Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Jan 15, 2020
Open Peer Review Period: Jan 15, 2020 - Feb 17, 2020
Date Accepted: Mar 25, 2020
(closed for review but you can still tweet)
Use of Clinical Notes and Machine Learning to Predict Onset of Dementia
Clinical trials need efficient tools to assist in recruiting patients at risk of Alzheimer’s Disease and related dementia (ADRD). Early detection can also assist patients with financial planning for long-term care. Clinical notes are an important under-utilized source of information in machine learning models due to the cost of collection and complexity of analysis.
This study investigates using de-identified clinical notes from multiple hospital systems collected over 10 years to augment retrospective machine learning models of ADRD risk.
The models use two years of data to predict a future outcome of ADRD onset. Notes data are provided in a de-identified format with specific terms and sentiments. Terms in notes are embedded into a 100-dimensional vector space to identify clusters of related terms and abbreviations that differ across hospital systems and individual clinicians.
When using notes, AUC improved from 85% to 94% and positive predictive value (PPV) increased from 45% to 68% in the model at disease onset. Models with notes improved in both AUC and PPV in years 3-6 when notes volume was largest, results are mixed in years 7 and 8 with smallest cohorts.
While notes helped in the short term, the presence of ADRD symptomatic terms years earlier than onset adds evidence to other studies that clinicians under-code ADRD diagnoses. De-identified clinical notes increase the accuracy of risk models. Clinical notes collected across multiple hospital systems via natural language processing can be merged using post-processing techniques to aid model accuracy.
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