Improving the dictionary lookup approach for disease normalization using enhanced dictionary and query expansion

Jonnagaddala, Jitendra; Jue, Toni Rose; Chang, Nen Chung; Dai, Hong-Jie

doi:10.1093/database/baw112

Cited by 16 publications

(11 citation statements)

References 39 publications

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“…To resolve this problem, MetaMap allows users to define a list of abbreviations and acronyms. On the other hand, cTAKES does not have such a list [ 21 ]. In this work, we did not use any list of abbreviations with the aim to keep the same configuration for both tools, but the use of this option could help MetaMap improve its results.…”

Section: Discussionmentioning

confidence: 99%

Comparison of MetaMap and cTAKES for entity extraction in clinical notes

Reátegui

Ratté

2018

BMC Med Inform Decis Mak

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BackgroundClinical notes such as discharge summaries have a semi- or unstructured format. These documents contain information about diseases, treatments, drugs, etc. Extracting meaningful information from them becomes challenging due to their narrative format. In this context, we aimed to compare the automatic extraction capacity of medical entities using two tools: MetaMap and cTAKES.MethodsWe worked with i2b2 (Informatics for Integrating Biology to the Bedside) Obesity Challenge data. Two experiments were constructed. In the first one, only one UMLS concept related with the diseases annotated was extracted. In the second, some UMLS concepts were aggregated.ResultsResults were evaluated with manually annotated medical entities. With the aggregation process the result shows a better improvement. MetaMap had an average of 0.88 in recall, 0.89 in precision, and 0.88 in F-score. With cTAKES, the average of recall, precision and F-score were 0.91, 0.89, and 0.89, respectively.ConclusionsThe aggregation of concepts (with similar and different semantic types) was shown to be a good strategy for improving the extraction of medical entities, and automatic aggregation could be considered in future works.

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Section: Discussionmentioning

confidence: 99%

Comparison of MetaMap and cTAKES for entity extraction in clinical notes

Reátegui

Ratté

2018

BMC Med Inform Decis Mak

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“…Finally, although medical entities were mainly extracted using hand crafted rules in this work, another approach worth exploring is to extract the risk factors by employing supervised learning methods using a specific terminology [39]. However, currently there is no specific ontology or terminology for violence related risk factors.…”

Section: Discussionmentioning

confidence: 99%

Exploring associations of clinical and social parameters with violent behaviors among psychiatric patients

Dai

Uddin

et al. 2017

Journal of Biomedical Informatics

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Evidence has revealed interesting associations of clinical and social parameters with violent behaviors of patients with psychiatric disorders. Men are more violent preceding and during hospitalization, whereas women are more violent than men throughout the 3 days following a hospital admission. It has also been proven that mental disorders may be a consistent risk factor for the occurrence of violence. In order to better understand violent behaviors of patients with psychiatric disorders, it is important to investigate both the clinical symptoms and psychosocial factors that accompany violence in these patients. In this study, we utilized a dataset released by the Partners Healthcare and Neuropsychiatric Genome-scale and RDoC Individualized Domains project of Harvard Medical School to develop a unique text mining pipeline that processes unstructured clinical data in order to recognize clinical and social parameters such as age, gender, history of alcohol use, violent behaviors, etc., and explored the associations between these parameters and violent behaviors of patients with psychiatric disorders. The aim of our work was to demonstrate the feasibility of mining factors that are strongly associated with violent behaviors among psychiatric patients from unstructured psychiatric evaluation records using clinical text mining. Experiment results showed that stimulants, followed by a family history of violent behavior, suicidal behaviors, and financial stress were strongly associated with violent behaviors. Key aspects explicated in this paper include employing our text mining pipeline to extract clinical and social factors linked with violent behaviors, generating association rules to uncover possible associations between these factors and violent behaviors, and lastly the ranking of top rules associated with violent behaviors using statistical analysis and interpretation.

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“…Two of the most commonly used knowledge-intensive concept normalization tools, MetaMap (Aronson, 2001) and cTAKES (Savova et al, 2010) both employ rules to first generate lexical variants for each noun phrase and then conduct dictionary look-up for each variant. Several systems (D'Souza and Ng, 2015;Jonnagaddala et al, 2016) have demonstrated that rule-based concept normalization systems achieve performance competitive with other approaches in a sieve-based approach that carefully selects combinations and orders of dictionaries, exact and partial matching, and heuristic rules. However, such rule-based approaches struggle when there are great variations between concept mention and concept, which is common, for example, when comparing social media text to medical ontologies.…”

Section: Related Workmentioning

confidence: 99%

A Generate-and-Rank Framework with Semantic Type Regularization for Biomedical Concept Normalization

Xu¹,

Zhang²,

Bethard³

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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Concept normalization, the task of linking textual mentions of concepts to concepts in an ontology, is challenging because ontologies are large. In most cases, annotated datasets cover only a small sample of the concepts, yet concept normalizers are expected to predict all concepts in the ontology. In this paper, we propose an architecture consisting of a candidate generator and a list-wise ranker based on BERT. The ranker considers pairings of concept mentions and candidate concepts, allowing it to make predictions for any concept, not just those seen during training. We further enhance this list-wise approach with a semantic type regularizer that allows the model to incorporate semantic type information from the ontology during training. Our proposed concept normalization framework achieves stateof-the-art performance on multiple datasets.

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Improving the dictionary lookup approach for disease normalization using enhanced dictionary and query expansion

Cited by 16 publications

References 39 publications

Comparison of MetaMap and cTAKES for entity extraction in clinical notes

Comparison of MetaMap and cTAKES for entity extraction in clinical notes

Exploring associations of clinical and social parameters with violent behaviors among psychiatric patients

A Generate-and-Rank Framework with Semantic Type Regularization for Biomedical Concept Normalization

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