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Clinical natural language processing
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Clinical interpretation of genetic variants in the context of the patient’s phenotype is becoming the largest component of cost and time expenditure for genome-based diagnosis of rare genetic diseases. Artificial intelligence (AI) holds promise to greatly simplify and speed genome interpretation by integrating predictive methods with the growing knowledge of genetic disease.
19p
vibransone
28-03-2024
3
2
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Rapidly and efficiently identifying critically ill infants for whole genome sequencing (WGS) is a costly and challenging task currently performed by scarce, highly trained experts and is a major bottleneck for application of WGS in the NICU. There is a dire need for automated means to prioritize patients for WGS.
9p
vicwell
29-02-2024
6
1
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Phenotyping algorithms applied to electronic health record (EHR) data enable investigators to identify large cohorts for clinical and genomic research. Algorithm development is often iterative, depends on fallible investigator intuition, and is time- and labor-intensive.
10p
visteverogers
24-06-2023
11
4
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The goal of this study was to develop a practical framework for recognizing and disambiguating clinical abbreviations, thereby improving current clinical natural language processing (NLP) systems’ capability to handle abbreviations in clinical narratives.
8p
visteverogers
24-06-2023
7
3
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The repurposing of electronic health records (EHRs) can improve clinical and genetic research for rare diseases. This paper presents a method to detect family history and negation in narrative reports and evaluates its impact on selecting populations from a clinical data warehouse (CDW).
7p
visteverogers
24-06-2023
2
2
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To develop automated classification methods for eligibility criteria in ClinicalTrials.gov to facilitate patient-trial matching for specific populations such as persons living with HIV or pregnant women.
7p
visteverogers
24-06-2023
6
2
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To develop an open-source information extraction system called Eligibility Criteria Information Extraction (EliIE) for parsing and formalizing free-text clinical research eligibility criteria (EC) following Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) version 5.0.
10p
visteverogers
24-06-2023
11
2
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Widespread application of clinical natural language processing (NLP) systems requires taking existing NLP systems and adapting them to diverse and heterogeneous settings. We describe the challenges faced and lessons learned in adapting an existing NLP system for measuring colonoscopy quality.
6p
visteverogers
24-06-2023
4
2
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Information overload remains a challenge for patients seeking clinical trials. We present a novel system (DQueST) that reduces information overload for trial seekers using dynamic questionnaires.
11p
visteverogers
24-06-2023
2
2
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The goal of the 2018 n2c2 shared task on cohort selection for clinical trials (track 1) is to identify which patients meet the selection criteria for clinical trials. Cohort selection is a particularly demanding task to which natural language processing and deep learning can make a valuable contribution. Our goal is to evaluate several deep learning architectures to deal with this task.
8p
visteverogers
24-06-2023
8
2
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Automated clinical phenotyping is challenging because word-based features quickly turn it into a high-dimensional problem, in which the small, privacy-restricted, training datasets might lead to overfitting. Pretrained embeddings might solve this issue by reusing input representation schemes trained on a larger dataset.
8p
visteverogers
24-06-2023
6
2
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The objective of this study is to characterize the dynamic structure of primary care consultations by identifying typical activities and their inter-relationships to inform the design of automated approaches to clinical documentation using natural language processing and summarization methods.
9p
visteverogers
24-06-2023
5
2
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In this study, the relations between 2 medical concepts are classified by simultaneously learning representations of text segments in the context of sentence syntactic dependency: preceding, concept1, middle, concept2, and succeeding segments. Seg-GCRN was systematically evaluated on the i2b2/VA relation classification challenge datasets.
7p
visteverogers
24-06-2023
7
2
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Alcohol misuse is present in over a quarter of trauma patients. Information in the clinical notes of the electronic health record of trauma patients may be used for phenotyping tasks with natural language processing (NLP) and supervised machine learning. The objective of this study is to train and validate an NLP classifier for identifying patients with alcohol misuse.
8p
visteverogers
24-06-2023
4
2
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Cohort definition is a bottleneck for conducting clinical research and depends on subjective decisions by domain experts. Data-driven cohort definition is appealing but requires substantial knowledge of terminologies and clinical data models. Criteria2Query is a natural language interface that facilitates human-computer collaboration for cohort definition and execution using clinical databases.
12p
visteverogers
24-06-2023
7
2
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The study sought to review recent literature regarding use of speech recognition (SR) technology for clinical documentation and to understand the impact of SR on document accuracy, provider efficiency, institutional cost, and more.
15p
visteverogers
24-06-2023
6
2
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CLAMP – a toolkit for efficiently building customized clinical natural language processing pipelines
Existing general clinical natural language processing (NLP) systems such as MetaMap and Clinical Text Analysis and Knowledge Extraction System have been successfully applied to information extraction from clinical text. However, end users often have to customize existing systems for their individual tasks, which can require substantial NLP skills.
6p
visteverogers
24-06-2023
5
2
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To assess clinical documentation variations across health care institutions using different electronic medical record systems and investigate how they affect natural language processing (NLP) system portability.
7p
visteverogers
24-06-2023
5
2
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To develop a comprehensive value set for documenting and encoding adverse reactions in the allergy module of an electronic health record. This work contributes a value set, harmonized with existing data, to improve the consistency and accuracy of reaction documentation in electronic health records, providing the necessary building blocks for more intelligent clinical decision support for allergies and adverse reactions.
9p
visteverogers
24-06-2023
2
2
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Distributional semantics algorithms, which learn vector space representations of words and phrases from large corpora, identify related terms based on contextual usage patterns. We hypothesize that distributional semantics can speed up lexicon expansion in a clinical domain, radiology, by unearthing synonyms from the corpus.
7p
visteverogers
24-06-2023
5
1
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