Sleep apnea causes cardiac arrest, sleep rhythm disorders, nocturnal hypoxia and abnormal blood pressure fluctuations in patients, which eventually lead to nocturnal target organ damage in hypertensive patients. The incidence of obstructive sleep apnea hypopnea syndrome (OSAHS) is extremely high, which seriously affects the physical and mental health of patients. This study attempts to extract features associated with OSAHS from 24-hour ambulatory blood pressure data and identify OSAHS by machine learning models for the differential diagnosis of this disease. The study data were obtained from ambulatory blood pressure examination data of 339 patients collected in outpatient clinics of the Chinese PLA General Hospital from December 2018 to December 2019, including 115 patients with OSAHS diagnosed by polysomnography (PSG) and 224 patients with non-OSAHS. Based on the characteristics of clinical changes of blood pressure in OSAHS patients, feature extraction rules were defined and algorithms were developed to extract features, while logistic regression and lightGBM models were then used to classify and predict the disease. The results showed that the identification accuracy of the lightGBM model trained in this study was 80.0%, precision was 82.9%, recall was 72.5%, and the area under the working characteristic curve (AUC) of the subjects was 0.906. The defined ambulatory blood pressure features could be effectively used for identifying OSAHS. This study provides a new idea and method for OSAHS screening.
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.
Alzheimer’s disease (AD) is a common and serious form of elderly dementia, but early detection and treatment of mild cognitive impairment can help slow down the progression of dementia. Recent studies have shown that there is a relationship between overall cognitive function and motor function and gait abnormalities. We recruited 302 cases from the Rehabilitation Hospital Affiliated to National Rehabilitation Aids Research Center and included 193 of them according to the screening criteria, including 137 patients with MCI and 56 healthy controls (HC). The gait parameters of the participants were collected during performing single-task (free walking) and dual-task (counting backwards from 100) using a wearable device. By taking gait parameters such as gait cycle, kinematics parameters, time-space parameters as the focus of the study, using recursive feature elimination (RFE) to select important features, and taking the subject’s MoCA score as the response variable, a machine learning model based on quantitative evaluation of cognitive level of gait features was established. The results showed that temporal and spatial parameters of toe-off and heel strike had important clinical significance as markers to evaluate cognitive level, indicating important clinical application value in preventing or delaying the occurrence of AD 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.
Myocardial infarction (MI) has the characteristics of high mortality rate, strong suddenness and invisibility. There are problems such as the delayed diagnosis, misdiagnosis and missed diagnosis in clinical practice. Electrocardiogram (ECG) examination is the simplest and fastest way to diagnose MI. The research on MI intelligent auxiliary diagnosis based on ECG is of great significance. On the basis of the pathophysiological mechanism of MI and characteristic changes in ECG, feature point extraction and morphology recognition of ECG, along with intelligent auxiliary diagnosis method of MI based on machine learning and deep learning are all summarized. The models, datasets, the number of ECG, the number of leads, input modes, evaluation methods and effects of different methods are compared. Finally, future research directions and development trends are pointed out, including data enhancement of MI, feature points and dynamic features extraction of ECG, the generalization and clinical interpretability of models, which are expected to provide references for researchers in related fields of MI intelligent auxiliary diagnosis.
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.
Objective To systematically review prediction models of small for gestational age (SGA) based on machine learning and provide references for the construction and optimization of such a prediction model. Methods The PubMed, EMbase, Web of Science, CBM, WanFang Data, VIP and CNKI databases were electronically searched to collect studies on SGA prediction models from database inception to August 10, 2022. Two researchers independently screened the literature, extracted data, evaluated the risk of bias of the included studies, and conducted a systematic review. Results A total of 14 studies, comprising 40 prediction models constructed using 19 methods, such as logical regression and random forest, were included. The results of the risk of bias assessment from 13 studies were high; the area under the curve of the prediction models ranged from 0.561 to 0.953. Conclusion The overall risk of bias in the prediction models for SGA was high, and the predictive performance was average. Models built using extreme gradient boosting (XGBoost) demonstrated the best predictive performance across different studies. The stacking method can improve predictive performance by integrating different models. Finally, maternal blood pressure, fetal abdominal circumference, head circumference, and estimated fetal weight were important predictors of SGA.
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.
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.
As an interdisciplinary subject of medicine and artificial intelligence, intelligent diagnosis and treatment has received extensive attention in both academia and industry. Traditional Chinese medicine (TCM) is characterized by individual syndrome differentiation as well as personalized treatment with personality analysis, which makes the common law mining technology of big data and artificial intelligence appear distortion in TCM diagnosis and treatment study. This article put forward an intelligent diagnosis model of TCM, as well as its construction method. It could not only obtain personal diagnosis varying individually through active learning, but also integrate multiple machine learning models for training, so as to form a more accurate model of learning TCM. Firstly, we used big data extraction technique from different case sources to form a structured TCM database under a unified view. Then, taken a pediatric common disease pneumonia with dyspnea and cough as an example, the experimental analysis on large-scale data verified that the TCM intelligent diagnosis model based on active learning is more accurate than the pre-existing machine learning methods, which may provide a new effective machine learning model for studying TCM diagnosis and treatment.