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Case Reports
. 2023 Jan 26;23(1):20.
doi: 10.1186/s12911-023-02117-3.

Entity and relation extraction from clinical case reports of COVID-19: a natural language processing approach

Shaina Raza  1   2 , Brian Schwartz  3   4
Affiliations
Case Reports

Entity and relation extraction from clinical case reports of COVID-19: a natural language processing approach

Shaina Raza et al. BMC Med Inform Decis Mak. .

Abstract

Background: Extracting relevant information about infectious diseases is an essential task. However, a significant obstacle in supporting public health research is the lack of methods for effectively mining large amounts of health data.

Objective: This study aims to use natural language processing (NLP) to extract the key information (clinical factors, social determinants of health) from published cases in the literature.

Methods: The proposed framework integrates a data layer for preparing a data cohort from clinical case reports; an NLP layer to find the clinical and demographic-named entities and relations in the texts; and an evaluation layer for benchmarking performance and analysis. The focus of this study is to extract valuable information from COVID-19 case reports.

Results: The named entity recognition implementation in the NLP layer achieves a performance gain of about 1-3% compared to benchmark methods. Furthermore, even without extensive data labeling, the relation extraction method outperforms benchmark methods in terms of accuracy (by 1-8% better). A thorough examination reveals the disease's presence and symptoms prevalence in patients.

Conclusions: A similar approach can be generalized to other infectious diseases. It is worthwhile to use prior knowledge acquired through transfer learning when researching other infectious diseases.

Keywords: Artificial intelligence; COVID-19; Data cohort; Named entity; Natural language processing; Relation extraction; Transfer learning.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Proposed framework for pandemic surveillance
Fig. 2
Fig. 2
Proposed model for named entity recognition
Fig. 3
Fig. 3
Zero-shot learning-based transformer model for relation extraction
Fig. 4
Fig. 4
Frequency of COVID-19 symptoms
Fig. 5
Fig. 5
Distribution of most frequent medical complications in the population
Fig. 6
Fig. 6
Condition prevalence related to different disease syndromes (Cerebrovascular, Cardiovascular, Pulmonary, Psychological). Bars represent the number of respondents who experienced each symptom at any point in their illness
Fig. 7
Fig. 7
COVID-19 hospitalization by race
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Fig. 8
Temporal relations in a text
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Fig. 9
Adverse drug effect with Paxlovid drug
See this image and copyright information in PMC

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