Previously submitted to: Journal of Medical Internet Research (no longer under consideration since Sep 21, 2022)
Date Submitted: Aug 12, 2022
Open Peer Review Period: Aug 4, 2022 - Sep 21, 2022
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Identifying encephalopathy in patients admitted to an intensive care unit: going beyond structured information. A proof-of-concept study using natural language processing.
ABSTRACT
Background:
Encephalopathy is a severe co-morbid condition in critically ill patients that includes different clinical constellation of neurological symptoms. However, even for the most recognised form, delirium, this medical condition is rarely recorded in structured fields of electronic health records precluding large and unbiased retrospective studies
Objective:
We aimed to identify patients with encephalopathy using a machine learning-based approach over clinical notes in electronic health records.
Methods:
We used a list of ICD-9 codes and clinical concepts related to encephalopathy to define a cohort of patients from the MIMIC-III dataset. Clinical notes were annotated with MedCAT and vectorized with a bag-of-word approach or word embedding using clinical concepts normalised to standard nomenclatures as features. Machine learning algorithms (support vector machines and random forest) trained with clinical notes from patients who had a diagnosis of encephalopathy (defined by ICD-9 codes) were used to classify patients with clinical concepts related to encephalopathy in their clinical notes but without any ICD-9 relevant code. A random selection of 50 patients were used for internal validation.
Results:
Among 46520 different patients, 7.5% had encephalopathy related ICD-9 codes in all their admissions (group 1), 45% clinical concepts related to encephalopathy only in their clinical notes (group 2) and 38% did not have encephalopathy related concepts neither in structured nor in clinical notes (group 3). Length of stay, mortality rate or number of co-morbid conditions were higher in groups 1 and 2 compared to group 3. The best model to classify patients from group 2 as patients with encephalopathy (SVM using embeddings) had F1 of 85% and predicted 31% patients from group 2 as having encephalopathy with a probability > 90%. Internal validation found a precision ranging from 92-98% depending on the criteria considered
Conclusions:
Natural language processing techniques can leverage relevant clinical information that might help to identify patients with under-recognised clinical disorders such as encephalopathy. In the MIMIC dataset, this approach can increase the cohort for study by thousands of patients with high probability.
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