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Mar 09, 2024 •411 likes •722 views

DR. TED Deep learning Recommendation of Treatment from Electronic Data David Ledbetter Melissa Aczon Randall Wetzel, M.D. Childrens Hospital Los Angeles (CHLA) Virtual Pediatric ICU (VPICU) GTC April 7th 2016 1 Outline Problem

  1. DR. TED Deep learning Recommendation of Treatment from Electronic Data David Ledbetter Melissa Aczon Randall Wetzel, M.D. Children’s Hospital Los Angeles (CHLA) Virtual Pediatric ICU (VPICU) GTC April 7th 2016 1

  2. Outline ● Problem ● Data ● Models ● Results ● Summary 2

  3. Problem For an individual patient, can we recommend the most effective treatment? There’s actually a patient there 3

  4. Traditional Approach ● Doctors generate implicit models ○ Requires significant training ○ Combination of academic, clinical experience, and medical research ● Use model to generate treatment strategies for new patients ○ Limited time (other patients, rapid deteriorations) ○ Limited capacity* to ingest data *Miller. The Magical Number Seven, Plus or Minus Two . Psychological Review, 63 (2): 81-97, 1956 4

  5. Moving Forward ● Generate explicit model from clinical data to predict which treatments will give best patient outcomes ○ Leverage 10+ years of electronic health records (EHR) ■ ~12,000 patient encounters from CHLA PICU ■ (patient, treatment, outcome) triples ● Learn the most important relationships utilizing state-of-the- art information extraction techniques 5

  6. Moving Forward My CPU is a neural-network processor; a learning computer. The more CHLA PICU data I have, the more I learn. 6

  7. DRTED Input Model Output 7

  8. DRTED Input Model Output 8

  9. Data Structure - Overview ● Convert non-uniformly sampled time-series data into image representation ● Image representation enables exploitation via advanced computer vision algorithms 9

  10. Data Structure - Patient Snapshots Labs ● 161 measurements ○ 53 labs/vitals Vitals ○ 108 drugs/interventions Drugs ● 12 hours of data ○ Sampled every 5 minutes ○ (144 samples) Inter. Time 10

  11. DRTED Input Model Output 11

  12. v1: Convolutional Neural Network ● Utilizes patient snapshot as input ‘image’ ● VGG-style architecture ○ Heavily exploit temporal relationships with 1-D convolutions ● Generates mortality prediction given fixed time window ● NVIDIA GTX Titan X used for training 12

  13. v2: Recurrent Neural Network ● Basic structure is a feedback loop Mortality Prediction y m ○ At each time, t, a vector X is input ○ An output is generated and fed back Physiology forecasting y p into the network ● Advantages: Kernel ○ Native comprehension of the temporal dimension ■ Including non-uniform samples Patient Vitals X V ○ Increased temporal memory Patient Treatments X T ○ Formal feedback mechanism ○ Generate predictions for all vitals 13

  14. DRTED Input Model Output 14

  15. Assessment - Mortality ● Results for holdout set of 3372 patients with encounter length of at least 12 hours ○ DRTED AUC - 90.3% ○ PIM 2* AUC - 83.0%** Notes: *Pediatric Index of Mortality **Published PIM 2 AUC 15

  16. Predictions Heart Rate Diastolic Pressure Systolic Pressure ● Predictions generated for 5 key vitals + Mortality ○ Heart Rate ○ Diastolic Blood Pressure ○ Systolic Blood Pressure Respiratory Rate Pulse Oximetry Probability of Survival ○ Respiratory Rate ○ Pulse Oximetry ● Accurate prediction of vitals and mortality enable prediction of treatment Time (hours) Time (hours) Time (hours) effects 16

  17. Predicted Treatment Effect Patient diagnosed with: Cardiac Arrest Cardiomyopathy Epileptic Seizures Utilize machinery to Pneumothorax predict effect of each treatment on patient Eventually treated with Piperacillin Vancomycin Epinephrine Phenylephrine 17

  18. Summary ● Applied deep learning methods on 10+ years of Pediatric ICU data ○ Able to generate state-of-the-art mortality predictions ○ Able to generate physiology predictions ○ Able to generate predictions of treatment/therapy effects ● Framework/machinery is being extended to provide additional Decision Support Services to clinicians 18

  19. Machine Learning in Healthcare Contact: ledbetdr@gmail.com macs.aczon@gmail.com Interested? Machine Learning in Healthcare Conference August 19th, 20th Children’s Hospital Los Angeles http://www.mucmd.org/ 19

  20. Backups 20

  21. Augment Data - Example Original 0-8 Hours 21

  22. Patient Snapshots - High Survive Survivors Sample surviving patients with high predicted probability of survival Labs Vitals Drugs Inter. 22

  23. Patient Snapshots - Low Survive Died Sample non-surviving patients with low predicted probability of survival Labs Vitals Drugs Inter. 23

  24. Patient Snapshots - Low Survive Survived Sample surviving patients with low predicted probability of survival Labs Vitals Drugs Inter. 24

  25. Patient Snapshots - High Survive Died Sample non-surviving patients with high predicted probability of survival Labs Vitals Patient encounter lasts for 4 days but no data during first 72 hours Drugs Inter. 25

  26. Optimization V - h t V || α + β|y - h t y | ● Minimize: ||x t+1 ● First term represents ability to predict future vital readings from current information ● Second term represents ability to predict outcome from current information ● Alpha term represents vector cost weight for vital prediction ● Beta term represents cost weight of mortality prediction 26

  27. Implementation ● Each patient is represented as a sequence of n t (m+1)-length vectors ○ m+1 → # of measurements + Δt ○ n t → # of discrete time steps ● Vitals receive forward fill + median imputation ● Intermittent exogenous inputs are delta functions ○ Continuous drugs/interventions are propagated ● Δt informs the algorithm how far into the future it needs to predict ○ training is allowed to ‘cheat’ - knows when next measure is ○ But that’s OK, we just want to learn the relationships ○ At test time, we specify Δt to predict precise point in time 27

  28. LSTM http://colah.github.io/posts/2015-08-Understanding-LSTMs/ 28

  29. Assessment - Treatment Response (cont.) ● Intrinsic difficulty of assessment is ambiguity of truth ● Best metric would be A/B test ○ Average outcome of patients whose doctors have access to decision aid vs. ○ Average outcome of patient whose doctors do not have access to decision aid ○ Not practical for initial development or iteration ● Instead, develop intuitive quantification ○ Provide adequate feedback for iteration ○ Base on simple assumption: ■ Maximizing frequency of recommendations of actual treatments used in successful cases is good 29

  30. Assessment - Treatment Response ● Compress interventions and drugs into treatment response y = Δ HealthIndex * treatment where: Δ HealthIndex = HealthIndex i+1 - HealthIndex i HealthIndex i is the expected survival at time t i as computed by survival model treatment is a vector: [t 1 , t 2 , …, t n ], with t i ∈ {0, 1} indicating presence of treatment categories ● Elements of y contain: ○ positive values for treatments that contributed to improvement ○ negative values for treatments detrimental to patient condition ○ 0 for treatments not utilized 30

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