Physician documentation matters. Using natural language processing to predict mortality in sepsis

Cooley-Rieders, Keaton; Zheng, Kai

doi:10.1016/j.ibmed.2021.100028

Cited by 2 publications

(2 citation statements)

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“…have received the least attention in ICU monitoring. 8 This is mainly because of the challenge of extracting phenotypical information expressed in different contexts and contextual synonyms. For example, the phenotype hypotension can be expressed as ‘a drop in blood pressure’ or ‘BP of 79/48’.…”

Section: Introductionmentioning

confidence: 99%

“…Among the clinical features found in unstructured data, phenotypes [In the medical text, the word ‘phenotype’ refers to deviations from normal morphology, physiology or behaviour7]. have received the least attention in ICU monitoring 8. This is mainly because of the challenge of extracting phenotypical information expressed in different contexts and contextual synonyms.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Clinical utility of automatic phenotype annotation in unstructured clinical notes: intensive care unit use

Zhang¹,

Trujillo²,

Tanwar³

et al. 2022

BMJ Health Care Inform

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ObjectiveClinical notes contain information that has not been documented elsewhere, including responses to treatment and clinical findings, which are crucial for predicting key outcomes in patients in acute care. In this study, we propose the automatic annotation of phenotypes from clinical notes as a method to capture essential information to predict outcomes in the intensive care unit (ICU). This information is complementary to typically used vital signs and laboratory test results.MethodsIn this study, we developed a novel phenotype annotation model to extract the phenotypical features of patients, which were then used as input features of predictive models to predict ICU patient outcomes. We demonstrated and validated this approach by conducting experiments on three ICU prediction tasks, including in-hospital mortality, physiological decompensation and length of stay (LOS) for over 24 000 patients using the Medical Information Mart for Intensive Care (MIMIC-III) dataset.ResultsThe predictive models incorporating phenotypical information achieved 0.845 (area under the curve–receiver operating characteristic (AUC-ROC)) for in-hospital mortality, 0.839 (AUC-ROC) for physiological decompensation and 0.430 (kappa) for LOS, all of which consistently outperformed the baseline models using only vital signs and laboratory test results. Moreover, we conducted a thorough interpretability study showing that phenotypes provide valuable insights at both the patient and cohort levels.ConclusionThe proposed approach demonstrates that phenotypical information complements traditionally used vital signs and laboratory test results and significantly improves the accuracy of outcome prediction in the ICU.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%