Applying deep neural networks to unstructured text notes in electronic medical records for phenotyping youth depression.

Evid Based Ment Health 2017 08 24;20(3):83-87. Epub 2017 Jul 24.

Centre for Addiction and Mental Health, Toronto, Ontario, Canada.

Background: We report a study of machine learning applied to the phenotyping of psychiatric diagnosis for research recruitment in youth depression, conducted with 861 labelled electronic medical records (EMRs) documents. A model was built that could accurately identify individuals who were suitable candidates for a study on youth depression.

Objective: Our objective was a model to identify individuals who meet inclusion criteria as well as unsuitable patients who would require exclusion.

Methods: Our methods included applying a system that coded the EMR documents by removing personally identifying information, using two psychiatrists who labelled a set of EMR documents (from which the 861 came), using a brute force search and training a deep neural network for this task.

Findings: According to a cross-validation evaluation, we describe a model that had a specificity of 97% and a sensitivity of 45% and a second model with a specificity of 53% and a sensitivity of 89%. We combined these two models into a third one (sensitivity 93.5%; specificity 68%; positive predictive value (precision) 77%) to generate a list of most suitable candidates in support of research recruitment.

Conclusion: Our efforts are meant to demonstrate the potential for this type of approach for patient recruitment purposes but it should be noted that a larger sample size is required to build a truly reliable recommendation system.

Clinical Implications: Future efforts will employ alternate neural network algorithms available and other machine learning methods.

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Source
http://dx.doi.org/10.1136/eb-2017-102688DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5566092PMC
August 2017
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