scholarly journals Artificial Intelligence–Based Traditional Chinese Medicine Assistive Diagnostic System: Validation Study

10.2196/17608 ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. e17608 ◽  
Author(s):  
Hong Zhang ◽  
Wandong Ni ◽  
Jing Li ◽  
Jiajun Zhang
Keyword(s):  
Artificial Intelligence ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Electronic Health Record ◽  
Diagnostic System ◽  
Prediction Method ◽  
Disease Type ◽  
Health Record ◽  
Data Set ◽  
Electronic Health

Background Artificial intelligence–based assistive diagnostic systems imitate the deductive reasoning process of a human physician in biomedical disease diagnosis and treatment decision making. While impressive progress in this area has been reported, most of the reported successes are applications of artificial intelligence in Western medicine. The application of artificial intelligence in traditional Chinese medicine has lagged mainly because traditional Chinese medicine practitioners need to perform syndrome differentiation as well as biomedical disease diagnosis before a treatment decision can be made. Syndrome, a concept unique to traditional Chinese medicine, is an abstraction of a variety of signs and symptoms. The fact that the relationship between diseases and syndromes is not one-to-one but rather many-to-many makes it very challenging for a machine to perform syndrome predictions. So far, only a handful of artificial intelligence–based assistive traditional Chinese medicine diagnostic models have been reported, and they are limited in application to a single disease-type. Objective The objective was to develop an artificial intelligence–based assistive diagnostic system capable of diagnosing multiple types of diseases that are common in traditional Chinese medicine, given a patient’s electronic health record notes. The system was designed to simultaneously diagnose the disease and produce a list of corresponding syndromes. Methods Unstructured freestyle electronic health record notes were processed by natural language processing techniques to extract clinical information such as signs and symptoms which were represented by named entities. Natural language processing used a recurrent neural network model called bidirectional long short-term memory network–conditional random forest. A convolutional neural network was then used to predict the disease-type out of 187 diseases in traditional Chinese medicine. A novel traditional Chinese medicine syndrome prediction method—an integrated learning model—was used to produce a corresponding list of probable syndromes. By following a majority-rule voting method, the integrated learning model for syndrome prediction can take advantage of four existing prediction methods (back propagation, random forest, extreme gradient boosting, and support vector classifier) while avoiding their respective weaknesses which resulted in a consistently high prediction accuracy. Results A data set consisting of 22,984 electronic health records from Guanganmen Hospital of the China Academy of Chinese Medical Sciences that were collected between January 1, 2017 and September 7, 2018 was used. The data set contained a total of 187 diseases that are commonly diagnosed in traditional Chinese medicine. The diagnostic system was designed to be able to detect any one of the 187 disease-types. The data set was partitioned into a training set, a validation set, and a testing set in a ratio of 8:1:1. Test results suggested that the proposed system had a good diagnostic accuracy and a strong capability for generalization. The disease-type prediction accuracies of the top one, top three, and top five were 80.5%, 91.6%, and 94.2%, respectively. Conclusions The main contributions of the artificial intelligence–based traditional Chinese medicine assistive diagnostic system proposed in this paper are that 187 commonly known traditional Chinese medicine diseases can be diagnosed and a novel prediction method called an integrated learning model is demonstrated. This new prediction method outperformed all four existing methods in our preliminary experimental results. With further improvement of the algorithms and the availability of additional electronic health record data, it is expected that a wider range of traditional Chinese medicine disease-types could be diagnosed and that better diagnostic accuracies could be achieved.

scholarly journals Artificial Intelligence–Based Traditional Chinese Medicine Assistive Diagnostic System: Validation Study (Preprint)

2019 ◽  
Author(s):  
Hong Zhang ◽  
Wandong Ni ◽  
Jing Li ◽  
Jiajun Zhang
Keyword(s):  
Artificial Intelligence ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Electronic Health Record ◽  
Diagnostic System ◽  
Prediction Method ◽  
Disease Type ◽  
Health Record ◽  
Data Set ◽  
Electronic Health

BACKGROUND Artificial intelligence–based assistive diagnostic systems imitate the deductive reasoning process of a human physician in biomedical disease diagnosis and treatment decision making. While impressive progress in this area has been reported, most of the reported successes are applications of artificial intelligence in Western medicine. The application of artificial intelligence in traditional Chinese medicine has lagged mainly because traditional Chinese medicine practitioners need to perform syndrome differentiation as well as biomedical disease diagnosis before a treatment decision can be made. Syndrome, a concept unique to traditional Chinese medicine, is an abstraction of a variety of signs and symptoms. The fact that the relationship between diseases and syndromes is not one-to-one but rather many-to-many makes it very challenging for a machine to perform syndrome predictions. So far, only a handful of artificial intelligence–based assistive traditional Chinese medicine diagnostic models have been reported, and they are limited in application to a single disease-type. OBJECTIVE The objective was to develop an artificial intelligence–based assistive diagnostic system capable of diagnosing multiple types of diseases that are common in traditional Chinese medicine, given a patient’s electronic health record notes. The system was designed to simultaneously diagnose the disease and produce a list of corresponding syndromes. METHODS Unstructured freestyle electronic health record notes were processed by natural language processing techniques to extract clinical information such as signs and symptoms which were represented by named entities. Natural language processing used a recurrent neural network model called bidirectional long short-term memory network–conditional random forest. A convolutional neural network was then used to predict the disease-type out of 187 diseases in traditional Chinese medicine. A novel traditional Chinese medicine syndrome prediction method—an integrated learning model—was used to produce a corresponding list of probable syndromes. By following a majority-rule voting method, the integrated learning model for syndrome prediction can take advantage of four existing prediction methods (back propagation, random forest, extreme gradient boosting, and support vector classifier) while avoiding their respective weaknesses which resulted in a consistently high prediction accuracy. RESULTS A data set consisting of 22,984 electronic health records from Guanganmen Hospital of the China Academy of Chinese Medical Sciences that were collected between January 1, 2017 and September 7, 2018 was used. The data set contained a total of 187 diseases that are commonly diagnosed in traditional Chinese medicine. The diagnostic system was designed to be able to detect any one of the 187 disease-types. The data set was partitioned into a training set, a validation set, and a testing set in a ratio of 8:1:1. Test results suggested that the proposed system had a good diagnostic accuracy and a strong capability for generalization. The disease-type prediction accuracies of the top one, top three, and top five were 80.5%, 91.6%, and 94.2%, respectively. CONCLUSIONS The main contributions of the artificial intelligence–based traditional Chinese medicine assistive diagnostic system proposed in this paper are that 187 commonly known traditional Chinese medicine diseases can be diagnosed and a novel prediction method called an integrated learning model is demonstrated. This new prediction method outperformed all four existing methods in our preliminary experimental results. With further improvement of the algorithms and the availability of additional electronic health record data, it is expected that a wider range of traditional Chinese medicine disease-types could be diagnosed and that better diagnostic accuracies could be achieved.

AIRA, an Artificial Intelligence Realistic Assistant that will save your time, a before-and-after study during Pandemic (Preprint)

2021 ◽  
Author(s):  
Kaio Bin ◽  
Adler Araújo Ribeiro Melo ◽  
José Guilherme Franco Da Rocha ◽  
Renata Pivi De Almeida ◽  
Vilson Cobello Junior ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Electronic Health Record ◽  
Medical Care ◽  
Health Screening ◽  
Urgent Care ◽  
Health Record ◽  
Before And After ◽  
Electronic Health ◽  
The Impact ◽  
Before And After Study

BACKGROUND AIRA is an AI designed to reduce the time that doctors dedicate filling out EHR, winner of the first edition of MIT Hacking Medicine held in Brazil in 2020. As a proof of concept, AIRA was implemented in administrative process before its application in a medical process. OBJECTIVE The aim of the study is to determinate the impact of AIRA by eliminating the Medical Care Registration (MCR) on Electronic Health Record (EHR) by Administrative Officer. METHODS This is a comparative before-and-after study following the guidance “Evaluating digital health products” from Public Health England. An Artificial Intelligence named AIRA was created and implemented at CEAC (Employee Attention Center) from HCFMUSP. A total of 25,507 attendances were evaluated along 2020 for determinate AIRA´s impact. Total of MCR, time of health screening and time between the end of the screening and the beginning of medical care, were compared in the pre and post AIRA periods. RESULTS AIRA eliminated the need for Medical Care Registration by Administrative Officer in 92% (p<0.0001). The nurse´s time of health screening increased 16% (p<0.0001) during the implementation, and 13% (p<0.0001) until three months after the implementation, but reduced in 4% three months after implementation (p<0.0001). The mean and median total time to Medical Care after the nurse’ Screening was decreased in 30% (p<0.0001) and 41% (p<0.0001) respectively. CONCLUSIONS The implementation of AIRA reduced the time to medical care in an urgent care after the nurse´ screening, by eliminating non-value-added activity the Medical Care Registration on Electronic Health Record (EHR) by Administrative Officer.

scholarly journals Utilization and Validity of the Dental Diagnostic System over Time in Academic and Private Practice

2018 ◽  
Vol 4 (2) ◽  
pp. 143-150 ◽  
Author(s):  
A. Yansane ◽  
O. Tokede ◽  
J. White ◽  
J. Etolue ◽  
L. McClellan ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Diagnostic System ◽  
Fold Increase ◽  
Dental Students ◽  
Primary Objective ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Resultant Data ◽  
Electronic Health ◽  
Over Time

Introduction: To fill the void created by insufficient dental terminologies, a multi-institutional workgroup was formed among members of the Consortium for Oral Health Research and Informatics to develop the Dental Diagnostic System (DDS) in 2009. The adoption of dental diagnosis terminologies by providers must be accompanied by rigorous usability and validity assessments to ensure their effectiveness in practice. Objectives: The primary objective of this study was to describe the utilization and correct use of the DDS over a 4-y period. Methods: Electronic health record data were amassed from 2013 to 2016 where diagnostic terms and Current Dental Terminology procedure code pairs were adjudicated by calibrated dentists. With the resultant data, we report on the 4-y utilization and validity of the DDS at 5 dental institutions. Utilization refers to the proportion of instances that diagnoses are documented in a structured format, and validity is defined as the frequency of valid pairs divided by the number of all treatment codes entered. Results: Nearly 10 million procedures ( n = 9,946,975) were documented at the 5 participating institutions between 2013 and 2016. There was a 1.5-fold increase in the number of unique diagnoses documented during the 4-y period. The utilization and validity proportions of the DDS had statistically significant increases from 2013 to 2016 ( P < 0.0001). Academic dental sites were more likely to document diagnoses associated with orthodontic and restorative procedures, while the private dental site was equally likely to document diagnoses associated with all procedures. Overall, the private dental site had significantly higher utilization and validity proportions than the academic dental sites. Conclusion: The results demonstrate an improvement in utilization and validity of the DDS terminology over time. These findings also yield insight into the factors that influence the usability, adoption, and validity of dental terminologies, raising the need for more focused training of dental students. Knowledge Transfer Statement: Ensuring that providers use standardized methods for documentation of diagnoses represents a challenge within dentistry. The results of this study can be used by clinicians when evaluating the utility of diagnostic terminologies embedded within the electronic health record.

scholarly journals Development of Electronic Health Record for Chinese Medicine eHR(CM) Sharing System in Hong Kong

2015 ◽  
Vol 4 (1) ◽  
pp. 115
Author(s):  
Ruth LEUNG ◽  
Kam Fung CHUNG ◽  
Veronica LI ◽  
Rita CHEUNG ◽  
Corey LAM ◽  
...  
Keyword(s):  
Hong Kong ◽  
Chinese Medicine ◽  
Electronic Health Record ◽  
Health Record ◽  
Electronic Health

Hysterectomy utilization among newly diagnosed endometrial cancer patients by body mass index category: A real-world analysis using a claims and electronic health record database.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e17112-e17112
Author(s):  
Debra E. Irwin ◽  
Ellen Thiel
Keyword(s):  
Endometrial Cancer ◽  
Electronic Health Record ◽  
Real World ◽  
Patient Population ◽  
Morbidly Obese ◽  
Health Record ◽  
Newly Diagnosed ◽  
Data Set ◽  
Electronic Health

e17112 Background: For endometrial cancer (EC), laparoscopic hysterectomy (LH) is an effective, minimally invasive surgical treatment; however, this approach may not be recommended for obese patients due to increased risk for complications. Methods: This retrospective study utilized insurance claims linked to electronic health record (EHR) data contained in the IBM MarketScan Explorys Claims-EHR Data Set. Newly diagnosed EC patients (1/1/2007 - 6/30/2017) with continuous enrollment during a 12-month baseline and 6-month follow-up period were selected. Patients were stratified into four BMI subgroups based on baseline BMI on the EHR: normal or underweight (BMI < 25), overweight (BMI 25- < 30), obese (BMI 30- < 40), morbidly obese (BMI > 40), and were required to have had a hysterectomy within the follow-up period. Emergency room visits and rehospitalization within 30 days of hysterectomy were measured. Results: A total of 1,090 newly-diagnosed EC patients met the selection criteria, of whom, 16% were normal/underweight, 19% were overweight, 39% were obese, and 26% were morbidly obese. The proportion of patients receiving LH increased as BMI category increased (Table 1). Among those with LH between 6% and 15% had an ER visit or rehospitalization in 30 days, and rates were higher among other hysterectomy modalities. Conclusions: This real-world analysis shows that LH is utilized in a high proportion of morbidly obese EC patients, despite that it is frequently deemed infeasible in this patient population. Although the rate of ER visits and rehospitalization is lower among LH patients than those undergoing traditional hysterectomy across all BMI strata, further research is needed to determine the optimal patient population to receive LH.[Table: see text]

scholarly journals Machine Learning Electronic Health Record Identification of Patients with Rheumatoid Arthritis: Algorithm Pipeline Development and Validation Study (Preprint)

2020 ◽  
Author(s):  
Tjardo D Maarseveen ◽  
Timo Meinderink ◽  
Marcel J T Reinders ◽  
Johannes Knitza ◽  
Tom W J Huizinga ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Machine Learning ◽  
Electronic Health Record ◽  
Support Vector ◽  
Free Text ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Data Set ◽  
Record Data ◽  
Electronic Health

BACKGROUND Financial codes are often used to extract diagnoses from electronic health records. This approach is prone to false positives. Alternatively, queries are constructed, but these are highly center and language specific. A tantalizing alternative is the automatic identification of patients by employing machine learning on format-free text entries. OBJECTIVE The aim of this study was to develop an easily implementable workflow that builds a machine learning algorithm capable of accurately identifying patients with rheumatoid arthritis from format-free text fields in electronic health records. METHODS Two electronic health record data sets were employed: Leiden (n=3000) and Erlangen (n=4771). Using a portion of the Leiden data (n=2000), we compared 6 different machine learning methods and a naïve word-matching algorithm using 10-fold cross-validation. Performances were compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPRC), and F1 score was used as the primary criterion for selecting the best method to build a classifying algorithm. We selected the optimal threshold of positive predictive value for case identification based on the output of the best method in the training data. This validation workflow was subsequently applied to a portion of the Erlangen data (n=4293). For testing, the best performing methods were applied to remaining data (Leiden n=1000; Erlangen n=478) for an unbiased evaluation. RESULTS For the Leiden data set, the word-matching algorithm demonstrated mixed performance (AUROC 0.90; AUPRC 0.33; F1 score 0.55), and 4 methods significantly outperformed word-matching, with support vector machines performing best (AUROC 0.98; AUPRC 0.88; F1 score 0.83). Applying this support vector machine classifier to the test data resulted in a similarly high performance (F1 score 0.81; positive predictive value [PPV] 0.94), and with this method, we could identify 2873 patients with rheumatoid arthritis in less than 7 seconds out of the complete collection of 23,300 patients in the Leiden electronic health record system. For the Erlangen data set, gradient boosting performed best (AUROC 0.94; AUPRC 0.85; F1 score 0.82) in the training set, and applied to the test data, resulted once again in good results (F1 score 0.67; PPV 0.97). CONCLUSIONS We demonstrate that machine learning methods can extract the records of patients with rheumatoid arthritis from electronic health record data with high precision, allowing research on very large populations for limited costs. Our approach is language and center independent and could be applied to any type of diagnosis. We have developed our pipeline into a universally applicable and easy-to-implement workflow to equip centers with their own high-performing algorithm. This allows the creation of observational studies of unprecedented size covering different countries for low cost from already available data in electronic health record systems.

scholarly journals Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review

2020 ◽  
Vol 27 (7) ◽  
pp. 1173-1185 ◽  
Author(s):  
Seyedeh Neelufar Payrovnaziri ◽  
Zhaoyi Chen ◽  
Pablo Rengifo-Moreno ◽  
Tim Miller ◽  
Jiang Bian ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Electronic Health Record ◽  
Scoping Review ◽  
Real World ◽  
Research Trend ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Explainable Artificial Intelligence ◽  
Record Data ◽  
Electronic Health

Abstract Objective To conduct a systematic scoping review of explainable artificial intelligence (XAI) models that use real-world electronic health record data, categorize these techniques according to different biomedical applications, identify gaps of current studies, and suggest future research directions. Materials and Methods We searched MEDLINE, IEEE Xplore, and the Association for Computing Machinery (ACM) Digital Library to identify relevant papers published between January 1, 2009 and May 1, 2019. We summarized these studies based on the year of publication, prediction tasks, machine learning algorithm, dataset(s) used to build the models, the scope, category, and evaluation of the XAI methods. We further assessed the reproducibility of the studies in terms of the availability of data and code and discussed open issues and challenges. Results Forty-two articles were included in this review. We reported the research trend and most-studied diseases. We grouped XAI methods into 5 categories: knowledge distillation and rule extraction (N = 13), intrinsically interpretable models (N = 9), data dimensionality reduction (N = 8), attention mechanism (N = 7), and feature interaction and importance (N = 5). Discussion XAI evaluation is an open issue that requires a deeper focus in the case of medical applications. We also discuss the importance of reproducibility of research work in this field, as well as the challenges and opportunities of XAI from 2 medical professionals’ point of view. Conclusion Based on our review, we found that XAI evaluation in medicine has not been adequately and formally practiced. Reproducibility remains a critical concern. Ample opportunities exist to advance XAI research in medicine.

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