1. Joseph D. Romano, MPhil Columbia University Twitter: #AMIA2017 #S23
Deep recurrent neural networks identify transgender patients
Oral Presentations – Methods for Identification, Classification, and Association using EHR Data
S23
Joseph D. Romano, MPhil
Columbia University
Twitter: #AMIA2017 #S23

2. Disclosure
I have no relevant relationships with commercial interests to disclose.
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3. Learning Objectives
After participating in this session the learner should be better able to:
Conceptualize a recurrent neural network text classifier, and see how it can be applied to transgender patient classification.
Understand the need for data-driven methods to improve healthcare for transgender patients.
Understand that deep learning models do not address the ethical issues presented by tasks such as transgender status classification.
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4. The transgender health crisis
Transgender individuals experience unique health disparities
Lacking adequate subpopulation research due to historical stigmatization
Health care professionals often untrained in LGBT health
Specific physical and psychological comorbidities more common
It is challenging to identify retrospective transgender cohorts
‘Transgender’ often not coded in health information systems
Fear of stigmatization may lead to lack of disclosure
Increased privacy concerns, particularly regarding EHR data
Institute of Medicine (US). National Academies Press (US);2011. (PMID: )
Kenrick approached us
Based on some of his previous work
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5. Recurrent Neural Networks
Accepts an ordered sequence as input
In our case, a sequence of embedded words
Returns a sequence as output
For sequence classification, discard all but the last item in the output sequence
What is a neural network?
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6. Vectorizing words via embedding
Mikolov, T et. al. NIPS. 2013;23:
2 primary benefits:
- reduces dimensionality
- models correlations between words
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7. Note classification pipeline
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8. Implementation LSTM network written in Keras (Tensorflow back-end)
Embedding layer  LSTM layer  Fully connected layer
Embedding dimensionality: 64
LSTM output dimensionality: 100
Activation functions:
LSTM layer: Hard sigmoid
Fully connected layer: Sigmoid
578,101 free parameters
Trained on CentOS Linux server with 4x Nvidia Tesla P100 GPU Accelerators
14,336 total CUDA cores
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9. Implementation
Targets
Inputs
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10. Results: Cohort and note characteristics
EHR Cohort
Cases: 39 manually-identified transgender patients
Controls: 400 randomly selected patients with clinical notes
Free-text clinical notes
Obtained all notes for included patients
Tokenized; removed numbers, proper nouns, punctuation
Left-pad/truncate notes to 1000 words
33/67% train-test split
Each patients’ notes in either train or test set, never both
Train word2vec embeddings on entire set of notes
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11. Results: Classifier performance
Metric
Score
Accuracy
0.901
Precision
0.830
Recall
0.737
F1-Score
0.780
AUC ROC
0.940
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12. Results: Word embeddings
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13. Results: Accuracy and training loss
Training epoch: 1 2 3 4 5
Model trains (5 epochs) in minutes
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14. Comparison to stroke classification
Acute ischemic stroke
Metric
Score
Accuracy
0.901
AUC ROC
0.940
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15. Limitations and future improvements
We need far more data!
37 patients so far–we must be overfitting
How do we find more patients? grep approach is primitive
Leverage emerging techniques to extract knowledge from the learned networks
Neural networks are hard to introspect; no “beta coefficient” equivalent
Eventually, incorporate into clinical decision support
See a clinical note, evaluate, trigger alert if likely transgender
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16. Application to multiple institutions
Does our model translate to other hospital systems?
If not, how about the word embeddings?
Major opportunity to improve training data size issues
Different institutions/EHR systems implement gender differently
NYP/Weill Cornell Medical Center: patient-reported gender with transgender options
Stuck in IRB purgatory
Use cutting-edge techniques to advance privacy guarantees
Generative Adversarial Networks and/or Variational Autoencoders
Differential privacy analysis
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17. Ethical considerations
Essential to address the ethical concerns associated with automated identification of transgender patients
Misuse could lead to patient discrimination
Reidentification of training patients may be possible
Gender is complicated, and imposing labels on patients may be counterproductive
See S57: Oral Presentations, first presentation:
“The Use of Informatics to Reduce Disparities in Transgender Health” (Kenrick Cato, PhD, RN)
8:30 AM-8:48 AM; Tuesday (Fairchild)
Cato, K et. al. J Empir Res Hum Res Ethics. 2016;11(3):
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18. Acknowledgements Tatonetti Lab Kenrick Cato, PhD*
Nicholas Tatonetti, PhD*
Rami Vanguri, PhD*
Kayla Quinnies, PhD
Theresa Koleck, PhD
Yun Hao
Phyllis Thangaraj
Alexandre Yahi
Fernanda Polubriaginof, MD
Nick Giangreco
Jenna Kefeli
Jing Ai
Katie LaRow
Kenrick Cato, PhD*
*Coauthors
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19. AMIA is the professional home for more than 5,400 informatics professionals, representing frontline clinicians, researchers, public health experts and educators who bring meaning to data, manage information and generate new knowledge across the research and healthcare enterprise.
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20. Email me at: jdr2160@cumc.columbia.edu
Thank you!
me at: