scholarly journals Model-Based Reasoning of Clinical Diagnosis in Integrative Medicine: Real-World Methodological Study of Electronic Medical Records and Natural Language Processing Methods

10.2196/23082 ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. e23082
Author(s):  
Wenye Geng ◽  
Xuanfeng Qin ◽  
Tao Yang ◽  
Zhilei Cong ◽  
Zhuo Wang ◽  
...  
Keyword(s):  
Sample Size ◽  
Integrative Medicine ◽  
Language Processing ◽  
Medical Records ◽  
Lung Diseases ◽  
High Performance ◽  
Data Set ◽  
Pattern Type ◽  
Pattern Diagnosis ◽  
Syndrome Pattern

Background Integrative medicine is a form of medicine that combines practices and treatments from alternative medicine with conventional medicine. The diagnosis in integrative medicine involves the clinical diagnosis based on modern medicine and syndrome pattern diagnosis. Electronic medical records (EMRs) are the systematized collection of patients health information stored in a digital format that can be shared across different health care settings. Although syndrome and sign information or relative information can be extracted from the EMR and content texts can be mapped to computability vectors using natural language processing techniques, application of artificial intelligence techniques to support physicians in medical practices remains a major challenge. Objective The purpose of this study was to investigate model-based reasoning (MBR) algorithms for the clinical diagnosis in integrative medicine based on EMRs and natural language processing. We also estimated the associations among the factors of sample size, number of syndrome pattern type, and diagnosis in modern medicine using the MBR algorithms. Methods A total of 14,075 medical records of clinical cases were extracted from the EMRs as the development data set, and an external test data set consisting of 1000 medical records of clinical cases was extracted from independent EMRs. MBR methods based on word embedding, machine learning, and deep learning algorithms were developed for the automatic diagnosis of syndrome pattern in integrative medicine. MBR algorithms combining rule-based reasoning (RBR) were also developed. A standard evaluation metrics consisting of accuracy, precision, recall, and F1 score was used for the performance estimation of the methods. The association analyses were conducted on the sample size, number of syndrome pattern type, and diagnosis of lung diseases with the best algorithms. Results The Word2Vec convolutional neural network (CNN) MBR algorithms showed high performance (accuracy of 0.9586 in the test data set) in the syndrome pattern diagnosis of lung diseases. The Word2Vec CNN MBR combined with RBR also showed high performance (accuracy of 0.9229 in the test data set). The diagnosis of lung diseases could enhance the performance of the Word2Vec CNN MBR algorithms. Each group sample size and syndrome pattern type affected the performance of these algorithms. Conclusions The MBR methods based on Word2Vec and CNN showed high performance in the syndrome pattern diagnosis of lung diseases in integrative medicine. The parameters of each group’s sample size, syndrome pattern type, and diagnosis of lung diseases were associated with the performance of the methods. Trial Registration ClinicalTrials.gov NCT03274908; https://clinicaltrials.gov/ct2/show/NCT03274908

scholarly journals Model-Based Reasoning of Clinical Diagnosis in Integrative Medicine: Real-World Methodological Study of Electronic Medical Records and Natural Language Processing Methods (Preprint)

2020 ◽  
Author(s):  
Wenye Geng ◽  
Xuanfeng Qin ◽  
Tao Yang ◽  
Zhilei Cong ◽  
Zhuo Wang ◽  
...  
Keyword(s):  
Sample Size ◽  
Integrative Medicine ◽  
Language Processing ◽  
Medical Records ◽  
Lung Diseases ◽  
High Performance ◽  
Data Set ◽  
Pattern Type ◽  
Pattern Diagnosis ◽  
Syndrome Pattern

BACKGROUND Integrative medicine is a form of medicine that combines practices and treatments from alternative medicine with conventional medicine. The diagnosis in integrative medicine involves the clinical diagnosis based on modern medicine and syndrome pattern diagnosis. Electronic medical records (EMRs) are the systematized collection of patients health information stored in a digital format that can be shared across different health care settings. Although syndrome and sign information or relative information can be extracted from the EMR and content texts can be mapped to computability vectors using natural language processing techniques, application of artificial intelligence techniques to support physicians in medical practices remains a major challenge. OBJECTIVE The purpose of this study was to investigate model-based reasoning (MBR) algorithms for the clinical diagnosis in integrative medicine based on EMRs and natural language processing. We also estimated the associations among the factors of sample size, number of syndrome pattern type, and diagnosis in modern medicine using the MBR algorithms. METHODS A total of 14,075 medical records of clinical cases were extracted from the EMRs as the development data set, and an external test data set consisting of 1000 medical records of clinical cases was extracted from independent EMRs. MBR methods based on word embedding, machine learning, and deep learning algorithms were developed for the automatic diagnosis of syndrome pattern in integrative medicine. MBR algorithms combining rule-based reasoning (RBR) were also developed. A standard evaluation metrics consisting of accuracy, precision, recall, and F1 score was used for the performance estimation of the methods. The association analyses were conducted on the sample size, number of syndrome pattern type, and diagnosis of lung diseases with the best algorithms. RESULTS The Word2Vec convolutional neural network (CNN) MBR algorithms showed high performance (accuracy of 0.9586 in the test data set) in the syndrome pattern diagnosis of lung diseases. The Word2Vec CNN MBR combined with RBR also showed high performance (accuracy of 0.9229 in the test data set). The diagnosis of lung diseases could enhance the performance of the Word2Vec CNN MBR algorithms. Each group sample size and syndrome pattern type affected the performance of these algorithms. CONCLUSIONS The MBR methods based on Word2Vec and CNN showed high performance in the syndrome pattern diagnosis of lung diseases in integrative medicine. The parameters of each group’s sample size, syndrome pattern type, and diagnosis of lung diseases were associated with the performance of the methods. CLINICALTRIAL ClinicalTrials.gov NCT03274908; https://clinicaltrials.gov/ct2/show/NCT03274908

scholarly journals Model-based reasoning methods for diagnosis in integrative medicine based on electronic medical records and natural language processing

2020 ◽  
Author(s):  
Wenye Geng ◽  
Xuanfeng Qin ◽  
Zhuo Wang ◽  
Qing Kong ◽  
Zihui Tang ◽  
...  
Keyword(s):  
Sample Size ◽  
Integrative Medicine ◽  
Language Processing ◽  
Medical Records ◽  
Lung Diseases ◽  
High Performance ◽  
Test Dataset ◽  
Pattern Type ◽  
Pattern Diagnosis ◽  
Syndrome Pattern

Background: This study aimed to investigate model-based reasoning (MBR) algorithms for the diagnosis of integrative medicine based on electronic medical records (EMRs) and natural language processing. Methods: A total of 14,075 medical records of clinical cases were extracted from the EMRs as the development dataset, and an external test dataset consisting of 1,000 medical records of clinical cases was extracted from independent EMRs. MBR methods based on word embedding, machine learning, and deep learning algorithms were developed for the automatic diagnosis of syndrome pattern in integrative medicine. MBR algorithms combining rule-based reasoning (RBR) were also developed. A standard evaluation metrics consisting of accuracy, precision, recall, and F1 score were used for the performance estimation of the methods. The association analyses were conducted on the sample size, number of syndrome pattern type, and diagnosis of lung diseases with the best algorithms. Results: The Word2Vec CNN MBR algorithms showed high performance (accuracy of 0.9586 in the test dataset) in the syndrome pattern diagnosis. The Word2Vec CNN MBR combined with RBR also showed high performance (accuracy of 0.9229 in the test dataset). The diagnosis of lung diseases could enhance the performance of the Word2Vec CNN MBR algorithms. Each group sample size and syndrome pattern type affected the performance of these algorithms. Conclusion: The MBR methods based on Word2Vec and CNN showed high performance in the syndrome pattern diagnosis in integrative medicine in lung diseases. The parameters of each group sample size, syndrome pattern type, and diagnosis of lung diseases were associated with the performance of the methods.

scholarly journals Predicting Inpatient Falls Using Natural Language Processing of Nursing Records Obtained From Japanese Electronic Medical Records: Case-Control Study

10.2196/16970 ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. e16970 ◽  
Author(s):  
Hayao Nakatani ◽  
Masatoshi Nakao ◽  
Hidefumi Uchiyama ◽  
Hiroyoshi Toyoshiba ◽  
Chikayuki Ochiai
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Health Care ◽  
Electronic Medical Records ◽  
Language Processing ◽  
Medical Records ◽  
Fall Risk ◽  
Data Set ◽  
Inpatient Falls ◽  
Risk Factors For Falls

Background Falls in hospitals are the most common risk factor that affects the safety of inpatients and can result in severe harm. Therefore, preventing falls is one of the most important areas of risk management for health care organizations. However, existing methods for predicting falls are laborious and costly. Objective The objective of this study is to verify whether hospital inpatient falls can be predicted through the analysis of a single input—unstructured nursing records obtained from Japanese electronic medical records (EMRs)—using a natural language processing (NLP) algorithm and machine learning. Methods The nursing records of 335 fallers and 408 nonfallers for a 12-month period were extracted from the EMRs of an acute care hospital and randomly divided into a learning data set and test data set. The former data set was subjected to NLP and machine learning to extract morphemes that contributed to separating fallers from nonfallers to construct a model for predicting falls. Then, the latter data set was used to determine the predictive value of the model using receiver operating characteristic (ROC) analysis. Results The prediction of falls using the test data set showed high accuracy, with an area under the ROC curve, sensitivity, specificity, and odds ratio of mean 0.834 (SD 0.005), mean 0.769 (SD 0.013), mean 0.785 (SD 0.020), and mean 12.27 (SD 1.11) for five independent experiments, respectively. The morphemes incorporated into the final model included many words closely related to known risk factors for falls, such as the use of psychotropic drugs, state of consciousness, and mobility, thereby demonstrating that an NLP algorithm combined with machine learning can effectively extract risk factors for falls from nursing records. Conclusions We successfully established that falls among hospital inpatients can be predicted by analyzing nursing records using an NLP algorithm and machine learning. Therefore, it may be possible to develop a fall risk monitoring system that analyzes nursing records daily and alerts health care professionals when the fall risk of an inpatient is increased.

scholarly journals DeGNServer: Deciphering Genome-Scale Gene Networks through High Performance Reverse Engineering Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Jun Li ◽  
Hairong Wei ◽  
Patrick Xuechun Zhao
Keyword(s):  
Reverse Engineering ◽  
Sample Size ◽  
Gene Networks ◽  
High Performance ◽  
Large Scale ◽  
Small Sample ◽  
Biological Processes ◽  
Expression Data ◽  
Data Set ◽  
Genome Scale

Analysis of genome-scale gene networks (GNs) using large-scale gene expression data provides unprecedented opportunities to uncover gene interactions and regulatory networks involved in various biological processes and developmental programs, leading to accelerated discovery of novel knowledge of various biological processes, pathways and systems. The widely used context likelihood of relatedness (CLR) method based on the mutual information (MI) for scoring the similarity of gene pairs is one of the accurate methods currently available for inferring GNs. However, the MI-based reverse engineering method can achieve satisfactory performance only when sample size exceeds one hundred. This in turn limits their applications for GN construction from expression data set with small sample size. We developed a high performance web server, DeGNServer, to reverse engineering and decipher genome-scale networks. It extended the CLR method by integration of different correlation methods that are suitable for analyzing data sets ranging from moderate to large scale such as expression profiles with tens to hundreds of microarray hybridizations, and implemented all analysis algorithms using parallel computing techniques to infer gene-gene association at extraordinary speed. In addition, we integrated the SNBuilder and GeNa algorithms for subnetwork extraction and functional module discovery. DeGNServer is publicly and freely available online.

Parsing Noun Phrases in the Penn Treebank

2011 ◽  
Vol 37 (4) ◽  
pp. 753-809 ◽  
Author(s):  
David Vadas ◽  
James R. Curran
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Gold Standard ◽  
High Performance ◽  
Large Scale ◽  
Complex Structure ◽  
Noun Phrases ◽  
Simple Task ◽  
Data Set ◽  
Lexical Information

Noun phrases (nps) are a crucial part of natural language, and can have a very complex structure. However, this np structure is largely ignored by the statistical parsing field, as the most widely used corpus is not annotated with it. This lack of gold-standard data has restricted previous efforts to parse nps, making it impossible to perform the supervised experiments that have achieved high performance in so many Natural Language Processing (nlp) tasks. We comprehensively solve this problem by manually annotating np structure for the entire Wall Street Journal section of the Penn Treebank. The inter-annotator agreement scores that we attain dispel the belief that the task is too difficult, and demonstrate that consistent np annotation is possible. Our gold-standard np data is now available for use in all parsers. We experiment with this new data, applying the Collins (2003) parsing model, and find that its recovery of np structure is significantly worse than its overall performance. The parser's F-score is up to 5.69% lower than a baseline that uses deterministic rules. Through much experimentation, we determine that this result is primarily caused by a lack of lexical information. To solve this problem we construct a wide-coverage, large-scale np Bracketing system. With our Penn Treebank data set, which is orders of magnitude larger than those used previously, we build a supervised model that achieves excellent results. Our model performs at 93.8% F-score on the simple task that most previous work has undertaken, and extends to bracket longer, more complex nps that are rarely dealt with in the literature. We attain 89.14% F-score on this much more difficult task. Finally, we implement a post-processing module that brackets nps identified by the Bikel (2004) parser. Our np Bracketing model includes a wide variety of features that provide the lexical information that was missing during the parser experiments, and as a result, we outperform the parser's F-score by 9.04%. These experiments demonstrate the utility of the corpus, and show that many nlp applications can now make use of np structure.

scholarly journals Predicting Inpatient Falls Using Natural Language Processing of Nursing Records Obtained From Japanese Electronic Medical Records: Case-Control Study (Preprint)

2019 ◽  
Author(s):  
Hayao Nakatani ◽  
Masatoshi Nakao ◽  
Hidefumi Uchiyama ◽  
Hiroyoshi Toyoshiba ◽  
Chikayuki Ochiai
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Health Care ◽  
Electronic Medical Records ◽  
Language Processing ◽  
Medical Records ◽  
Fall Risk ◽  
Data Set ◽  
Inpatient Falls ◽  
Risk Factors For Falls

BACKGROUND Falls in hospitals are the most common risk factor that affects the safety of inpatients and can result in severe harm. Therefore, preventing falls is one of the most important areas of risk management for health care organizations. However, existing methods for predicting falls are laborious and costly. OBJECTIVE The objective of this study is to verify whether hospital inpatient falls can be predicted through the analysis of a single input—unstructured nursing records obtained from Japanese electronic medical records (EMRs)—using a natural language processing (NLP) algorithm and machine learning. METHODS The nursing records of 335 fallers and 408 nonfallers for a 12-month period were extracted from the EMRs of an acute care hospital and randomly divided into a learning data set and test data set. The former data set was subjected to NLP and machine learning to extract morphemes that contributed to separating fallers from nonfallers to construct a model for predicting falls. Then, the latter data set was used to determine the predictive value of the model using receiver operating characteristic (ROC) analysis. RESULTS The prediction of falls using the test data set showed high accuracy, with an area under the ROC curve, sensitivity, specificity, and odds ratio of mean 0.834 (SD 0.005), mean 0.769 (SD 0.013), mean 0.785 (SD 0.020), and mean 12.27 (SD 1.11) for five independent experiments, respectively. The morphemes incorporated into the final model included many words closely related to known risk factors for falls, such as the use of psychotropic drugs, state of consciousness, and mobility, thereby demonstrating that an NLP algorithm combined with machine learning can effectively extract risk factors for falls from nursing records. CONCLUSIONS We successfully established that falls among hospital inpatients can be predicted by analyzing nursing records using an NLP algorithm and machine learning. Therefore, it may be possible to develop a fall risk monitoring system that analyzes nursing records daily and alerts health care professionals when the fall risk of an inpatient is increased.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

scholarly journals Hugoniot Data and Equation of State Parameters for an Ultra-High Performance Concrete

2021 ◽  
Author(s):  
C. Sauer ◽  
F. Bagusat ◽  
M.-L. Ruiz-Ripoll ◽  
C. Roller ◽  
M. Sauer ◽  
...  
Keyword(s):  
Equation Of State ◽  
High Performance ◽  
High Performance Concrete ◽  
Applied Pressure ◽  
High Strength ◽  
Parameter Study ◽  
Ultra High Performance Concrete ◽  
Data Set ◽  
Shock Response ◽  
Particle Velocities

AbstractThis work aims at the characterization of a modern concrete material. For this purpose, we perform two experimental series of inverse planar plate impact (PPI) tests with the ultra-high performance concrete B4Q, using two different witness plate materials. Hugoniot data in the range of particle velocities from 180 to 840 m/s and stresses from 1.1 to 7.5 GPa is derived from both series. Within the experimental accuracy, they can be seen as one consistent data set. Moreover, we conduct corresponding numerical simulations and find a reasonably good agreement between simulated and experimentally obtained curves. From the simulated curves, we derive numerical Hugoniot results that serve as a homogenized, mean shock response of B4Q and add further consistency to the data set. Additionally, the comparison of simulated and experimentally determined results allows us to identify experimental outliers. Furthermore, we perform a parameter study which shows that a significant influence of the applied pressure dependent strength model on the derived equation of state (EOS) parameters is unlikely. In order to compare the current results to our own partially reevaluated previous work and selected recent results from literature, we use simulations to numerically extrapolate the Hugoniot results. Considering their inhomogeneous nature, a consistent picture emerges for the shock response of the discussed concrete and high-strength mortar materials. Hugoniot results from this and earlier work are presented for further comparisons. In addition, a full parameter set for B4Q, including validated EOS parameters, is provided for the application in simulations of impact and blast scenarios.

An approach for document retrieval using cluster-based inverted indexing

2021 ◽  
pp. 016555152110184
Author(s):  
Gunjan Chandwani ◽  
Anil Ahlawat ◽  
Gaurav Dubey
Keyword(s):  
High Performance ◽  
Clustering Algorithm ◽  
Pearson Correlation ◽  
Relevant Information ◽  
Document Retrieval ◽  
Bhattacharyya Distance ◽  
Data Set ◽  
Query Matching ◽  
Inverted Indexing ◽  
Query Optimisation

Document retrieval plays an important role in knowledge management as it facilitates us to discover the relevant information from the existing data. This article proposes a cluster-based inverted indexing algorithm for document retrieval. First, the pre-processing is done to remove the unnecessary and redundant words from the documents. Then, the indexing of documents is done by the cluster-based inverted indexing algorithm, which is developed by integrating the piecewise fuzzy C-means (piFCM) clustering algorithm and inverted indexing. After providing the index to the documents, the query matching is performed for the user queries using the Bhattacharyya distance. Finally, the query optimisation is done by the Pearson correlation coefficient, and the relevant documents are retrieved. The performance of the proposed algorithm is analysed by the WebKB data set and Twenty Newsgroups data set. The analysis exposes that the proposed algorithm offers high performance with a precision of 1, recall of 0.70 and F-measure of 0.8235. The proposed document retrieval system retrieves the most relevant documents and speeds up the storing and retrieval of information.

PSVI-8 Meta-regression Analysis to Determine the Relationship Between Growing Pig Body Weight and Variation

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 218-219
Author(s):  
Andres Fernando T Russi ◽  
Mike D Tokach ◽  
Jason C Woodworth ◽  
Joel M DeRouchey ◽  
Robert D Goodband ◽  
...  
Keyword(s):  
Body Weight ◽  
Regression Analysis ◽  
Sample Size ◽  
Polynomial Regression ◽  
Data Sets ◽  
Regression Equations ◽  
Prediction Equations ◽  
Data Set ◽  
Rate Of Increase ◽  
The Relationship

Abstract The swine industry has been constantly evolving to select animals with improved performance traits and to minimize variation in body weight (BW) in order to meet packer specifications. Therefore, understanding variation presents an opportunity for producers to find strategies that could help reduce, manage, or deal with variation of pigs in a barn. A systematic review and meta-analysis was conducted by collecting data from multiple studies and available data sets in order to develop prediction equations for coefficient of variation (CV) and standard deviation (SD) as a function of BW. Information regarding BW variation from 16 papers was recorded to provide approximately 204 data points. Together, these data included 117,268 individually weighed pigs with a sample size that ranged from 104 to 4,108 pigs. A random-effects model with study used as a random effect was developed. Observations were weighted using sample size as an estimate for precision on the analysis, where larger data sets accounted for increased accuracy in the model. Regression equations were developed using the nlme package of R to determine the relationship between BW and its variation. Polynomial regression analysis was conducted separately for each variation measurement. When CV was reported in the data set, SD was calculated and vice versa. The resulting prediction equations were: CV (%) = 20.04 – 0.135 × (BW) + 0.00043 × (BW)2, R2=0.79; SD = 0.41 + 0.150 × (BW) - 0.00041 × (BW)2, R2 = 0.95. These equations suggest that there is evidence for a decreasing quadratic relationship between mean CV of a population and BW of pigs whereby the rate of decrease is smaller as mean pig BW increases from birth to market. Conversely, the rate of increase of SD of a population of pigs is smaller as mean pig BW increases from birth to market.

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