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

TL;DR

This study uses natural language processing and machine learning to detect heart failure in critically ill children from French clinical notes, achieving high accuracy and recall in an ICU setting.

Contribution

It introduces a novel framework combining clinical natural language processing with neural networks for early heart failure diagnosis in ICU patients.

Findings

Achieved 89% accuracy in classification

Outperformed other classifiers with neural networks

Demonstrated feasibility in French clinical data

Abstract

The rapid progress in clinical data management systems and artificial intelligence approaches enable the era of personalized medicine. Intensive care units (ICUs) are the ideal clinical research environment for such development because they collect many clinical data and are highly computerized environments. 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. 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. 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. This study successfully applied learning representation and machine learning algorithms to detect heart failure from clinical natural language in a single French institution. Further work is needed to use the same methodology in other institutions and other languages.