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

arXiv: https://arxiv.org/abs/2104.03969 · HTML: https://mlsanity.com/paper/2104.03969/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 92 references that are in the map.
- First posted on arXiv 8 Apr 2021.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs](https://mlsanity.com/paper/1903.00161/) (2019)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [Scalable and accurate deep learning for electronic health records](https://mlsanity.com/paper/1801.07860/) (2018)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [Do NLP Models Know Numbers? Probing Numeracy in Embeddings](https://mlsanity.com/paper/1909.07940/) (2019)
- [Challenges in Deploying Machine Learning: a Survey of Case Studies](https://mlsanity.com/paper/2011.09926/) (2020)
- [Convergence Analysis of Two-layer Neural Networks with ReLU Activation](https://mlsanity.com/paper/1705.09886/) (2017)
- [Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review](https://mlsanity.com/paper/1908.05780/) (2019)
- [Scattertext: a Browser-Based Tool for Visualizing how Corpora Differ](https://mlsanity.com/paper/1703.00565/) (2017)
- [An Empirical Investigation of Contextualized Number Prediction](https://mlsanity.com/paper/2011.07961/) (2020)
- [Early Prediction of Acute Kidney Injury in Critical Care Setting Using Clinical Notes](https://mlsanity.com/paper/1811.02757/) (2018)
- [Bayes Imbalance Impact Index: A Measure of Class Imbalanced Dataset for Classification Problem](https://mlsanity.com/paper/1901.10173/) (2019)
- [How to Generate a Good Word Embedding?](https://mlsanity.com/paper/1507.05523/) (2015)
- [The Effects of Data Size and Frequency Range on Distributional Semantic Models](https://mlsanity.com/paper/1609.08293/) (2016)
- [Predicting Heart Failure Readmission from Clinical Notes Using Deep Learning](https://mlsanity.com/paper/1912.10306/) (2019)
- [Effective Representations of Clinical Notes](https://mlsanity.com/paper/1705.07025/) (2017)
- [Morphological Skip-Gram: Using morphological knowledge to improve word representation](https://mlsanity.com/paper/2007.10055/) (2020)

## Built on by (most cited first, 8 of 8)

- [Adaptation of Autoencoder for Sparsity Reduction From Clinical Notes Representation Learning](https://mlsanity.com/paper/2209.12831/) (2022)
- [The Impact of LoRA Adapters on LLMs for Clinical Text Classification Under Computational and Data Constraints](https://mlsanity.com/paper/2407.19299/) (2024)
- [Improving Transformer Performance for French Clinical Notes Classification Using Mixture of Experts on a Limited Dataset](https://mlsanity.com/paper/2303.12892/) (2023)
- [Label Propagation Techniques for Artifact Detection in Imbalanced Classes using Photoplethysmogram Signals](https://mlsanity.com/paper/2308.08480/) (2023)
- [A Novel Transformer-Based Self-Supervised Learning Method to Enhance Photoplethysmogram Signal Artifact Detection](https://mlsanity.com/paper/2401.01013/) (2024)
- [Multi-objective Representation for Numbers in Clinical Narratives: A CamemBERT-Bio-Based Alternative to Large-Scale LLMs](https://mlsanity.com/paper/2405.18448/) (2024)
- [Detecci'on Autom'atica de Patolog'ias en Notas Cl'inicas en Espa~nol Combinando Modelos de Lenguaje y Ontolog'ias M'edicos](https://mlsanity.com/paper/2410.00616/) (2024)
- [Automatic detection of diseases in Spanish clinical notes combining medical language models and ontologies](https://mlsanity.com/paper/2412.03176/) (2024)

## Abstract (arXiv)

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.