Construction of blood consumption prediction model for emergency trauma patients based on machine learning algorithm / 中华急诊医学杂志

Objective:To establish a blood consumption prediction model for emergency trauma patients based on machine learning algorithm, so as to guide blood collection and blood supply institutions to prepare for the early blood demand of mass casualties in public emergencies.Methods:A retrospective analysis was conducted on trauma patients in the emergency system database of 12 hospitals in Zhejiang Province from January 2018 to December 2020. Patients with chronic medical history such as hematological diseases and tumors, and transferred from other hospitals after external treatment were excluded. The patients were divided into the transfusion group and non-transfusion group according to whether they received blood transfusion. The differences in demographic and clinical characteristics between the two groups were compared, and the computer learning algorithm (XGBoost) was used to build the blood consumption prediction model and blood consumption volume prediction model of emergency trauma patients.Results:Totally 2025 patients were included in this study, including 1146 patients in the transfusion group and 879 patients in the non-transfusion group. The blood demand of emergency trauma patients mainly occurred within 3 days of admission (60%). The main variables affecting the blood consumption prediction model of emergency trauma patients were shock index, hematocrit, systolic blood pressure, abdominal injury, pelvic injury, ascites and hemoglobin. Compared with the traditional prediction model, XGBoost model had the highest hit rate of 59.0%. The accuracy of blood consumption prediction model was the highest when seven levels of blood volume were adopted, and the deviation fluctuated between [0~1] U. According to the prediction model, the blood consumption prediction formula was∑ nw× c. Conclusions:The preliminarily constructed prediction model of blood transfusion and blood consumption for emergency trauma patients has better performance than the traditional prediction model of blood transfusion, which provides reference for optimizing the decision-making ability of blood demand assessment of hospitals and blood supply institutions under public emergencies.

Using machine learning algorithm to predict the risk of post-traumatic stress disorder among firefighters in Changsha / 中南大学学报(医学版)

OBJECTIVES@#Firefighters are prone to suffer from psychological trauma and post-traumatic stress disorder (PTSD) in the workplace, and have a poor prognosis after PTSD. Reliable models for predicting PTSD allow for effective identification and intervention for patients with early PTSD. By collecting the psychological traits, psychological states and work situations of firefighters, this study aims to develop a machine learning algorithm with the aim of effectively and accurately identifying the onset of PTSD in firefighters, as well as detecting some important predictors of PTSD onset.@*METHODS@#This study conducted a cross-sectional survey through convenient sampling of firefighters from 20 fire brigades in Changsha, which were evenly distributed across 6 districts and Changsha County, with a total of 628 firefighters. We used the synthetic minority oversampling technique (SMOTE) to process data sets and used grid search to finish the parameter tuning. The predictive capability of several commonly used machine learning models was compared by 5-fold cross-validation and using the area under the receiver operating characteristic curve (ROC-AUC), accuracy, precision, recall, and F1 score.@*RESULTS@#The random forest model achieved good performance in predicting PTSD with an average AUC score at 0.790. The mean accuracy of the model was 90.1%, with an F1 score of 0.945. The three most important predictors were perseverance, forced thinking, and reflective deep thinking, with weights of 0.165, 0.158, and 0.152, respectively. The next most important predictors were employment time, psychological power, and optimism.@*CONCLUSIONS@#PTSD onset prediction model for Changsha firefighters constructed by random forest has strong predictive ability, and both psychological characteristics and work situation can be used as predictors of PTSD onset risk for firefighters. In the next step of the study, validation using other large datasets is needed to ensure that the predictive models can be used in clinical setting.

Research on Self-perception and Active Warning Model of Medical Equipment Operation and Maintenance Status Based on Machine Learning Algorithm / 中国医疗器械杂志

The panoramic perception of medical equipment operation and maintenance status is the basic guarantee for the implementation of smart medical care, the machine learning algorithm-based autonomous perception and active early warning model of medical equipment operation and maintenance status is proposed. Introduce deep learning multi-dimensional perception of medical equipment multi-source heterogeneous fault data training sample characteristics to realize autonomous perception of medical equipment operation and maintenance status, introduce reinforcement learning to realize autonomous decision-making of test sample fault characteristics, and build the active early warning mechanism for medical equipment faults. Taking the equipment department of hospital as the carrier of model effectiveness verification, the effectiveness simulation of the model was carried out, the results show that the model has the advantages of comprehensive fault information perception, strong compatibility of medical equipment, high efficiency of active early warning.

Use of a Machine Learning Algorithm to Predict Individuals with Suicide Ideation in the General Population

OBJECTIVE: In this study, we aimed to develop a model predicting individuals with suicide ideation within a general population using a machine learning algorithm. METHODS: Among 35,116 individuals aged over 19 years from the Korea National Health & Nutrition Examination Survey, we selected 11,628 individuals via random down-sampling. This included 5,814 suicide ideators and the same number of non-suicide ideators. We randomly assigned the subjects to a training set (n=10,466) and a test set (n=1,162). In the training set, a random forest model was trained with 15 features selected with recursive feature elimination via 10-fold cross validation. Subsequently, the fitted model was used to predict suicide ideators in the test set and among the total of 35,116 subjects. All analyses were conducted in R. RESULTS: The prediction model achieved a good performance [area under receiver operating characteristic curve (AUC)=0.85] in the test set and predicted suicide ideators among the total samples with an accuracy of 0.821, sensitivity of 0.836, and specificity of 0.807. CONCLUSION: This study shows the possibility that a machine learning approach can enable screening for suicide risk in the general population. Further work is warranted to increase the accuracy of prediction.

A feasibility study of the automatic cystocele severity grading software for quantitative evaluation of prolapse of bladder posterior wall by transperineal ultrasound / 中华超声影像学杂志

Objective To investigate the feasibility of the automatic cystocele severity grading software for quantitative evaluation of prolapse of bladder posterior wall by transperineal ultrasound . Methods One hundred and seventy transperineal ultrasound video clips were recorded when the female patients performing the Valsalva maneuver and those clips were divided into training group ( 85 cases) and test group ( 85 cases) randomly ,then the ralated structures of the images from the training group offline were marked . Through machine learning algorithm ,the computer had learned and was able to analyzed the marking information ,then the automatic cystocele severity grading software was obtained . And later the software was ran to mark the structures and get the cystocele severity grading in the images from the test group . Meanwhile , the same structures of the same images manually were marked and after an interval of more than two weeks the process were repeated by 3 doctors . Finally the grading results obtained from the software and the measurers of the 3 doctors were compared . Results The intelligent identification and automatic measurement software obtained from the machine learning algorithm was able to identify the related structures . The grading results of each measurer were of good consistency ( κ :0 .72 －0 .78 ;ICC :0 .980－0 .990) . The grading results between different measurers were of good consistency ( κ :0 .65－0 .75 ;ICC :0 .985－0 .992) . The grading results between automatic software and three different measurers were of good consistency ( κ :0 .63－0 .67 ;ICC :0 .967－0 .969 ; r ＝0 .936 ,0 .943 ,0 .936 ,all P ＜0 .01) . Conclusions The automatic cystocele severity grading software is able to identify the related structures in the images and reliable to apply the software in pelvic floor ultrasound .