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Detecting of a patient's condition from clinical narratives using natural language representation

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Le, Thanh Dung, Noumeir, Rita, Rambaud, Jérôme, Sans, Guillaume et Jouvet, Philippe. 2022. « Detecting of a patient's condition from clinical narratives using natural language representation ». IEEE Open Journal of Engineering in Medicine and Biology.
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Abstract

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 ma- chine 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.

Item Type: Peer reviewed article published in a journal
Professor:
Professor
Noumeir, Rita
Affiliation: Génie électrique
Date Deposited: 02 Nov 2022 19:13
Last Modified: 18 Nov 2022 15:39
URI: https://espace2.etsmtl.ca/id/eprint/25706

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