Congestive heart failure information extraction framework for automated treatment performance measures assessment

Meystre, Stéphane M.; Kim, Young‐Jun; Gobbel, Glenn T.; Matheny, Michael E.; Redd, Andrew; Bray, Bruce E.; Garvin, Jennifer H.

doi:10.1093/jamia/ocw097

Cited by 31 publications

(33 citation statements)

References 14 publications

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“…Meystre et al [24], for example focused on the information extraction of a few highly selected parameters (e.g., LVEF value and medication) from texts in contrast to an overall detection of heart failure. They utilized a pre-defined data set of heart failure patients in contrast to all-comers and, subsequently, received high F1 scores of up to 99% for single parameters (e.g., LVEF value).…”

Section: Discussionmentioning

confidence: 99%

Underestimated prevalence of heart failure in hospital inpatients: a comparison of ICD codes and discharge letter information

et al. 2018

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BackgroundHeart failure is the predominant cause of hospitalization and amongst the leading causes of death in Germany. However, accurate estimates of prevalence and incidence are lacking. Reported figures originating from different information sources are compromised by factors like economic reasons or documentation quality.MethodsWe implemented a clinical data warehouse that integrates various information sources (structured parameters, plain text, data extracted by natural language processing) and enables reliable approximations to the real number of heart failure patients. Performance of ICD-based diagnosis in detecting heart failure was compared across the years 2000–2015 with (a) advanced definitions based on algorithms that integrate various sources of the hospital information system, and (b) a physician-based reference standard.ResultsApplying these methods for detecting heart failure in inpatients revealed that relying on ICD codes resulted in a marked underestimation of the true prevalence of heart failure, ranging from 44% in the validation dataset to 55% (single year) and 31% (all years) in the overall analysis. Percentages changed over the years, indicating secular changes in coding practice and efficiency. Performance was markedly improved using search and permutation algorithms from the initial expert-specified query (F1 score of 81%) to the computer-optimized query (F1 score of 86%) or, alternatively, optimizing precision or sensitivity depending on the search objective.ConclusionsEstimating prevalence of heart failure using ICD codes as the sole data source yielded unreliable results. Diagnostic accuracy was markedly improved using dedicated search algorithms. Our approach may be transferred to other hospital information systems.

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Section: Discussionmentioning

confidence: 99%

Underestimated prevalence of heart failure in hospital inpatients: a comparison of ICD codes and discharge letter information

et al. 2018

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“…The ADAHF corpus [4], obtained from the TIU files, includes clinical notes from inpatients with HF discharged from a selection of 8 VA medical centers in 2008. It includes 18,397 notes that were manually annotated for LVEF and its values, for a selection of HF medications (ACEIs and ARBs), and for reasons not to administer these medications.…”

Section: Methodsmentioning

confidence: 99%

“…As described above, we previously developed three versions of the LVEF extraction modules: CUIMANDREef [6], CHIEF EF [12,14], and CHIEF ADAHF [4] to detect LVEF mentions and associated values in clinical notes. CUIMANDREef is a rule-based application using regular expressions to capture lexical patterns targeting specific VA echocardiogram reports.…”

Section: Methodsmentioning

confidence: 99%

“…Automated Data Acquisition for Heart Failure (ADAHF) [4] project automated the extraction of information needed for the inpatient HF quality measure from clinical notes, including LVEF and its values, and guideline-concordant HF medications, Angiotensin-Converting Enzyme Inhibitors (ACEI) and Angiotensin Receptor Blockers (ARB), and reasons why the patient was not prescribed these medications. The Consortium for Healthcare Informatics Research (CHIR) [5] Translational Use-Case Project for Ejection Fraction (TUCP-EF) [6] aimed at the automated extraction of LVEF mentions, LVSF (left ventricular systolic function) mentions, and their associated qualitative assessments and quantitative values:…”

Section: Introductionmentioning

confidence: 99%

“…We developed an information extraction application based on rules and machine learning approaches to detect LVEF mentions and associated values in clinical notes as well as other information for HF treatment performance measures [4,6,12,14]. This application was named Congestive Heart Failure Information Extraction Framework (CHIEF) [12,14–16].…”

Section: Introductionmentioning

confidence: 99%

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Extraction of left ventricular ejection fraction information from various types of clinical reports

Kim

Garvin

Goldstein

et al. 2017

Journal of Biomedical Informatics

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Efforts to improve the treatment of congestive heart failure, a common and serious medical condition, include the use of quality measures to assess guideline-concordant care. The goal of this study is to identify left ventricular ejection fraction (LVEF) information from various types of clinical notes, and to then use this information for heart failure quality measurement. We analyzed the annotation differences between a new corpus of clinical notes from the Echocardiography, Radiology, and Text Integrated Utility package and other corpora annotated for natural language processing (NLP) research in the Department of Veterans Affairs. These reports contain varying degrees of structure. To examine whether existing LVEF extraction modules we developed in prior research improve the accuracy of LVEF information extraction from the new corpus, we created two sequence-tagging NLP modules trained with a new data set, with or without predictions from the existing LVEF extraction modules. We also conducted a set of experiments to examine the impact of training data size on information extraction accuracy. We found that less training data is needed when reports are highly structured, and that combining predictions from existing LVEF extraction modules improves information extraction when reports have less structured formats and a rich set of vocabulary.

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Clinical Natural Language Processing in Secondary Use of EHR for Research

Wen

Liu

2023

Health Informatics

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Congestive heart failure information extraction framework for automated treatment performance measures assessment

Cited by 31 publications

References 14 publications

Underestimated prevalence of heart failure in hospital inpatients: a comparison of ICD codes and discharge letter information

Underestimated prevalence of heart failure in hospital inpatients: a comparison of ICD codes and discharge letter information

Extraction of left ventricular ejection fraction information from various types of clinical reports

Clinical Natural Language Processing in Secondary Use of EHR for Research

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