An Efficient, Robust, and Customizable Information Extraction and Pre-processing Pipeline for Electronic Health Records

Lee, Eva K.; Yuan-bo, Wang; He, Yuntian; Egan, Brent M.

doi:10.5220/0008071303100321

Cited by 5 publications

(5 citation statements)

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“…We demonstrate the use of CERC within a customizable information extraction and pre-processing pipeline for EHRs which extracts, anonymizes, and encrypts data directly from EHRs [63,64]. Specifically, CERC is used for information extraction from extracted narrative clinical texts.…”

Section: A Customizable Information Extraction and Pre-processing Pipmentioning

confidence: 99%

“…Specifically, CERC is used for information extraction from extracted narrative clinical texts. Below is a short excerpt from our paper [64].…”

Section: A Customizable Information Extraction and Pre-processing Pipmentioning

confidence: 99%

“…This establishes an interoperable cohort of patients. Users can apply clustering and machine learning for evidence-based treatment planning discovery or other comparative effectiveness and personalized treatment advances [64].…”

Section: Data Standardization To Snomed-ct Using Snomed-ctmentioning

confidence: 99%

“…It has also been shown that high-information clinical findings appear in the medical records of patients before the high-risk diagnosis is determined [13]. Furthermore, automated summarization of patient information to extract salient information can improve decision-making and reduce the risk of information overload [7,14,64].…”

Section: Introductionmentioning

confidence: 99%

“…The topic cloud provides a visual overview of the content in the input text and allows interactive filtering of the sentence extraction results based on users' interests. A keyword-based filtering allows users to generate a summary based on the topranked sentences and edit and save the selected summary for future review or additional processing such as language translation [41,42,64]. Finally, related articles in PubMed are presented based on the topic cloud to incorporate external knowledge.…”

Section: Introductionmentioning

confidence: 99%

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CERC: an interactive content extraction, recognition, and construction tool for clinical and biomedical text

Lee

Uppal

2020

BMC Med Inform Decis Mak

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Background Automated summarization of scientific literature and patient records is essential for enhancing clinical decision-making and facilitating precision medicine. Most existing summarization methods are based on single indicators of relevance, offer limited capabilities for information visualization, and do not account for user specific interests. In this work, we develop an interactive content extraction, recognition, and construction system (CERC) that combines machine learning and visualization techniques with domain knowledge for highlighting and extracting salient information from clinical and biomedical text. Methods A novel sentence-ranking framework multi indicator text summarization, MINTS, is developed for extractive summarization. MINTS uses random forests and multiple indicators of importance for relevance evaluation and ranking of sentences. Indicative summarization is performed using weighted term frequency-inverse document frequency scores of over-represented domain-specific terms. A controlled vocabulary dictionary generated using MeSH, SNOMED-CT, and PubTator is used for determining relevant terms. 35 full-text CRAFT articles were used as the training set. The performance of the MINTS algorithm is evaluated on a test set consisting of the remaining 32 full-text CRAFT articles and 30 clinical case reports using the ROUGE toolkit. Results The random forests model classified sentences as “good” or “bad” with 87.5% accuracy on the test set. Summarization results from the MINTS algorithm achieved higher ROUGE-1, ROUGE-2, and ROUGE-SU4 scores when compared to methods based on single indicators such as term frequency distribution, position, eigenvector centrality (LexRank), and random selection, p < 0.01. The automatic language translator and the customizable information extraction and pre-processing pipeline for EHR demonstrate that CERC can readily be incorporated within clinical decision support systems to improve quality of care and assist in data-driven and evidence-based informed decision making for direct patient care. Conclusions We have developed a web-based summarization and visualization tool, CERC (https://newton.isye.gatech.edu/CERC1/), for extracting salient information from clinical and biomedical text. The system ranks sentences by relevance and includes features that can facilitate early detection of medical risks in a clinical setting. The interactive interface allows users to filter content and edit/save summaries. The evaluation results on two test corpuses show that the newly developed MINTS algorithm outperforms methods based on single characteristics of importance.

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Section: A Customizable Information Extraction and Pre-processing Pipmentioning

confidence: 99%

“…Specifically, CERC is used for information extraction from extracted narrative clinical texts. Below is a short excerpt from our paper [64].…”

Section: A Customizable Information Extraction and Pre-processing Pipmentioning

confidence: 99%

Section: Data Standardization To Snomed-ct Using Snomed-ctmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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CERC: an interactive content extraction, recognition, and construction tool for clinical and biomedical text

Lee

Uppal

2020

BMC Med Inform Decis Mak

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A General-Purpose Multi-stage Multi-group Machine Learning Framework for Knowledge Discovery and Decision Support

Lee,

Yuan,

Man

et al. 2023

Communications in Computer and Information Science

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Multi-Site Best Practice Discovery: From Free Text to Standardized Concepts to Clinical Decisions

Lee

Yuan-bo

et al. 2021

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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An Efficient, Robust, and Customizable Information Extraction and Pre-processing Pipeline for Electronic Health Records

Cited by 5 publications

References 0 publications

CERC: an interactive content extraction, recognition, and construction tool for clinical and biomedical text

CERC: an interactive content extraction, recognition, and construction tool for clinical and biomedical text

A General-Purpose Multi-stage Multi-group Machine Learning Framework for Knowledge Discovery and Decision Support

Multi-Site Best Practice Discovery: From Free Text to Standardized Concepts to Clinical Decisions

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