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Acronym Disambiguation in Clinical Notes from Electronic Health Records [article]

Nicholas Byron Link, Sicong Huang, Tianrun Cai, Zeling He, Jiehuan Sun, Kumar Dahal, Lauren Costa, Kelly Cho, Katherine Liao, Tianxi Cai, Chuan Hong
2020 medRxiv   pre-print
Objective: The use of electronic health records (EHR) systems has grown over the past decade, and with it, the need to extract information from unstructured clinical narratives.  ...  In this study we introduce an unsupervised method for acronym disambiguation, the task of classifying the correct sense of acronyms in the clinical EHR notes.  ...  INTRODUCTION A large amount of important clinical data is embedded in the narrative notes within electronic health record (EHR) systems.  ... 
doi:10.1101/2020.11.25.20221648 fatcat:vrjo7s3rezdijeec6hamkkzg4a

Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review [article]

Irene Li, Jessica Pan, Jeremy Goldwasser, Neha Verma, Wai Pan Wong, Muhammed Yavuz Nuzumlalı, Benjamin Rosand, Yixin Li, Matthew Zhang, David Chang, R. Andrew Taylor, Harlan M. Krumholz (+1 others)
2021 arXiv   pre-print
Electronic health records (EHRs), digital collections of patient healthcare events and observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and research.  ...  In this survey paper, we summarize current neural NLP methods for EHR applications.  ...  Some efforts have been made for abbreviation disambiguation on clinical notes. Traditional methods like decision trees were applied for acronym disambiguation in Spanish EHRs [192] .  ... 
arXiv:2107.02975v1 fatcat:nayhw7gadfdzrovycdkvzy75pi

Natural Language Processing for EHR-Based Computational Phenotyping

Zexian Zeng, Yu Deng, Xiaoyu Li, Tristan Naumann, Yuan Luo
2018 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
This article reviews recent advances in applying natural language processing (NLP) to Electronic Health Records (EHRs) for computational phenotyping.  ...  NLP-based computational phenotyping has numerous applications including diagnosis categorization, novel phenotype discovery, clinical trial screening, pharmacogenomics, drug-drug interaction (DDI), and  ...  Acknowledgment This work was supported in part by NIH Grant 1R21LM012618-01, NLM Biomedical Informatics Training Grant 2T15 LM007092-22, and the Intel Science and Technology Center for Big Data.  ... 
doi:10.1109/tcbb.2018.2849968 pmid:29994486 pmcid:PMC6388621 fatcat:wsksxvr7lfbgjowrsymghld64u

Clinical Text Data in Machine Learning: Systematic Review

Irena Spasic, Goran Nenadic
2020 JMIR Medical Informatics  
Supervised learning was successfully used where clinical codes integrated with free-text notes into electronic health records were utilized as class labels.  ...  Clinical narratives represent the main form of communication within health care, providing a personalized account of patient history and assessments, and offering rich information for clinical decision  ...  Acknowledgments The authors gratefully acknowledge the support from the Engineering and Physical Sciences Research Council for HealTex-UK Healthcare Text Analytics Research Network (Grant number EP/N027280  ... 
doi:10.2196/17984 pmid:32229465 fatcat:zbnsn4hi4zakhpukefktb72yo4

Natural Language Processing for EHR-Based Computational Phenotyping [article]

Zexian Zeng, Yu Deng, Xiaoyu Li, Tristan Naumann, Yuan Luo
2018 arXiv   pre-print
This article reviews recent advances in applying natural language processing (NLP) to Electronic Health Records (EHRs) for computational phenotyping.  ...  NLP-based computational phenotyping has numerous applications including diagnosis categorization, novel phenotype discovery, clinical trial screening, pharmacogenomics, drug-drug interaction (DDI) and  ...  Nationwide adoption of Electronic Health Records (EHRs) has given rise to a large amount of digital health data, which can be used for secondary analysis [2] .  ... 
arXiv:1806.04820v2 fatcat:fo5ck7rpgzhb7dgmqfjc3bdw7y

Complexities, variations, and errors of numbering within clinical notes: the potential impact on information extraction and cohort-identification

David A. Hanauer, Qiaozhu Mei, V. G. Vinod Vydiswaran, Karandeep Singh, Zach Landis-Lewis, Chunhua Weng
2019 BMC Medical Informatics and Decision Making  
Numbers and numerical concepts appear frequently in free text clinical notes from electronic health records.  ...  This paper describes an analysis of the variation in how numbers and numerical concepts are represented in clinical notes.  ...  Acknowledgments Not applicable.  ... 
doi:10.1186/s12911-019-0784-1 pmid:30944012 pmcid:PMC6448181 fatcat:t5i5qv3dxvb3hpifmrk57pncpe

Knowledge discovery for Deep Phenotyping serious mental illness from Electronic Mental Health records

Richard Jackson, Rashmi Patel, Sumithra Velupillai, George Gkotsis, David Hoyle, Robert Stewart
2018 F1000Research  
In UK mental health clinical practice, most clinically relevant information is recorded as free text in the Electronic Health Record, and offers a granularity of information beyond what is expressed in  ...  Deep Phenotyping is the precise and comprehensive Background: analysis of phenotypic features in which the individual components of the phenotype are observed and described.  ...  reflects the fact that the authors very reasonably cannot publish the raw data, but they do address how to obtain the data through a formal application process. (Ergo the "Yes" to whether  ... 
doi:10.12688/f1000research.13830.2 fatcat:t5fn5gsagfeqnbi5rbqe53u6wm

Knowledge discovery for Deep Phenotyping serious mental illness from Electronic Mental Health records

Richard Jackson, Rashmi Patel, Sumithra Velupillai, George Gkotsis, David Hoyle, Robert Stewart
2018 F1000Research  
In UK mental health clinical practice, most clinically relevant information is recorded as free text in the Electronic Health Record, and offers a granularity of information beyond that expressed in most  ...  task of phenotyping Serious Mental Illness (SMI) with only a small amount of prior knowledge. 20 403 n-grams were derived and curated via a two stage Results: methodology.  ...  Historical clinical data mined from Electronic Health Record (EHR) systems are frequently employed to meet the related use case of observational epidemiology.  ... 
doi:10.12688/f1000research.13830.1 pmid:29899974 pmcid:PMC5968362 fatcat:zxc7yl3lfrg3zbqzyxxvxoshby

Machine Learning Techniques for Biomedical Natural Language Processing: A comprehensive Review

Essam H. Houssein, Rehab E. Mohamed, Abdelmgeid A. Ali
2021 IEEE Access  
Natural language processing (NLP) techniques have been used as an artificial intelligence strategy to extract information from clinical narratives in electronic health records since they include a great  ...  However, in a free-form text such as electronic health records, many clinical data are still hidden in a clinical narrative format.  ...  All searches used the keywords "electronic health records" or "electronic medical records" or "EHR" or "EMR," in combination with either "machine learning" or the name of a particular technique of machine  ... 
doi:10.1109/access.2021.3119621 fatcat:pl7h35nvqngk3gxpbdxvrgzg2u

Assessing mortality prediction through different representation models based on concepts extracted from clinical notes [article]

Hoda Memarzadeh, Nasser Ghadiri, Maryam Lotfi Shahreza
2022 arXiv   pre-print
Existing methods use clinical notes directly or with an initial preprocessing as input to representation models.  ...  Recent years have seen particular interest in using electronic medical records (EMRs) for secondary purposes to enhance the quality and safety of healthcare delivery.  ...  We followed these steps: 1) collecting discharge notes from medical records in the dataset; 2) applying the clinical NLP pipelines to extract concepts from clinical notes; 3) extracting key phrases using  ... 
arXiv:2207.10872v1 fatcat:us53jonfsnhx5imsl4rdtxppoq

An Empirical Study of UMLS Concept Extraction from Clinical Notes using Boolean Combination Ensembles [article]

Greg M. Silverman, Raymond L. Finzel, Michael V. Heinz, Jake Vasilakes, Jacob C. Solinsky, Reed McEwan, Benjamin C. Knoll, Christopher J. Tignanelli, Hongfang Liu, Hua Xu, Xiaoqian Jiang, Genevieve B. Melton (+1 others)
2021 arXiv   pre-print
Our objective in this study is to investigate the behavior of Boolean operators on combining annotation output from multiple Natural Language Processing (NLP) systems across multiple corpora and to assess  ...  including UMLS concept unique identifiers), and Fairview Health Services corpus (44,530 annotations).  ...  Acknowledgements We would like to thank Jen Morgan, Angel Helget, and George Konstantinides for their help with annotating clinical data.  ... 
arXiv:2108.02255v1 fatcat:2jjsp5wuovfrbo3rqvb4ui4zo4

The Revival of the Notes Field: Leveraging the Unstructured Content in Electronic Health Records

Michela Assale, Linda Greta Dui, Andrea Cina, Andrea Seveso, Federico Cabitza
2019 Frontiers in Medicine  
electronic patient records.  ...  Problem: Clinical practice requires the production of a time- and resource-consuming great amount of notes.  ...  Michele Ulivi for their advice in regard to the medical aspects of our research.  ... 
doi:10.3389/fmed.2019.00066 pmid:31058150 pmcid:PMC6478793 fatcat:6koblecrsnbabld6y6qwukwxgy

A text mining approach to cohort selection from longitudinal patient records (Preprint)

Irena Spasić, Dominik Krzemiński, Padraig Corcoran, Alexander Balinsky
2019 JMIR Medical Informatics  
Clinical trials are an important step in introducing new interventions into clinical practice by generating data on their safety and efficacy.  ...  With an F measure of 88.14%, we significantly outperformed these systems (81.03%, 78.50%, and 70.81%) in identifying patients with advanced coronary artery disease.  ...  This raises an important aspect of the completeness of information recorded in an EMR [15] .  ... 
doi:10.2196/15980 pmid:31674914 pmcid:PMC6913747 fatcat:gfzlg57zkzc4tczgreqi26scgi

Mining Electronic Health Records (EHRs)

Pranjul Yadav, Michael Steinbach, Vipin Kumar, Gyorgy Simon
2018 ACM Computing Surveys  
Central to the ideas aimed at curbing this trend is the use of technology, in the form of the mandate to implement electronic health records (EHRs).  ...  We conclude with a case study of patients diagnosed with Type 2 diabetes mellitus (T2DM).  ...  [50] have explored temporal disease progression patterns in data from an electronic health record registry which covers the entire population of Denmark.  ... 
doi:10.1145/3127881 fatcat:xil7qev3xbf3pmfv5vtak4f2jq

Natural Language Processing Accurately Categorizes Indications, Findings and Pathology Reports from Multicenter Colonoscopy [article]

Shashank Reddy Vadyala, Eric A. Sherer
2021 arXiv   pre-print
Extracting details of the colonoscopy findings from free text in electronic health records (EHRs) can be used to determine patient risk for CRC and colorectal screening strategies.  ...  These clinical notes were from a group of patients over 40 years of age enrolled in four Veterans Affairs Medical Centers.  ...  Adapting a clinical information retrieval system, Named entity recognition (NER), identified pathology reports consistent with CRC from an electronic medical record system [26] .  ... 
arXiv:2108.11034v1 fatcat:r42gyhuiavfu3m2xbukbu74jn4
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