ObjectiveTo systematically review the early clinical prediction value of machine learning (ML) for cardiac arrest (CA).MethodsPubMed, EMbase, WanFang Data and CNKI databases were electronically searched to retrieve all ML studies on predicting CA from January 2015 to February 2021. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. The value of each model was evaluated based on the area under receiver operating characteristic curve (AUC) and accuracy.ResultsA total of 38 studies were included. In terms of data sources, 13 studies were based on public database, and other studies retrospectively collected clinical data, in which 21 directly predicted CA, 3 predicted CA-related arrhythmias, and 9 predicted sudden cardiac death. A total of 51 models had been adopted, among which the most popular ML methods included artificial neural network (n=11), followed by random forest (n=9) and support vector machine (n=5). The most frequently used input feature was electrocardiogram parameters (n=20), followed by age (n=12) and heart rate variability (n=10). Six studies compared the ML models with other traditional statistical models and the results showed that the AUC value of ML was generally higher than that in traditional statistical models.ConclusionsThe available evidence suggests that ML can accurately predict the occurrence of CA, and the performance is significantly superior to traditional statistical model in certain cases.
Machine learning-based diagnostic tests have certain differences of measurement indicators with traditional diagnostic tests. In this paper, we elaborate the definitions, calculation methods and statistical inferences of common measurement indicators of machine learning-based diagnosis models in detail. We hope that this paper will be helpful for clinical researchers to better evaluate machine learning diagnostic models.
This review describes the concept of artificial intelligence, introduces the working mechanism and the main structure of medical expert system, as well as the development history of medical expert system at home and abroad and its applications in the medical field. The concept of machine learning, commonly used algorithms and its clinical applications in medical diagnosis are briefly described. It mainly introduces the application of artificial intelligence in neurology. The advantages and disadvantages of artificial intelligence system in medical field are analyzed. Finally, the future of artificial intelligence in the medical field is forecasted.
Objective To perform data-driven, assisted prediction of health insurance reimbursement ratios for the major thoracic surgery group in CHS-DRG, in addition to providing an optional solution for health insurance providers and medical institutions to accurately and effectively predict the references of health insurance payments for the patient group. Methods Using the information on major thoracic surgery cases from a large tertiary hospital in Sichuan province in 2020 as a sample, 70% of the total dataset was used as a training dataset and 30% as a test dataset. This data was used to predict health insurance spending through a multiple linear regression model and an improved machine learning method that is based on feature selection. Results When the number of filtered features was the same via three machine learning methods including random forest, logistic regression, and support vector machine, there was no significant difference in the prediction effectiveness. The model with the best prediction effect had an accuracy of 78.96%, sensitivity of 83.93%, specificity of 71.27%, precision of 0.818 8, AUC value of 0.841 4, and a Kappa value of 0.610 8. Conclusion The basic characteristics such as the number of disease diagnoses and surgical operations, as well as the age of patients affect the reimbursement ratio. The cost of materials, drugs, and treatments has a greater impact on the reimbursement ratio. The combined method of feature selection and machine learning outperforms traditional statistical linear models. When dealing with a larger dataset that has many features, selecting the right number can enhance the prediction ability and efficiency of the model.
ObjectiveTo systematically evaluate the risk prediction models of acute kidney injury in patients with acute coronary syndrome (ACS) based on machine learning, providing a reference for clinical selection of appropriate risk assessment tools. MethodsClinical studies using machine learning methods for predicting the risk of acute kidney injury in ACS patients were retrieved from PubMed, Cochrane Library, EMbase, Web of Science core database, CNKI, Wanfang Database, Chinese Biomedical Literature Database, and VIP Journal Database. The retrieval time was from the establishment of the database to May 24, 2025, and the quality of the model was evaluated using the prediction model bias risk assessment tool. ResultsNine articles were included, using 20 machine learning methods to construct 58 prediction models. The area under the receiver operating characteristic curve ranged from 0.740 to 0.894. The most commonly used predictors were age and creatinine. The overall bias risk of the included studies was relatively high, but the applicability was good. Conclusion: Machine learning models can identify the risk of acute kidney injury in ACS patients. All models have good predictive potential, but they are still in the development stage. It is recommended that future studies adopt prospective research and external validation to improve the stability and predictive accuracy of the model.
ObjectiveTo systematically evaluate the clinical value of machine learning (ML) for predicting the neurological outcome of out-of-hospital cardiac arrest (OHCA), and to develop a prediction model. MethodsWe searched the PubMed, Web of Science, EMbase, CNKI, Wanfang database from January 1, 2011 to November 24, 2021. Studies on ML for predicting neurological outcomes in OHCA pateints were collected. Two researchers independently screened the literature, extracted the data and evaluated the bias of the included literature, evaluated the accuracy of different models and compared the area under the receiver operating characteristic curve (AUC). ResultsA total of 20 studies were included. Eleven of the studies were from open source databases and nine were from retrospective studies. Sixteen studies directly predicted OHCA neurological outcomes, and four predicted OHCA neurological outcomes after target temperature management. A total of seven ML algorithms were used, among which neural network was the ML algorithm with the highest frequency (n=5), followed by support vector machine and random forest (n=4). Three papers used multiple algorithms. The most frequently used input characteristic was age (n=19), followed by heart rate (n=17) and gender (n=13). A total of 4 studies compared the predictive value of ML with other classical statistical models, and the AUC value of ML model was higher than that of classical statistical models. ConclusionExisting evidence suggests that ML can more accurately predict OHCA nervous system outcomes, and the predictive performance of ML is superior to traditional statistical models in certain situations.
China is one of the countries in the world with the highest rate of esophageal cancer. Early detection, accurate diagnosis, and treatment of esophageal cancer are critical for improving patients’ prognosis and survival. Machine learning technology has become widely used in cancer, which is benefited from the accumulation of medical images and advancement of artificial intelligence technology. Therefore, the learning model, image type, data type and application efficiency of current machine learning technology in esophageal cancer are summarized in this review. The major challenges are identified, and solutions are proposed in medical image machine learning for esophageal cancer. Machine learning's potential future directions in esophageal cancer diagnosis and treatment are discussed, with a focus on the possibility of establishing a link between medical images and molecular mechanisms. The general rules of machine learning application in the medical field are summarized and forecasted on this foundation. By drawing on the advanced achievements of machine learning in other cancers and focusing on interdisciplinary cooperation, esophageal cancer research will be effectively promoted.
This paper expounds the classification and characteristics of healthcare-associated infections (HAI) surveillance systems from the perspective of the informatization needs of HAI monitoring, explains the determination requirements of numerator and denominator in the surveillance statistical data, and introduces the regular verification for auditing the quality of HAI surveillance. The basic knowledge of machine learning and its achievements are introduced in processing surveillance data as well. Machine learning may become the mainstream algorithm of HAI automatic monitoring system in the future. Infection control professionals should learn relevant knowledge, cooperate with computer engineers and data analysts to establish more effective, reasonable and accurate monitoring systems, and improve the outcomes of HAI prevention and control in medical institutions.
With the development of artificial intelligence (AI) technology, great progress has been made in the application of AI in the medical field. While foreign journals have published a large number of papers on the application of AI in epilepsy, there is a dearth of studies within domestic journals. In order to understand the global research progress and development trend of AI applications in epilepsy, a total of 895 papers on AI applications in epilepsy included in the Web of Science Core Collection and published before December 31, 2022 were selected as the research objects. The annual number of papers and their cited times, the most published authors, institutions and countries, and their cooperative relationships were analyzed, and the research hotspots and future trends in this field were explored by using bibliometrics and other methods. The results showed that before 2016, the annual number of papers on the application of AI in epilepsy increased slowly, and after 2017, the number of publications increased rapidly. The United States had the largest number of papers (n=273), followed by China (n=195). The institution with the largest number of papers was the University of London (n=36), and Capital Medical University in China had 23 papers. The author with the most published papers was Gregory Worrell (n=14), and the scholar with the most published articles in China was Guo Jiayan from Xiamen University (n=7). The application of machine learning in the diagnosis and treatment of epilepsy is an early research focus in this field, while the seizure prediction model based on EEG feature extraction, deep learning especially convolutional neural network application in epilepsy diagnosis, and cloud computing application in epilepsy healthcare, are the current research priorities in this field. AI-based EEG feature extraction, the application of deep learning in the diagnosis and treatment of epilepsy, and the Internet of things to solve epilepsy health-related problems are the research aims of this field in the future.
Emotion recognition refers to the process of determining and identifying an individual's current emotional state by analyzing various signals such as voice, facial expressions, and physiological indicators etc. Using electroencephalogram (EEG) signals and virtual reality (VR) technology for emotion recognition research helps to better understand human emotional changes, enabling applications in areas such as psychological therapy, education, and training to enhance people’s quality of life. However, there is a lack of comprehensive review literature summarizing the combined researches of EEG signals and VR environments for emotion recognition. Therefore, this paper summarizes and synthesizes relevant research from the past five years. Firstly, it introduces the relevant theories of VR and EEG signal emotion recognition. Secondly, it focuses on the analysis of emotion induction, feature extraction, and classification methods in emotion recognition using EEG signals within VR environments. The article concludes by summarizing the research’s application directions and providing an outlook on future development trends, aiming to serve as a reference for researchers in related fields.