all the participants, 140 had myopia, 127 had strabismus, and 145 had ptosis. Based on the multivariable logistic regression analysis model, we found that the history of parental myopia and daily average near-distance eye usage time were risk factors for myopia, and increased body mass index (BMI) was identified as a risk factor for strabismus and ptosis. Individuals with ptosis possessed decreased HRQoL. The multivariable linear regression model suggested that daily average near-distance eye usage time, light intensity during visual tasks, and daily average sleep duration had potential influences on HRQoL. This is the first study to assess the risk factors and HRQoL of myopia, strabismus, and ptosis together. We identified risk factors for these common pediatric eye diseases to help doctors, parents, and teachers better manage them. Our study discovered that children with eye disorders exhibit a notably diminished HRQoL. Consequently, it emphasizes the necessity for increased social attention and mental health assistance for these children.

Considering the high incidence of medical privacy disclosure, it is of vital importance to study doctors' privacy protection behavior and its influencing factors. We aim to develop a scale for doctors' protection of patients' privacy in Chinese public medical institutions, following construction of a theoretical model framework through grounded theory, and subsequently to validate the scale to measure this protection behavior. Combined with the theoretical paradigm of protection motivation theory (PMT) and semistructured interview data, the grounded theory research method, followed by the Delphi expert and group discussion methods, a theoretical framework and initial scale for doctors in Chinese public medical institutions to protect patients' privacy was formed. The adjusted scale was collected online using a WeChat electronic survey measured using a 5-point Likert scale. Exploratory and confirmatory factor analysis (EFA and CFA) and tests to analyze reliability and validity were performed on the sample data. SPSS 19.0 and Amos 26.0 statistical analysis software were used for EFA and CFA of the sample data, respectively. According to the internal logic of PMT, we developed a novel theoretical framework of a \"storyline,\" which was a process from being unaware of patients' privacy to having privacy protection behavior, that affected doctors' cognitive intermediary and changed the development of doctors' awareness, finally affecting actual privacy protection behavior in Chinese public medical institutions. Ultimately, we created a scale to measure 18 variables in the theoretical model, comprising 63 measurement items, with a total of 208 doctors participating in the scaling survey, who were predominantly educated to the master's degree level (n=151, 72.6%). The department distribution was relatively balanced. Prior to EFA, the Kaiser-Meyer-Olkin (KMO) value was 0.702, indicating that the study was suitable for factor analysis. The minimum value of Cronbach α for each study variable was .754, which met the internal consistency requirements of the scale. The standard factor loading value of each potential measurement item in CFA had scores greater than 0.5, which signified that all the items in the scale could effectively converge to the corresponding potential variables. The theoretical framework and scale to assess doctors' patient protection behavior in public medical institutions in China fills a significant gap in the literature and can be used to further the current knowledge of physicians' thought processes and adoption decisions.

Background: There is limited research on the associations between serum nutritional biomarkers and mortality risk in patients with metabolic syndrome (MetS). Existing studies merely investigated the single-biomarker effect. Thus, this study aimed to investigate the combined effect of nutritional biomarker mixtures and mortality risk using the Bayesian kernel machine regression (BKMR) model in patients with MetS. Methods: We included the MetS patients, defined according to the 2018 Guideline on the Management of Blood Cholesterol from the National Health and Nutrition Examination Survey (NHANES) 2001–2006. A total of 20 serum nutritional biomarkers were measured and evaluated in this study. The Cox proportional hazard model and restricted cubic spline models were used to evaluate the individual linear and non-linear association of 20 nutritional biomarkers with mortality risk. Bayesian kernel machine regression (BKMR) was used to assess the associations between mixture of nutritional biomarkers and mortality risk. Results: A total of 1455 MetS patients had a median age of 50 years (range: 20–85). During a median of 17.1-year follow-up, 453 (24.72%) died: 146 (7.20%) caused by CVD and 87 (5.26%) by cancer. Non-linear and linear analyses indicated that, in total, eight individual biomarkers (α-carotene, β-carotene, bicarbonate, lutein/zeaxanthin, lycopene, potassium, protein, and vitamin A) were significantly associated with all-cause mortality (all p-values < 0.05). Results from BKMR showed an association between the low levels of the mixture of nutritional biomarkers and high risk of all-cause mortality with the estimated effects ranging from 0.04 to 0.14 (referent: medians). α-Carotene (PIP = 0.971) and potassium (PIP = 0.796) were the primary contributors to the combined effect of the biomarker mixture. The nutritional mixture levels were found to be negatively associated with the risk of cardiovascular disease (CVD) mortality and positively associated with the risk of cancer mortality. After it was stratified by nutrients, the mixture of vitamins showed a negative association with all-cause and CVD mortality, whereas the mixture of mineral-related biomarkers was positively associated with all-cause and cancer mortality. Conclusion: Our findings support the evidence that nutritional status was associated with long-term health outcomes in MetS patients. It is necessary for MetS patients to be concerned with certain nutritional status (i.e., vitamins and mineral elements).

A chiplet placement algorithm for 2.5-D IC integration on an interposer is discussed in this paper. Inspired by the NoC (network-on-chip) mapping problem, we propose a novel chiplet placement algorithm called the CCEOA (chiplet communication energy optimization algorithm), which takes into account the actual size of the chiplet. The CCEOA can map chiplets to mesh topology, resulting in a layout with a low CEC (communication energy consumption). The algorithm considers the spacing of the chiplets while selecting the initial nodes and the nodes to map the next chiplet. Furthermore, because there exist nodes resulting in the same CEC increment during the mapping process, the algorithm adopts a secondary local exploration strategy to further select nodes. Meanwhile, the lateral and vertical placements of chiplets are also considered. The algorithm is implemented and evaluated with a 2.5-D IC integration with 22 chiplets to demonstrate its efficiency and the accuracy.

A chiplet multi-objective optimization algorithm for 2.5-D integrated circuit (IC) based on a passive interposer is discussed in this article. Inspired by the network-on-chip mapping problem, we propose a novel algorithm, called chiplet multi-objective optimization, which minimizes the average temperature and the communication consumption between chiplets at the same time. The algorithm considers the specificities of 2.5-D IC chiplets, such as the spacing and different sizes of chiplets. In addition to the weight factor, α is also introduced to make a balance between temperature and the communication consumption. The designer can change the weight factor according to their own requirement. The multi-window display system is used as an example in this article to demonstrate the algorithm’s efficiency and the accuracy. According to our algorithm, the system temperature of the most ideal solution can be reduced by 8.34 K and the communication consumption reduced by 232.13 μJ.

Background: Prior studies on the relationship between bowel health and mortality have generally focused on the individual association of stool frequency or consistency with mortality but did not present a joint association. Therefore, we aimed to systematically evaluate the individual and joint associations of stool frequency and consistency with all-cause and cause-specific mortality in this study. Methods: A total of 14,574 participants from the National Health and Nutrition Examination Survey 2005–2010 were incorporated in this analysis. Survey sample-weighted Cox proportional hazards models adjusted for potential confounders were used to estimate hazard ratios (HRs) between bowel health measures and mortality risks. Results: During a median of 7.6 years of follow-up, 1502 deaths occurred, including 357 cancer deaths and 284 cardiovascular disease (CVD) deaths. The bowel habit of the most participants was 7 times/week (50.7%), and the most common type was “Like a sausage or snake, smooth and soft” (51.8%). Stool frequency displayed a parabolic relationship with all-cause mortality, and less than 7 times/week is a significant risk factor for mortality (HR for 1 time/week: 1.43, p-values = 0.04. HR for 6 times/week: 1.05, p-value = 0.03). Analyzing the joint association of stool frequency and consistency on mortality clarified the limitations of only inspecting the effects of either individual factor. Compared with 7 times/week of normal stool, infrequent soft stools at 4 times/week were associated with 1.78-, 2.42-, and 2.27-times higher risks of all-cause, cancer, and CVD mortality, respectively. Conclusion: Analyses of bowel health should consider the joint effects of stool frequency and stool consistency. Self-appraisal of stool frequency and consistency may be a simple but useful tool for informing about major chronic illnesses.

This study aimed to evaluate the clinical efficacy of Longyizhengqi granule, a traditional Chinese medicine, in patients with mild COVID-19. We conducted a prospective study including mild COVID-19 participants conducted at Mobile Cabin Hospital in Shanghai, China. Participants were assigned to receive Longyizhengqi granule or conventional treatment. The primary outcome was the time for nucleic acid to turn negative and the secondary outcomes are hospital stay and changes in cycle threshold (Ct) values for N gene and Orf gene. Multilevel random-intercept model was performed to analyze the effects of treatment. A total of 3243 patients were included in this study (Longyizhengqi granule 667 patients; conventional treatment 2576 patients). Age (43.5 vs 42.1, p<0.01) and vaccination doses (not vaccinated: 15.8% vs 21.7%, 1 dose: 3.5% vs 2.9%, 2 doses: 27.9% vs 25.6%, 3 doses: 52.8% vs 49.8%. p<0.01) show statistical difference between Conventional treatment group and LYZQ granules group. The use of Longyizhengqi granule could significantly reduce the time for nucleic acid to turn negative (14.2 days vs 10.7 days, p<0.01), shorten hospital stay (12.5 days vs 9.9 days, p<0.01), and increase the changes in Ct value for N gene (8.44 vs 10.33, p<0.01) and Orf gene (7.31 vs 8.44, p<0.01) to approximately 1.5. Moreover, the difference in the changes of Ct values on the 4th, 6th, 8th, and 10th days seem to increase between two groups. No serious adverse events were reported. Longyizhengqi granule might be a promising drug for the treatment of mild COVID-19, and it might be beneficial to effectively shorten the negative transition time of nucleic acid, the total days of hospitalization, and increase the changes of Ct values. Long-term randomized controlled trials with follow-up evaluations are required to confirm its long-term efficacy.