Detecting of a Patient's Condition From Clinical Narratives Using Natural Language Representation.

LE, Thanh-Dung; Noumeir, Rita; Rambaud, Jerome; Sans, Guillaume; Jouvet, Philippe

doi:10.1109/OJEMB.2022.3209900

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Detecting of a Patient's Condition From Clinical Narratives Using Natural Language Representation.

LE, Thanh-Dung; Noumeir, Rita; Rambaud, Jerome et al.

2022 • In IEEE open journal of engineering in medicine and biology, 3, p. 142 - 149

Peer Reviewed verified by ORBi

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https://hdl.handle.net/10993/61924

DOI
10.1109/OJEMB.2022.3209900

PubMed
36712317

arXiV
2104.03969v7

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Keywords :

Clinical natural language processing; cardiac failure; feature selection; imbalance learning; machine learning; Deep learning; Features selection; Language processing; Machine-learning; Medical diagnostic imaging; Natural languages; Performances evaluation; Representation learning; Biomedical Engineering; Computer Science - Computation and Language; eess.SP

Abstract :

[en] The rapid progress in clinical data management systems and artificial intelligence approaches enable the era of personalized medicine. Intensive care units (ICUs) are ideal clinical research environments for such development because they collect many clinical data and are highly computerized. Goal: We designed a retrospective clinical study on a prospective ICU database using clinical natural language to help in the early diagnosis of heart failure in critically ill children. Methods: The methodology consisted of empirical experiments of a learning algorithm to learn the hidden interpretation and presentation of the French clinical note data. This study included 1386 patients' clinical notes with 5444 single lines of notes. There were 1941 positive cases (36% of total) and 3503 negative cases classified by two independent physicians using a standardized approach. Results: The multilayer perceptron neural network outperforms other discriminative and generative classifiers. Consequently, the proposed framework yields an overall classification performance with 89% accuracy, 88% recall, and 89% precision. Conclusions: This study successfully applied learning representation and machine learning algorithms to detect heart failure in a single French institution from clinical natural language. Further work is needed to use the same methodology in other languages and institutions.

Disciplines :

Electrical & electronics engineering

Author, co-author :

LE, Thanh-Dung ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom ; Biomedical Information Processing Lab, École de Technologie SupérieureUniversity of Québec Montreal QB H3G 1M8 Canada ; Research Center at CHU Sainte-Justine HospitalUniversity of Montreal Montreal QB H3T 1J4 Canada

Noumeir, Rita ; Biomedical Information Processing Lab, École de Technologie SupérieureUniversity of Québec Montreal QB H3G 1M8 Canada

Rambaud, Jerome; Research Center at CHU Sainte-Justine HospitalUniversity of Montreal Montreal QB H3T 1J4 Canada

Sans, Guillaume; Research Center at CHU Sainte-Justine HospitalUniversity of Montreal Montreal QB H3T 1J4 Canada

Jouvet, Philippe ; Research Center at CHU Sainte-Justine HospitalUniversity of Montreal Montreal QB H3T 1J4 Canada

External co-authors :

yes

Language :

English

Title :

Detecting of a Patient's Condition From Clinical Narratives Using Natural Language Representation.

Publication date :

2022

Journal title :

IEEE open journal of engineering in medicine and biology

eISSN :

2644-1276

Publisher :

Institute of Electrical and Electronics Engineers Inc., United States

Volume :

Pages :

142 - 149

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

http://xplorestaging.ieee.org/ielx7/8782705/9698118/09903583.pdf?arnumber=9903583

Commentary :

Accepted for publication in IEEE Open Journal of Engineering in Medicine and Biology. arXiv admin note: text overlap with arXiv:2104.03934

Available on ORBilu :

since 03 September 2024

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