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A study of active learning methods for named entity recognition in clinical text.
Chen Y, Lasko TA, Mei Q, Denny JC, Xu H
(2015) J Biomed Inform 58: 11-18
MeSH Terms: Humans, Learning, Machine Learning, Natural Language Processing
Show Abstract · Added April 7, 2017
OBJECTIVES - Named entity recognition (NER), a sequential labeling task, is one of the fundamental tasks for building clinical natural language processing (NLP) systems. Machine learning (ML) based approaches can achieve good performance, but they often require large amounts of annotated samples, which are expensive to build due to the requirement of domain experts in annotation. Active learning (AL), a sample selection approach integrated with supervised ML, aims to minimize the annotation cost while maximizing the performance of ML-based models. In this study, our goal was to develop and evaluate both existing and new AL methods for a clinical NER task to identify concepts of medical problems, treatments, and lab tests from the clinical notes.
METHODS - Using the annotated NER corpus from the 2010 i2b2/VA NLP challenge that contained 349 clinical documents with 20,423 unique sentences, we simulated AL experiments using a number of existing and novel algorithms in three different categories including uncertainty-based, diversity-based, and baseline sampling strategies. They were compared with the passive learning that uses random sampling. Learning curves that plot performance of the NER model against the estimated annotation cost (based on number of sentences or words in the training set) were generated to evaluate different active learning and the passive learning methods and the area under the learning curve (ALC) score was computed.
RESULTS - Based on the learning curves of F-measure vs. number of sentences, uncertainty sampling algorithms outperformed all other methods in ALC. Most diversity-based methods also performed better than random sampling in ALC. To achieve an F-measure of 0.80, the best method based on uncertainty sampling could save 66% annotations in sentences, as compared to random sampling. For the learning curves of F-measure vs. number of words, uncertainty sampling methods again outperformed all other methods in ALC. To achieve 0.80 in F-measure, in comparison to random sampling, the best uncertainty based method saved 42% annotations in words. But the best diversity based method reduced only 7% annotation effort.
CONCLUSION - In the simulated setting, AL methods, particularly uncertainty-sampling based approaches, seemed to significantly save annotation cost for the clinical NER task. The actual benefit of active learning in clinical NER should be further evaluated in a real-time setting.
Copyright © 2015 Elsevier Inc. All rights reserved.
0 Communities
2 Members
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4 MeSH Terms
A Standards-based Semantic Metadata Repository to Support EHR-driven Phenotype Authoring and Execution.
Jiang G, Solbrig HR, Kiefer R, Rasmussen LV, Mo H, Speltz P, Thompson WK, Denny JC, Chute CG, Pathak J
(2015) Stud Health Technol Inform 216: 1098
MeSH Terms: Databases, Factual, Electronic Health Records, Guidelines as Topic, Health Level Seven, Medical Record Linkage, Natural Language Processing, Semantics, United States, Vocabulary, Controlled
Show Abstract · Added March 14, 2018
This study describes our efforts in developing a standards-based semantic metadata repository for supporting electronic health record (EHR)-driven phenotype authoring and execution. Our system comprises three layers: 1) a semantic data element repository layer; 2) a semantic services layer; and 3) a phenotype application layer. In a prototype implementation, we developed the repository and services through integrating the data elements from both Quality Data Model (QDM) and HL7 Fast Healthcare Inteoroperability Resources (FHIR) models. We discuss the modeling challenges and the potential of our system to support EHR phenotype authoring and execution applications.
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9 MeSH Terms
A Robust e-Epidemiology Tool in Phenotyping Heart Failure with Differentiation for Preserved and Reduced Ejection Fraction: the Electronic Medical Records and Genomics (eMERGE) Network.
Bielinski SJ, Pathak J, Carrell DS, Takahashi PY, Olson JE, Larson NB, Liu H, Sohn S, Wells QS, Denny JC, Rasmussen-Torvik LJ, Pacheco JA, Jackson KL, Lesnick TG, Gullerud RE, Decker PA, Pereira NL, Ryu E, Dart RA, Peissig P, Linneman JG, Jarvik GP, Larson EB, Bock JA, Tromp GC, de Andrade M, Roger VL
(2015) J Cardiovasc Transl Res 8: 475-83
MeSH Terms: Algorithms, Data Mining, Electronic Health Records, Female, Heart Failure, Humans, Male, Natural Language Processing, Phenotype, Reproducibility of Results, Stroke Volume, United States, Ventricular Function, Left
Show Abstract · Added April 6, 2017
Identifying populations of heart failure (HF) patients is paramount to research efforts aimed at developing strategies to effectively reduce the burden of this disease. The use of electronic medical record (EMR) data for this purpose is challenging given the syndromic nature of HF and the need to distinguish HF with preserved or reduced ejection fraction. Using a gold standard cohort of manually abstracted cases, an EMR-driven phenotype algorithm based on structured and unstructured data was developed to identify all the cases. The resulting algorithm was executed in two cohorts from the Electronic Medical Records and Genomics (eMERGE) Network with a positive predictive value of >95 %. The algorithm was expanded to include three hierarchical definitions of HF (i.e., definite, probable, possible) based on the degree of confidence of the classification to capture HF cases in a whole population whereby increasing the algorithm utility for use in e-Epidemiologic research.
0 Communities
2 Members
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13 MeSH Terms
A Preliminary Study of Clinical Abbreviation Disambiguation in Real Time.
Wu Y, Denny JC, Rosenbloom ST, Miller RA, Giuse DA, Song M, Xu H
(2015) Appl Clin Inform 6: 364-74
MeSH Terms: Abbreviations as Topic, Documentation, Health Personnel, Natural Language Processing, Time Factors, User-Computer Interface
Show Abstract · Added January 26, 2016
OBJECTIVE - To save time, healthcare providers frequently use abbreviations while authoring clinical documents. Nevertheless, abbreviations that authors deem unambiguous often confuse other readers, including clinicians, patients, and natural language processing (NLP) systems. Most current clinical NLP systems "post-process" notes long after clinicians enter them into electronic health record systems (EHRs). Such post-processing cannot guarantee 100% accuracy in abbreviation identification and disambiguation, since multiple alternative interpretations exist.
METHODS - Authors describe a prototype system for real-time Clinical Abbreviation Recognition and Disambiguation (rCARD) - i.e., a system that interacts with authors during note generation to verify correct abbreviation senses. The rCARD system design anticipates future integration with web-based clinical documentation systems to improve quality of healthcare records. When clinicians enter documents, rCARD will automatically recognize each abbreviation. For abbreviations with multiple possible senses, rCARD will show a ranked list of possible meanings with the best predicted sense at the top. The prototype application embodies three word sense disambiguation (WSD) methods to predict the correct senses of abbreviations. We then conducted three experments to evaluate rCARD, including 1) a performance evaluation of different WSD methods; 2) a time evaluation of real-time WSD methods; and 3) a user study of typing clinical sentences with abbreviations using rCARD.
RESULTS - Using 4,721 sentences containing 25 commonly observed, highly ambiguous clinical abbreviations, our evaluation showed that the best profile-based method implemented in rCARD achieved a reasonable WSD accuracy of 88.8% (comparable to SVM - 89.5%) and the cost of time for the different WSD methods are also acceptable (ranging from 0.630 to 1.649 milliseconds within the same network). The preliminary user study also showed that the extra time costs by rCARD were about 5% of total document entry time and users did not feel a significant delay when using rCARD for clinical document entry.
CONCLUSION - The study indicates that it is feasible to integrate a real-time, NLP-enabled abbreviation recognition and disambiguation module with clinical documentation systems.
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2 Members
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6 MeSH Terms
Using natural language processing to provide personalized learning opportunities from trainee clinical notes.
Denny JC, Spickard A, Speltz PJ, Porier R, Rosenstiel DE, Powers JS
(2015) J Biomed Inform 56: 292-9
MeSH Terms: Academic Medical Centers, Advance Directives, Aged, Algorithms, Automation, Clinical Clerkship, Clinical Competence, Education, Medical, Educational Measurement, Electronic Health Records, Geriatrics, Hospitals, Veterans, Humans, Learning, Mental Disorders, Middle Aged, Natural Language Processing, Outcome Assessment, Health Care, Reproducibility of Results, Software, Students, Medical, Tennessee, User-Computer Interface
Show Abstract · Added March 14, 2018
OBJECTIVE - Assessment of medical trainee learning through pre-defined competencies is now commonplace in schools of medicine. We describe a novel electronic advisor system using natural language processing (NLP) to identify two geriatric medicine competencies from medical student clinical notes in the electronic medical record: advance directives (AD) and altered mental status (AMS).
MATERIALS AND METHODS - Clinical notes from third year medical students were processed using a general-purpose NLP system to identify biomedical concepts and their section context. The system analyzed these notes for relevance to AD or AMS and generated custom email alerts to students with embedded supplemental learning material customized to their notes. Recall and precision of the two advisors were evaluated by physician review. Students were given pre and post multiple choice question tests broadly covering geriatrics.
RESULTS - Of 102 students approached, 66 students consented and enrolled. The system sent 393 email alerts to 54 students (82%), including 270 for AD and 123 for AMS. Precision was 100% for AD and 93% for AMS. Recall was 69% for AD and 100% for AMS. Students mentioned ADs for 43 patients, with all mentions occurring after first having received an AD reminder. Students accessed educational links 34 times from the 393 email alerts. There was no difference in pre (mean 62%) and post (mean 60%) test scores.
CONCLUSIONS - The system effectively identified two educational opportunities using NLP applied to clinical notes and demonstrated a small change in student behavior. Use of electronic advisors such as these may provide a scalable model to assess specific competency elements and deliver educational opportunities.
Copyright © 2015 Elsevier Inc. All rights reserved.
0 Communities
1 Members
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23 MeSH Terms
Automated Assessment of Medical Students' Clinical Exposures according to AAMC Geriatric Competencies.
Chen Y, Wrenn J, Xu H, Spickard A, Habermann R, Powers J, Denny JC
(2014) AMIA Annu Symp Proc 2014: 375-84
MeSH Terms: Area Under Curve, Artificial Intelligence, Clinical Competence, Education, Medical, Undergraduate, Educational Measurement, Geriatrics, Humans, Natural Language Processing, Students, Medical, Tennessee
Show Abstract · Added March 14, 2018
Competence is essential for health care professionals. Current methods to assess competency, however, do not efficiently capture medical students' experience. In this preliminary study, we used machine learning and natural language processing (NLP) to identify geriatric competency exposures from students' clinical notes. The system applied NLP to generate the concepts and related features from notes. We extracted a refined list of concepts associated with corresponding competencies. This system was evaluated through 10-fold cross validation for six geriatric competency domains: "medication management (MedMgmt)", "cognitive and behavioral disorders (CBD)", "falls, balance, gait disorders (Falls)", "self-care capacity (SCC)", "palliative care (PC)", "hospital care for elders (HCE)" - each an American Association of Medical Colleges competency for medical students. The systems could accurately assess MedMgmt, SCC, HCE, and Falls competencies with F-measures of 0.94, 0.86, 0.85, and 0.84, respectively, but did not attain good performance for PC and CBD (0.69 and 0.62 in F-measure, respectively).
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1 Members
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10 MeSH Terms
Learning to identify treatment relations in clinical text.
Bejan CA, Denny JC
(2014) AMIA Annu Symp Proc 2014: 282-8
MeSH Terms: Artificial Intelligence, Databases as Topic, Electronic Health Records, Humans, Information Storage and Retrieval, Natural Language Processing, Semantics, Therapeutics
Show Abstract · Added March 14, 2018
In clinical notes, physicians commonly describe reasons why certain treatments are given. However, this information is not typically available in a computable form. We describe a supervised learning system that is able to predict whether or not a treatment relation exists between any two medical concepts mentioned in clinical notes. To train our prediction model, we manually annotated 958 treatment relations in sentences selected from 6,864 discharge summaries. The features used to indicate the existence of a treatment relation between two medical concepts consisted of lexical and semantic information associated with the two concepts as well as information derived from the MEDication Indication (MEDI) resource and SemRep. The best F1-measure results of our supervised learning system (84.90) were significantly better than the F1-measure results achieved by SemRep (72.34).
0 Communities
1 Members
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8 MeSH Terms
Building bridges across electronic health record systems through inferred phenotypic topics.
Chen Y, Ghosh J, Bejan CA, Gunter CA, Gupta S, Kho A, Liebovitz D, Sun J, Denny J, Malin B
(2015) J Biomed Inform 55: 82-93
MeSH Terms: Electronic Health Records, Information Storage and Retrieval, Machine Learning, Medical Record Linkage, Natural Language Processing, Phenotype, United States, Vocabulary, Controlled
Show Abstract · Added April 10, 2018
OBJECTIVE - Data in electronic health records (EHRs) is being increasingly leveraged for secondary uses, ranging from biomedical association studies to comparative effectiveness. To perform studies at scale and transfer knowledge from one institution to another in a meaningful way, we need to harmonize the phenotypes in such systems. Traditionally, this has been accomplished through expert specification of phenotypes via standardized terminologies, such as billing codes. However, this approach may be biased by the experience and expectations of the experts, as well as the vocabulary used to describe such patients. The goal of this work is to develop a data-driven strategy to (1) infer phenotypic topics within patient populations and (2) assess the degree to which such topics facilitate a mapping across populations in disparate healthcare systems.
METHODS - We adapt a generative topic modeling strategy, based on latent Dirichlet allocation, to infer phenotypic topics. We utilize a variance analysis to assess the projection of a patient population from one healthcare system onto the topics learned from another system. The consistency of learned phenotypic topics was evaluated using (1) the similarity of topics, (2) the stability of a patient population across topics, and (3) the transferability of a topic across sites. We evaluated our approaches using four months of inpatient data from two geographically distinct healthcare systems: (1) Northwestern Memorial Hospital (NMH) and (2) Vanderbilt University Medical Center (VUMC).
RESULTS - The method learned 25 phenotypic topics from each healthcare system. The average cosine similarity between matched topics across the two sites was 0.39, a remarkably high value given the very high dimensionality of the feature space. The average stability of VUMC and NMH patients across the topics of two sites was 0.988 and 0.812, respectively, as measured by the Pearson correlation coefficient. Also the VUMC and NMH topics have smaller variance of characterizing patient population of two sites than standard clinical terminologies (e.g., ICD9), suggesting they may be more reliably transferred across hospital systems.
CONCLUSIONS - Phenotypic topics learned from EHR data can be more stable and transferable than billing codes for characterizing the general status of a patient population. This suggests that EHR-based research may be able to leverage such phenotypic topics as variables when pooling patient populations in predictive models.
Copyright © 2015 Elsevier Inc. All rights reserved.
0 Communities
1 Members
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MeSH Terms
Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality.
Xu H, Aldrich MC, Chen Q, Liu H, Peterson NB, Dai Q, Levy M, Shah A, Han X, Ruan X, Jiang M, Li Y, Julien JS, Warner J, Friedman C, Roden DM, Denny JC
(2015) J Am Med Inform Assoc 22: 179-91
MeSH Terms: Administration, Oral, Adult, Diabetes Mellitus, Type 2, Drug Repositioning, Electronic Health Records, Humans, Hypoglycemic Agents, Information Storage and Retrieval, Metformin, Natural Language Processing, Neoplasms, Registries, Survival Analysis
Show Abstract · Added May 6, 2016
OBJECTIVES - Drug repurposing, which finds new indications for existing drugs, has received great attention recently. The goal of our work is to assess the feasibility of using electronic health records (EHRs) and automated informatics methods to efficiently validate a recent drug repurposing association of metformin with reduced cancer mortality.
METHODS - By linking two large EHRs from Vanderbilt University Medical Center and Mayo Clinic to their tumor registries, we constructed a cohort including 32,415 adults with a cancer diagnosis at Vanderbilt and 79,258 cancer patients at Mayo from 1995 to 2010. Using automated informatics methods, we further identified type 2 diabetes patients within the cancer cohort and determined their drug exposure information, as well as other covariates such as smoking status. We then estimated HRs for all-cause mortality and their associated 95% CIs using stratified Cox proportional hazard models. HRs were estimated according to metformin exposure, adjusted for age at diagnosis, sex, race, body mass index, tobacco use, insulin use, cancer type, and non-cancer Charlson comorbidity index.
RESULTS - Among all Vanderbilt cancer patients, metformin was associated with a 22% decrease in overall mortality compared to other oral hypoglycemic medications (HR 0.78; 95% CI 0.69 to 0.88) and with a 39% decrease compared to type 2 diabetes patients on insulin only (HR 0.61; 95% CI 0.50 to 0.73). Diabetic patients on metformin also had a 23% improved survival compared with non-diabetic patients (HR 0.77; 95% CI 0.71 to 0.85). These associations were replicated using the Mayo Clinic EHR data. Many site-specific cancers including breast, colorectal, lung, and prostate demonstrated reduced mortality with metformin use in at least one EHR.
CONCLUSIONS - EHR data suggested that the use of metformin was associated with decreased mortality after a cancer diagnosis compared with diabetic and non-diabetic cancer patients not on metformin, indicating its potential as a chemotherapeutic regimen. This study serves as a model for robust and inexpensive validation studies for drug repurposing signals using EHR data.
© The Author 2014. Published by Oxford University Press on behalf of the American Medical Informatics Association.
0 Communities
3 Members
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13 MeSH Terms
Design patterns for the development of electronic health record-driven phenotype extraction algorithms.
Rasmussen LV, Thompson WK, Pacheco JA, Kho AN, Carrell DS, Pathak J, Peissig PL, Tromp G, Denny JC, Starren JB
(2014) J Biomed Inform 51: 280-6
MeSH Terms: Algorithms, Biological Ontologies, Data Curation, Data Mining, Electronic Health Records, Genomics, Natural Language Processing, Pattern Recognition, Automated, Phenotype
Show Abstract · Added March 14, 2018
BACKGROUND - Design patterns, in the context of software development and ontologies, provide generalized approaches and guidance to solving commonly occurring problems, or addressing common situations typically informed by intuition, heuristics and experience. While the biomedical literature contains broad coverage of specific phenotype algorithm implementations, no work to date has attempted to generalize common approaches into design patterns, which may then be distributed to the informatics community to efficiently develop more accurate phenotype algorithms.
METHODS - Using phenotyping algorithms stored in the Phenotype KnowledgeBase (PheKB), we conducted an independent iterative review to identify recurrent elements within the algorithm definitions. We extracted and generalized recurrent elements in these algorithms into candidate patterns. The authors then assessed the candidate patterns for validity by group consensus, and annotated them with attributes.
RESULTS - A total of 24 electronic Medical Records and Genomics (eMERGE) phenotypes available in PheKB as of 1/25/2013 were downloaded and reviewed. From these, a total of 21 phenotyping patterns were identified, which are available as an online data supplement.
CONCLUSIONS - Repeatable patterns within phenotyping algorithms exist, and when codified and cataloged may help to educate both experienced and novice algorithm developers. The dissemination and application of these patterns has the potential to decrease the time to develop algorithms, while improving portability and accuracy.
Copyright © 2014 Elsevier Inc. All rights reserved.
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1 Members
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9 MeSH Terms