@Article{info:doi/10.2196/35239,
author="Zhang, Hong
and Zhang, Jiajun
and Ni, Wandong
and Jiang, Youlin
and Liu, Kunjing
and Sun, Daying
and Li, Jing",
title="Transformer- and Generative Adversarial Network--Based Inpatient Traditional Chinese Medicine Prescription Recommendation: Development Study",
journal="JMIR Med Inform",
year="2022",
month="May",
day="31",
volume="10",
number="5",
pages="e35239",
keywords="traditional Chinese medicine",
keywords="transformer",
keywords="generative adversary networks",
keywords="electronic health records",
keywords="artificial intelligence",
keywords="natural language processing",
keywords="machine learning",
keywords="word2Vec",
abstract="Background: Traditional Chinese medicine (TCM) practitioners usually follow a 4-step evaluation process during patient diagnosis: observation, auscultation, olfaction, inquiry, pulse feeling, and palpation. The information gathered in this process, along with laboratory test results and other measurements such as vital signs, is recorded in the patient's electronic health record (EHR). In fact, all the information needed to make a treatment plan is contained in the EHR; however, only a seasoned TCM physician could use this information well to make a good treatment plan as the reasoning process is very complicated, and it takes years of practice for a medical graduate to master the reasoning skill. In this digital medicine era, with a deluge of medical data, ever-increasing computing power, and more advanced artificial neural network models, it is not only desirable but also readily possible for a computerized system to mimic the decision-making process of a TCM physician. Objective: This study aims to develop an assistive tool that can predict prescriptions for inpatients in a hospital based on patients' clinical EHRs. Methods: Clinical health records containing medical histories, as well as current symptoms and diagnosis information, were used to train a transformer-based neural network model using the corresponding physician's prescriptions as the target. This was accomplished by extracting relevant information, such as the patient's current illness, medicines taken, nursing care given, vital signs, examinations, and laboratory results from the patient's EHRs. The obtained information was then sorted chronologically to produce a sequence of data for the patient. These time sequence data were then used as input to a modified transformer network, which was chosen as a prescription prediction model. The output of the model was the prescription for the patient. The ultimate goal is for this tool to generate a prescription that matches what an expert TCM physician would prescribe. To alleviate the issue of overfitting, a generative adversarial network was used to augment the training sample data set by generating noise-added samples from the original training samples. Results: In total, 21,295 copies of inpatient electronic medical records from Guang'anmen Hospital were used in this study. These records were generated between January 2017 and December 2018, covering 6352 types of medicines. These medicines were sorted into 819 types of first-category medicines based on their class relationships. As shown by the test results, the performance of a fully trained transformer model can have an average precision rate of 80.58\% and an average recall rate of 68.49\%. Conclusions: As shown by the preliminary test results, the transformer-based TCM prescription recommendation model outperformed the existing conventional methods. The extra training samples generated by the generative adversarial network help to overcome the overfitting issue, leading to further improved recall and precision rates. ",
doi="10.2196/35239",
url="https://medinform.jmir.org/2022/5/e35239/",
url="http://www.ncbi.nlm.nih.gov/pubmed/35639469"
}


@Article{info:doi/10.2196/21455,
author="Sheng, Hongfeng
and Xu, Weixing
and Xu, Bin
and Song, Hongpu
and Lu, Di
and Ding, Weiguo
and Mildredl, Henry",
title="Application of Intelligent Computer-Assisted Taylor 3D External Fixation in the Treatment of Tibiofibular Fracture: Retrospective Case Study",
journal="JMIR Med Inform",
year="2021",
month="May",
day="14",
volume="9",
number="5",
pages="e21455",
keywords="intelligent computer-assisted instruction",
keywords="Taylor three-dimensional external fixation",
keywords="tibial fracture",
keywords="internal fixation",
keywords="external fixation",
abstract="Background: With the development of modern society, severe and complex tibial fractures caused by high-energy injuries such as traffic accidents have gradually increased. At present, the commonly used methods for the treatment of tibial fractures include plate fixation, intramedullary nail fixation, and external fixation. Most of these fractures are open wounds with severe soft tissue injury and wound contamination, and some involve bone defects, which makes internal fixation treatment difficult. Objective: This study aims to explore the use of intelligent computer-assisted Taylor 3D external fixation for the treatment of tibiofibular fractures. Methods: In total, 70 patients were included and divided into the Taylor 3D external fixation (TSF) group (28 patients with severe tibial fractures treated with TSF) and the internal fixation group (42 patients with complicated tibiofibular fractures treated by internal fixation). After the treatment, the follow-up evaluation of TSF for the treatment of tibiofibular fractures noted the incidence of complications, as well as the efficacy and occurrence of internal fixation for the treatment of tibial fractures in our hospital. Results: The results showed that TSF was superior to orthopedics in the treatment of tibiofibular fractures in terms of efficacy and complications. Conclusions: TSF for the treatment of tibiofibular fractures is more effective than internal fixation and the incidence of complications is low. This is a new technology for the treatment of tibiofibular fractures that is worthy of clinical promotion. ",
doi="10.2196/21455",
url="https://medinform.jmir.org/2021/5/e21455",
url="http://www.ncbi.nlm.nih.gov/pubmed/33988516"
}


@Article{info:doi/10.2196/19055,
author="Yang, Yuanlin
and Li, Dehua",
title="Medical Data Feature Learning Based on Probability and Depth Learning Mining: Model Development and Validation",
journal="JMIR Med Inform",
year="2021",
month="Apr",
day="8",
volume="9",
number="4",
pages="e19055",
keywords="deep learning",
keywords="data mining",
keywords="medical big data",
keywords="model building",
abstract="Background: Big data technology provides unlimited potential for efficient storage, processing, querying, and analysis of medical data. Technologies such as deep learning and machine learning simulate human thinking, assist physicians in diagnosis and treatment, provide personalized health care services, and promote the use of intelligent processes in health care applications. Objective: The aim of this paper was to analyze health care data and develop an intelligent application to predict the number of hospital outpatient visits for mass health impact and analyze the characteristics of health care big data. Designing a corresponding data feature learning model will help patients receive more effective treatment and will enable rational use of medical resources. Methods: A cascaded depth model was successfully implemented by constructing a cascaded depth learning framework and by studying and analyzing the specific feature transformation, feature selection, and classifier algorithm used in the framework. To develop a medical data feature learning model based on probabilistic and deep learning mining, we mined information from medical big data and developed an intelligent application that studies the differences in medical data for disease risk assessment and enables feature learning of the related multimodal data. Thus, we propose a cascaded data feature learning model. Results: The depth model created in this paper is more suitable for forecasting daily outpatient volumes than weekly or monthly volumes. We believe that there are two reasons for this: on the one hand, the training data set in the daily outpatient volume forecast model is larger, so the training parameters of the model more closely fit the actual data relationship. On the other hand, the weekly and monthly outpatient volume is the cumulative daily outpatient volume; therefore, errors caused by the prediction will gradually accumulate, and the greater the interval, the lower the prediction accuracy. Conclusions: Several data feature learning models are proposed to extract the relationships between outpatient volume data and obtain the precise predictive value of the outpatient volume, which is very helpful for the rational allocation of medical resources and the promotion of intelligent medical treatment. ",
doi="10.2196/19055",
url="https://medinform.jmir.org/2021/4/e19055",
url="http://www.ncbi.nlm.nih.gov/pubmed/33830067"
}


@Article{info:doi/10.2196/19110,
author="Chen, Yulong
and Du, Jianxia
and Sun, Xiao
and Li, Qiancheng
and Qin, Ming
and Xiao, Qian
and Bryan, Mark",
title="Treatment of Left Ventricular Circulation Disorder: Application of Echocardiography Information Data Monitoring",
journal="JMIR Med Inform",
year="2020",
month="Sep",
day="16",
volume="8",
number="9",
pages="e19110",
keywords="echocardiography information",
keywords="data monitoring",
keywords="hemodynamics",
keywords="left ventricular circulation disorder",
abstract="Background: Cardiac hypertrophy induced by pressure overload is one of the important causes of heart failure and sudden cardiac death. At present, there are few studies on the outcome of left ventricular hypertrophy and left ventricular function after complete pressure load removal. Objective: This study aims to better simulate the changes of left ventricular structure and function during the process of left ventricular pressure overload and deloading, and to explore the application of echocardiography in it. Methods: In this study, healthy male (BALB/C) mice were used as research objects to establish an ascending aorta constriction model, to carry out echocardiographic and hemodynamic examinations, to establish an ascending aorta deconstriction model in mice, and to carry out echocardiographic and hemodynamic examinations. Results: Compared with the sham operation group, the left ventricular end-systolic diameter (LVESD), left ventricular end-diastolic diameter (LVEDD), interventricular septal (IVS), and left ventricular posterior wall (LVPW) in the constriction operation group were significantly increased (P=.02, P=.02, P=.02, and P=.02, respectively). LVESD, LVEDD, IVS, and LVPW in the early and late constriction groups were significantly decreased, and the degree of decrease in the early group was greater than that in the late group; compared with the sham operation group, left ventricular diastolic pressure in the constriction operation group increased significantly at 9 and 15 weeks after operation (P=.03). Left ventricular systolic pressure at 15 weeks after operation decreased to a certain extent but was higher than that of the sham operation group (P=.02). The maximal rate of the increase of left ventricular pressure at 3 weeks, 9 weeks, and 15 weeks after operation decreased significantly (P=.03, P=.02, and P=.02, respectively). Conclusions: In this study, the ascending aorta coarctation model and descending aorta coarctation model were successfully established, which verifies the value of echocardiography information data monitoring in the treatment of left ventricular circulation disorders and the evaluation of surgical treatment. ",
doi="10.2196/19110",
url="http://medinform.jmir.org/2020/9/e19110/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32936076"
}


@Article{info:doi/10.2196/19428,
author="Gong, Liheng
and Zhang, Xiao
and Li, Ling",
title="An Artificial Intelligence Fusion Model for Cardiac Emergency Decision Making: Application and Robustness Analysis",
journal="JMIR Med Inform",
year="2020",
month="Jul",
day="27",
volume="8",
number="7",
pages="e19428",
keywords="artificial intelligence",
keywords="fusion model",
keywords="cardiac emergency",
keywords="robustness",
abstract="Background: During cardiac emergency medical treatment, reducing the incidence of avoidable adverse events, ensuring the safety of patients, and generally improving the quality and efficiency of medical treatment have been important research topics in theoretical and practical circles. Objective: This paper examines the robustness of the decision-making reasoning process from the overall perspective of the cardiac emergency medical system. Methods: The principle of robustness was introduced into our study on the quality and efficiency of cardiac emergency decision making. We propose the concept of robustness for complex medical decision making by targeting the problem of low reasoning efficiency and accuracy in cardiac emergency decision making. The key bottlenecks such as anti-interference capability, fault tolerance, and redundancy were studied. The rules of knowledge acquisition and transfer in the decision-making process were systematically analyzed to reveal the core role of knowledge reasoning. Results: The robustness threshold method was adopted to construct the robustness criteria group of the system, and the fusion and coordination mechanism was realized through information entropy, information gain, and mutual information methods. Conclusions: A set of fusion models and robust threshold methods such as the R2CMIFS (treatment mode of fibroblastic sarcoma) model and the RTCRF (clinical trial observation mode) model were proposed. Our study enriches the theoretical research on robustness in this field. ",
doi="10.2196/19428",
url="https://medinform.jmir.org/2020/7/e19428",
url="http://www.ncbi.nlm.nih.gov/pubmed/32716305"
}


@Article{info:doi/10.2196/19202,
author="Du, Lin",
title="Medical Emergency Resource Allocation Model in Large-Scale Emergencies Based on Artificial Intelligence: Algorithm Development",
journal="JMIR Med Inform",
year="2020",
month="Jun",
day="25",
volume="8",
number="6",
pages="e19202",
keywords="medical emergency",
keywords="resource allocation model",
keywords="distribution model",
keywords="large-scale emergencies",
keywords="artificial intelligence",
abstract="Background: Before major emergencies occur, the government needs to prepare various emergency supplies in advance. To do this, it should consider the coordinated storage of different types of materials while ensuring that emergency materials are not missed or superfluous. Objective: This paper aims to improve the dispatch and transportation efficiency of emergency materials under a model in which the government makes full use of Internet of Things technology and artificial intelligence technology. Methods: The paper established a model for emergency material preparation and dispatch based on queueing theory and further established a workflow system for emergency material preparation, dispatch, and transportation based on a Petri net, resulting in a highly efficient emergency material preparation and dispatch simulation system framework. Results: A decision support platform was designed to integrate all the algorithms and principles proposed. Conclusions: The resulting framework can effectively coordinate the workflow of emergency material preparation and dispatch, helping to shorten the total time of emergency material preparation, dispatch, and transportation. ",
doi="10.2196/19202",
url="http://medinform.jmir.org/2020/6/e19202/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32584262"
}


@Article{info:doi/10.2196/18677,
author="Fu, Weifeng",
title="Application of an Isolated Word Speech Recognition System in the Field of Mental Health Consultation: Development and Usability Study",
journal="JMIR Med Inform",
year="2020",
month="Jun",
day="3",
volume="8",
number="6",
pages="e18677",
keywords="speech recognition",
keywords="isolated words",
keywords="mental health",
keywords="small vocabulary",
keywords="HMM",
keywords="hidden Markov model",
keywords="programming",
abstract="Background: Speech recognition is a technology that enables machines to understand human language. Objective: In this study, speech recognition of isolated words from a small vocabulary was applied to the field of mental health counseling. Methods: A software platform was used to establish a human-machine chat for psychological counselling. The software uses voice recognition technology to decode the user's voice information. The software system analyzes and processes the user's voice information according to many internal related databases, and then gives the user accurate feedback. For users who need psychological treatment, the system provides them with psychological education. Results: The speech recognition system included features such as speech extraction, endpoint detection, feature value extraction, training data, and speech recognition. Conclusions: The Hidden Markov Model was adopted, based on multithread programming under a VC2005 compilation environment, to realize the parallel operation of the algorithm and improve the efficiency of speech recognition. After the design was completed, simulation debugging was performed in the laboratory. The experimental results showed that the designed program met the basic requirements of a speech recognition system. ",
doi="10.2196/18677",
url="https://medinform.jmir.org/2020/6/e18677",
url="http://www.ncbi.nlm.nih.gov/pubmed/32384054"
}


@Article{info:doi/10.2196/18682,
author="Liu, Yue",
title="Artificial Intelligence--Based Neural Network for the Diagnosis of Diabetes: Model Development",
journal="JMIR Med Inform",
year="2020",
month="May",
day="27",
volume="8",
number="5",
pages="e18682",
keywords="artificial intelligence",
keywords="diabetes",
keywords="neural network",
abstract="Background: The incidence of diabetes is increasing in China, and its impact on national health cannot be ignored. Smart medicine is a medical model that uses technology to assist the diagnosis and treatment of disease. Objective: The aim of this paper was to apply artificial intelligence (AI) in the diagnosis of diabetes. Methods: We established an AI diagnostic model in the MATLAB software platform based on a backpropagation neural network by collecting data for the cases of integration and extraction and selecting an input feature vector. Based on this diagnostic model, using an intelligent combination of the LabVIEW development platform and the MATLAB software-designed diabetes diagnosis system with user data, we called the neural network diagnostic module to correctly diagnose diabetes. Results: Compared to conventional diagnostic procedures, the system can effectively improve diagnostic efficiency and save time for physicians. Conclusions: The development of AI applications has utility to aid diabetes diagnosis. ",
doi="10.2196/18682",
url="http://medinform.jmir.org/2020/5/e18682/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32459183"
}


@Article{info:doi/10.2196/18664,
author="Huang, Yihao
and Li, Mingtao",
title="Optimization of Precontrol Methods and Analysis of a Dynamic Model for Brucellosis: Model Development and Validation",
journal="JMIR Med Inform",
year="2020",
month="May",
day="27",
volume="8",
number="5",
pages="e18664",
keywords="brucellosis",
keywords="dynamic model",
keywords="protective measures",
keywords="precontrol methods",
abstract="Background: Brucella is a gram-negative, nonmotile bacterium without a capsule. The infection scope of Brucella is wide. The major source of infection is mammals such as cattle, sheep, goats, pigs, and dogs. Currently, human beings do not transmit Brucella to each other. When humans eat Brucella-contaminated food or contact animals or animal secretions and excretions infected with Brucella, they may develop brucellosis. Although brucellosis does not originate in humans, its diagnosis and cure are very difficult; thus, it has a huge impact on humans. Even with the rapid development of medical science, brucellosis is still a major problem for Chinese people. Currently, the number of patients with brucellosis in China is 100,000 per year. In addition, due to the ongoing improvement in the living standards of Chinese people, the demand for meat products has gradually increased, and increased meat transactions have greatly promoted the spread of brucellosis. Therefore, many researchers are concerned with investigating the transmission of Brucella as well as the diagnosis and treatment of  brucellosis.Mathematical models have become an important tool for the study of infectious diseases. Mathematical models can reflect the spread of infectious diseases and be used to study the effect of different inhibition methods on infectious diseases. The effect of control measures to obtain effective suppression can provide theoretical support for the suppression of infectious diseases. Therefore, it is the objective of this study to build a suitable mathematical model for brucellosis infection. Objective: We aimed to study the optimized precontrol methods of brucellosis using a dynamic threshold--based microcomputer model and to provide critical theoretical support for the prevention and control of brucellosis. Methods: By studying the transmission characteristics of Brucella and building a Brucella transmission model, the precontrol methods were designed and presented to the key populations (Brucella-susceptible populations). We investigated the utilization of protective tools by the key populations before and after precontrol methods. Results: An improvement in the amount of glove-wearing was evident and significant (P<.001), increasing from 51.01\% before the precontrol methods to 66.22\% after the precontrol methods, an increase of 15.21\%. However, the amount of hat-wearing did not improve significantly (P=.95). Hat-wearing among the key populations increased from 57.3\% before the precontrol methods to 58.6\% after the precontrol methods, an increase of 1.3\%. Conclusions: By demonstrating the optimized precontrol methods for a brucellosis model built on a dynamic threshold--based microcomputer model, this study provides theoretical support for the suppression of Brucella and the improved usage of protective measures by key populations. ",
doi="10.2196/18664",
url="https://medinform.jmir.org/2020/5/e18664",
url="http://www.ncbi.nlm.nih.gov/pubmed/32459180"
}


@Article{info:doi/10.2196/18627,
author="Huang, Yihao
and Li, Mingtao",
title="Application of a Mathematical Model in Determining the Spread of the Rabies Virus: Simulation Study",
journal="JMIR Med Inform",
year="2020",
month="May",
day="27",
volume="8",
number="5",
pages="e18627",
keywords="rabies",
keywords="computer model",
keywords="suppression measures",
keywords="basic reproductive number",
abstract="Background: Rabies is an acute infectious disease of the central nervous system caused by the rabies virus. The mortality rate of rabies is almost 100\%. For some countries with poor sanitation, the spread of rabies among dogs is very serious. Objective: The objective of this paper was to study the ecological transmission mode of rabies to make theoretical contributions to the suppression of rabies in China. Methods: A mathematical model of the transmission mode of rabies was constructed using relevant data from the literature and officially published figures in China. Using this model, we fitted the data of the number of patients with rabies and predicted the future number of patients with rabies. In addition, we studied the effectiveness of different rabies suppression measures. Results: The results of the study indicated that the number of people infected with rabies will rise in the first stage, and then decrease. The model forecasted that in about 10 years, the number of rabies cases will be controlled within a relatively stable range. According to the prediction results of the model reported in this paper, the number of rabies cases will eventually plateau at approximately 500 people every year. Relatively effective rabies suppression measures include controlling the birth rate of domestic and wild dogs as well as increasing the level of rabies immunity in domestic dogs. Conclusions: The basic reproductive number of rabies in China is still greater than 1. That is, China currently has insufficient measures to control rabies. The research on the transmission mode of rabies and control measures in this paper can provide theoretical support for rabies control in China. ",
doi="10.2196/18627",
url="http://medinform.jmir.org/2020/5/e18627/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32459185"
}