Clinical Concept-Based Radiology Reports Classification Pipeline for Lung Carcinoma.
Artificial Intelligence
Big data analytics
Clinical concept extraction
Deep learning
Electronic medical records
Lung carcinoma
Named entity recognition
Natural Language Processing
Radiology reports
Journal
Journal of digital imaging
ISSN: 1618-727X
Titre abrégé: J Digit Imaging
Pays: United States
ID NLM: 9100529
Informations de publication
Date de publication:
06 2023
06 2023
Historique:
received:
31
08
2022
accepted:
24
01
2023
revised:
23
01
2023
medline:
26
6
2023
pubmed:
15
2
2023
entrez:
14
2
2023
Statut:
ppublish
Résumé
Rising incidence and mortality of cancer have led to an incremental amount of research in the field. To learn from preexisting data, it has become important to capture maximum information related to disease type, stage, treatment, and outcomes. Medical imaging reports are rich in this kind of information but are only present as free text. The extraction of information from such unstructured text reports is labor-intensive. The use of Natural Language Processing (NLP) tools to extract information from radiology reports can make it less time-consuming as well as more effective. In this study, we have developed and compared different models for the classification of lung carcinoma reports using clinical concepts. This study was approved by the institutional ethics committee as a retrospective study with a waiver of informed consent. A clinical concept-based classification pipeline for lung carcinoma radiology reports was developed using rule-based as well as machine learning models and compared. The machine learning models used were XGBoost and two more deep learning model architectures with bidirectional long short-term neural networks. A corpus consisting of 1700 radiology reports including computed tomography (CT) and positron emission tomography/computed tomography (PET/CT) reports were used for development and testing. Five hundred one radiology reports from MIMIC-III Clinical Database version 1.4 was used for external validation. The pipeline achieved an overall F1 score of 0.94 on the internal set and 0.74 on external validation with the rule-based algorithm using expert input giving the best performance. Among the machine learning models, the Bi-LSTM_dropout model performed better than the ML model using XGBoost and the Bi-LSTM_simple model on internal set, whereas on external validation, the Bi-LSTM_simple model performed relatively better than other 2. This pipeline can be used for clinical concept-based classification of radiology reports related to lung carcinoma from a huge corpus and also for automated annotation of these reports.
Identifiants
pubmed: 36788196
doi: 10.1007/s10278-023-00787-z
pii: 10.1007/s10278-023-00787-z
pmc: PMC10287609
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
812-826Informations de copyright
© 2023. The Author(s).
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