Applying deep neural networks to unstructured text notes in electronic medical records for phenotyping youth depression
By J. Geraci, P. Wilansky, V. De Luca, Anvesh Roy, J. Kennedy, J. Strauss. 2017
67 citations recorded by the source corpus.
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.
Subfields: Applied Psychology, Artificial Intelligence, Social Psychology
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In Artificial Intelligence
Citations in the corpus
- The need for uncertainty quantification in machine-assisted medical decision making (2019, background): “…for the analysis of images, recurrent neural networks (RNNs) for analysing time series and prediction, and sequence recognizers (for example, long short-term memory units (LSTMs) 15 ) for the analysis of text, though the architecture of the network is itself often the subject of exploration 16 .”
- Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review (2019, methodology): “In particular, Geraci et al [101] apply deep neural networks to EMRs to identify suitable candidates for a study on youth depression.”