2023
DOI: 10.3389/frai.2022.1051724
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Clinical concept recognition: Evaluation of existing systems on EHRs

Abstract: ObjectiveThe adoption of electronic health records (EHRs) has produced enormous amounts of data, creating research opportunities in clinical data sciences. Several concept recognition systems have been developed to facilitate clinical information extraction from these data. While studies exist that compare the performance of many concept recognition systems, they are typically developed internally and may be biased due to different internal implementations, parameters used, and limited number of systems includ… Show more

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Cited by 3 publications
(2 citation statements)
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“…14,15 Furthermore, current methods for handling information extraction tasks in clinical NLP often lack robustness, leading to suboptimal performance. 16 This is especially evident in cancer-related data, where phenotype extraction from public clinical data does not translate to cancer text sourced from proprietary institutional data, which tend to be unprocessed compared to the cleaned public data . In addition, the limited availability of labeled, publicly accessible cancer EHR text leaves an important domain unexplored for NLP.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…14,15 Furthermore, current methods for handling information extraction tasks in clinical NLP often lack robustness, leading to suboptimal performance. 16 This is especially evident in cancer-related data, where phenotype extraction from public clinical data does not translate to cancer text sourced from proprietary institutional data, which tend to be unprocessed compared to the cleaned public data . In addition, the limited availability of labeled, publicly accessible cancer EHR text leaves an important domain unexplored for NLP.…”
Section: Introductionmentioning
confidence: 99%
“…15,16 Moreover, current methods often lack robustness, leading to suboptimal performance. [15][16][17][18][19] In addition, limited availability of labeled, publicly accessible cancer EHR text leaves an important domain underexplored for NLP.…”
Section: Introductionmentioning
confidence: 99%