Clinical Utility of the Automatic Phenotype Annotation in Unstructured Clinical Notes: ICU Use Cases

TL;DR

This study introduces an automatic phenotype annotation method from clinical notes to enhance ICU outcome predictions, demonstrating improved accuracy and interpretability over traditional models using vital signs and labs.

Contribution

The paper presents a novel phenotype annotation model that effectively incorporates phenotypic features into predictive models for ICU patient outcomes, validated on a large dataset.

Findings

Phenotype-based models outperform baseline in predicting mortality and decompensation.

Incorporating phenotypes improves predictive accuracy (AUC-ROC up to 0.845).

Phenotypes offer valuable interpretability insights.

Abstract

Objective: Clinical notes contain information not present elsewhere, including drug response and symptoms, all of which are highly important when predicting key outcomes in acute care patients. We propose the automatic annotation of phenotypes from clinical notes as a method to capture essential information, which is complementary to typically used vital signs and laboratory test results, to predict outcomes in the Intensive Care Unit (ICU). Methods: We develop a novel phenotype annotation model to annotate phenotypic features of patients which are then used as input features of predictive models to predict ICU patient outcomes. We demonstrate and validate our approach conducting experiments on three ICU prediction tasks including in-hospital mortality, physiological decompensation and length of stay for over 24,000 patients by using MIMIC-III dataset. Results: The predictive models incorporating phenotypic information achieve 0.845 (AUC-ROC) to predict in-hospital mortality, 0.839 (AUC-ROC) for physiological decompensation and 0.430 (Kappa) for length of stay, all of which consistently outperform the baseline models leveraging only vital signs and laboratory test results. Moreover, we conduct a thorough interpretability study, showing that phenotypes provide valuable insights at the patient and cohort levels. Conclusion: The proposed approach demonstrates phenotypic information complements traditionally used vital signs and laboratory test results, improving significantly forecast of outcomes in the ICU.