Making Deep Learning Powered Healthcare a Reality

Will Jack, Co-founder

LARGE CLEAN DATA SETS

DEPLOYING TECHNOLOGY AT THE POINT OF CARE

INTERPRETABLE MODELS

INTERPRETABLE MODELS

Deep models are necessary for accuracy but are hard to interpret

FLU

sneeze

weight

headache

no fatigue

age

sneeze

headache

no fatigue

Model Data and Prediction Explanation Human Decision

Explainer (LIME)

Model Interpretability: LIME

Source: Marco Tulio Ribeiro

Source: Marco Tulio Ribeiro

Model Interpretability: LIME

P ( ) = 0.54 P ( ) = 0.07 P ( ) = 0.05

Source: Marco Tulio Ribeiro

Model Interpretability: LIME

Model Interpretability: Distillation

Target

Output

Input

Deep Learning

Model

Mimic Model

y y y y

x

nn nn

nn

m

x x

Source: Che et al. “Interpretable Deep Models for ICU Outcome Prediction”

Model DistillationBLACK BOXDEEP MODEL

0

1 2

3 4 5 6

OUTPUTS

INPUTS

Model Interpretability: Distillation

Source: Che et al. “Interpretable Deep Models for ICU Outcome Prediction”

LARGE CLEAN DATA SETS

HIGHLY SILOEDLACKS

STANDARDIZATION DIRTY DATA

Interpretation Model: Distillation

Source: Marco Tulio Ribeiro

DEPLOYING TECHNOLOGY AT THE POINT OF CARE

Deploying to health systems is a

NIGHTMARE

VIRTUAL PHYSICIAN’S ASSISTANT

Remy

Remy collects granular, structured data around the diagnostic thought process

INTAKE HISTORY TRIAGE FOLLOW UP

COMPREHENSIVE DATASET

OUTCOMES

Remedy is building a

for automated diagnosis and treatment plan design

based on

Questions?