Artificial intelligence promotes shared decision-making through recommending tests to febrile pediatric outpatients

BACKGROUND:To promote the shared decision-making(SDM)between patients and doctors in pediatric outpatient departments,this study was designed to validate artificial intelligence(AI)-initiated medical tests for children with fever.METHODS:We designed an AI model,named Xiaoyi,to suggest necessary tests for a febrile child before visiting a pediatric outpatient clinic.We calculated the sensitivity,specificity,and F1 score to evaluate the efficacy of Xiaoyi’s recommendations.The patients were divided into the rejection and acceptance groups.Then we analyzed the rejected examination items in order to obtain the corresponding reasons.RESULTS:We recruited a total of 11,867 children with fever who had used Xiaoyi in outpatient clinics.The recommended examinations given by Xiaoyi for 10,636(89.6%)patients were qualified.The average F1 score reached 0.94.A total of 58.4%of the patients accepted Xiaoyi’s suggestions(acceptance group),and 41.6%refused(rejection group).Imaging examinations were rejected by most patients(46.7%).The tests being time-consuming were rejected by 2,133 patients(43.2%),including rejecting pathogen studies in 1,347 patients(68.5%)and image studies in 732 patients(31.8%).The difficulty of sampling was the main reason for rejecting routine tests(41.9%).CONCLUSION:Our model has high accuracy and acceptability in recommending medical tests to febrile pediatric patients,and is worth promoting in facilitating SDM.

Health-Related Quality of Life of Experts Who Worked in Air Disasters in Sao Paulo, Brazil

Aim: To evaluate the influence of identifying victims of air disasters in São Paulo on experts’ quality of life (QoL). Methods: QoL was evaluated using the abbreviated version of the World Health Organization (WHO) quality of life questionnaire (WHOQOL-bref). We assessed 29 forensic experts who worked in air disasters in São Paulo and 29 experts who have not worked. The results were analyzed with Student’s t-tests;we compared the QoL scores of individuals at the time of the accident with their current QoL scores, and the scores of the control group were compared with the current scores of the disaster group. Results: Statistical analyses revealed a significant decrease in forensic expert QoL when they worked at the accident site, and this result was evident in all WHOQOL-bref domains. No significant difference was observed between the experts’ current QoL scores and those of the control group. Conclusions: The identification of air disaster victims in the city of São Paulo significantly decreased expert health-related QoL (HRQoL) with regard to physical and psychological aspects, social relationships and environment domains. This disturbance on the QoL was not persistent over the years.

Prediction of Hyperuricemia Risk Based on Medical Examination Report Analysis

This study hopes to contribute to disease detection by analyzing a medical examination dataset with 123,968 samples.Based on association rules mining and related medical knowledge,6 models were constructed here to predict hyperuricemia prevalence and investigated its risk factors.Comparing different models,the prediction performances of Lasso logistic regression,traditional logistic regression,and random forest are excellent,and the results can be interpreted.PCA logistic regression model also works well,but it is not analytical.KNN's prediction performance is relatively poor,while data dimensionality reduction can significantly improve its AUC.SVC has the worst performance and its efficiency of processing high-dimensional large dataset is extremely low.The risk factors of hyperuricemia mainly belongs to 4 categories,which are obesity-related factors,renal function factors,liver function factors,and myeloproliferative diseases-related factors.Random forest,Lasso regression,and logistic regression all treat serum creatinine,BMI,triglyceride,fatty liver,and age as key predictive variables.Models also show that serum urea,serum alanine aminotransferase,negative urobilinogen,red blood cell count,white blood cell count and the pH are significantly correlated with the risk.

Confrontation and Integration: The Present and Future of Forensic Examinations on Medical Damage in China

In China,two administrative regulations and judicial interpretations related to forensic examinations of medical damage were issued from March 2017 to June 2018.In chronological order,they were interpretation of the supreme people’s court on several questions concerning the application of law in the trial of disputes over liability for medical damage and regulations on the prevention and handling of medical disputes.Those two laws,especially the regulation,have had a fundamental impact on the pattern of forensic examinations on medical damage.This paper systematically reviews the current status and existing problems with forensic examinations on medical damage following implementation of the law of tort liability;it discusses new concepts of procedures,institutional arrangements,and the selection of examiners for forensic examinations on medical damage.We believe that through the regulation,the dualistic confrontation status of forensic examinations on medical damage will gradually change toward integration.We consider that forensic examinations of medical damage will face three challenges in the future:(1)enhancing the establishment of standards;(2)undertaking theoretical research into forensic examinations onmedical damage;and(3)promoting the development ofinterdisciplinary identification specialistsmajoring in both medicine and law.Only in this way will it be possible to rectify the current dilemma with forensic examinations on medical damage in China.