scholarly journals Citywide quality of health information system through text mining of electronic health records

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Anastasia A. Funkner ◽  
Michil P. Egorov ◽  
Sergey A. Fokin ◽  
Gennady M. Orlov ◽  
Sergey V. Kovalchuk
Keyword(s):  
Electronic Health Records ◽  
Health Information ◽  
Language Processing ◽  
Information Quality ◽  
Medical Information ◽  
Health Information System ◽  
Interconnected System ◽  
Health Records ◽  
Electronic Health

AbstractA system of hospitals in large cities can be considered a large and diverse but interconnected system. Widely applied in hospitals, electronic health records (EHR) are crucially different from each other because of the use of different health information systems, internal hospital rules, and individual behavior of physicians. The unstructured (textual) data of EHR is rarely used to assess the citywide quality of healthcare. Within the study, we analyze EHR data, particularly textual unstructured data, as a reflection of the complex multi-agent system of healthcare in the city of Saint Petersburg, Russia. Through analyzing the data collected by the Medical Information and Analytical Center, a method was proposed and evaluated for identifying a common structure, understanding the diversity, and assessing information quality in EHR data through the application of natural language processing techniques.

scholarly journals medExtractR: A targeted, customizable approach to medication extraction from electronic health records

2020 ◽  
Vol 27 (3) ◽  
pp. 407-418 ◽  
Author(s):  
Hannah L Weeks ◽  
Cole Beck ◽  
Elizabeth McNeer ◽  
Michael L Williams ◽  
Cosmin A Bejan ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Language Processing ◽  
High Performance ◽  
Medical Information ◽  
Medical Center ◽  
Processing System ◽  
Small Sample ◽  
Health Records ◽  
Electronic Health ◽  
Mimic Iii

Abstract Objective We developed medExtractR, a natural language processing system to extract medication information from clinical notes. Using a targeted approach, medExtractR focuses on individual drugs to facilitate creation of medication-specific research datasets from electronic health records. Materials and Methods Written using the R programming language, medExtractR combines lexicon dictionaries and regular expressions to identify relevant medication entities (eg, drug name, strength, frequency). MedExtractR was developed on notes from Vanderbilt University Medical Center, using medications prescribed with varying complexity. We evaluated medExtractR and compared it with 3 existing systems: MedEx, MedXN, and CLAMP (Clinical Language Annotation, Modeling, and Processing). We also demonstrated how medExtractR can be easily tuned for better performance on an outside dataset using the MIMIC-III (Medical Information Mart for Intensive Care III) database. Results On 50 test notes per development drug and 110 test notes for an additional drug, medExtractR achieved high overall performance (F-measures >0.95), exceeding performance of the 3 existing systems across all drugs. MedExtractR achieved the highest F-measure for each individual entity, except drug name and dose amount for allopurinol. With tuning and customization, medExtractR achieved F-measures >0.90 in the MIMIC-III dataset. Discussion The medExtractR system successfully extracted entities for medications of interest. High performance in entity-level extraction provides a strong foundation for developing robust research datasets for pharmacological research. When working with new datasets, medExtractR should be tuned on a small sample of notes before being broadly applied. Conclusions The medExtractR system achieved high performance extracting specific medications from clinical text, leading to higher-quality research datasets for drug-related studies than some existing general-purpose medication extraction tools.

Safe Use of Electronic Health Records and Health Information Technology Systems

2013 ◽  
Vol 9 (4) ◽  
pp. 177-189 ◽  
Author(s):  
Charles R. Denham ◽  
David C. Classen ◽  
Stephen J. Swenson ◽  
Michael J. Henderson ◽  
Thomas Zeltner ◽  
...  
Keyword(s):  
Information Technology ◽  
Electronic Health Records ◽  
Health Information Technology ◽  
Health Information ◽  
Health Records ◽  
Electronic Health ◽  
Technology Systems

Development of algorithm for classification smoking status from unstructured bilingual electronic health records based on natural language processing (Preprint)

2021 ◽  
Author(s):  
Ye Seul Bae ◽  
Kyung Hwan Kim ◽  
Han Kyul Kim ◽  
Sae Won Choi ◽  
Taehoon Ko ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Electronic Health Records ◽  
Natural Language ◽  
Language Processing ◽  
Smoking Status ◽  
Svm Classifier ◽  
Keyword Extraction ◽  
Health Records ◽  
Clinical Notes ◽  
Electronic Health

BACKGROUND Smoking is a major risk factor and important variable for clinical research, but there are few studies regarding automatic obtainment of smoking classification from unstructured bilingual electronic health records (EHR). OBJECTIVE We aim to develop an algorithm to classify smoking status based on unstructured EHRs using natural language processing (NLP). METHODS With acronym replacement and Python package Soynlp, we normalize 4,711 bilingual clinical notes. Each EHR notes was classified into 4 categories: current smokers, past smokers, never smokers, and unknown. Subsequently, SPPMI (Shifted Positive Point Mutual Information) is used to vectorize words in the notes. By calculating cosine similarity between these word vectors, keywords denoting the same smoking status are identified. RESULTS Compared to other keyword extraction methods (word co-occurrence-, PMI-, and NPMI-based methods), our proposed approach improves keyword extraction precision by as much as 20.0%. These extracted keywords are used in classifying 4 smoking statuses from our bilingual clinical notes. Given an identical SVM classifier, the extracted keywords improve the F1 score by as much as 1.8% compared to those of the unigram and bigram Bag of Words. CONCLUSIONS Our study shows the potential of SPPMI in classifying smoking status from bilingual, unstructured EHRs. Our current findings show how smoking information can be easily acquired and used for clinical practice and research.

scholarly journals Desiderata for computable representations of electronic health records-driven phenotype algorithms

2015 ◽  
Vol 22 (6) ◽  
pp. 1220-1230 ◽  
Author(s):  
Huan Mo ◽  
William K Thompson ◽  
Luke V Rasmussen ◽  
Jennifer A Pacheco ◽  
Guoqian Jiang ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Language Processing ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Relational Algebra ◽  
Common Data Model ◽  
Health Records ◽  
Electronic Health ◽  
Value Sets ◽  
Text Searching

Abstract Background Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM). Methods A team of clinicians and informaticians reviewed common features for multisite phenotype algorithms published in PheKB.org and existing phenotype representation platforms. We also evaluated well-known diagnostic criteria and clinical decision-making guidelines to encompass a broader category of algorithms. Results We propose 10 desired characteristics for a flexible, computable PheRM: (1) structure clinical data into queryable forms; (2) recommend use of a common data model, but also support customization for the variability and availability of EHR data among sites; (3) support both human-readable and computable representations of phenotype algorithms; (4) implement set operations and relational algebra for modeling phenotype algorithms; (5) represent phenotype criteria with structured rules; (6) support defining temporal relations between events; (7) use standardized terminologies and ontologies, and facilitate reuse of value sets; (8) define representations for text searching and natural language processing; (9) provide interfaces for external software algorithms; and (10) maintain backward compatibility. Conclusion A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems. These desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages.

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