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Project TitleInterpretable Deep Learning Framework for Predictive Modeling
Track Code2017-057
Short Description
Abstract
 
Tagsmachine learning, AI framework, prediction, knowledge-distillation, model interpretability, mimic learning, multitask learning, reinforcement learning, gradient boosting trees, Personalized healthcare, phenotyping
 
Posted DateOct 9, 2017 1:09 PM

Market Opportunity

Exponential growth in Electronic Healthcare Records (EHR) has resulted in the urgent need for discovery of meaningful data-driven representations and patterns of diseases in Computational Phenotyping research. Deep Learning models have shown superior performance for robust prediction in computational phenotyping tasks, but suffer from the issue of model interpretability which is crucial for clinicians involved in decision-making.

USC Solution

USC researchers have developed a novel knowledge-distillation approach called Interpretable Mimic Learning, to learn interpretable features for making robust prediction while mimicking the performance of deep learning models. This approach provides interpretable models that help primary care providers, physicians and clinical experts in monitoring and decision-making for patient care. The technology can be successfully applied not only to healthcare, but also to other applications such as speech processing, computer vision, finance or marketing.

Value Proposition

  • Achieves or exceeds state-of-the-art prediction performance

  • Provides interpretable features and decision rules

  • Phenotype discovery for clinical decision making

  • Better quality of patient care

  • Faster adoptability among clinical staff

Applications

  • Predictive modeling

  • Data mining

  • Personalized Healthcare

  • Computer vision

  • Speech Processing

  • Finance

  • Marketing

Stage of Development

  • Experimentally validated

  • Available for exclusive and non-exclusive license

Intellectual Property

Status:

Provisional patent application filed


Key Publications:

Distilling Knowledge from Deep Networks with Applications to Healthcare Domain

Contact Information

Nikolaus Traitler, Licensing Associate

USC Stevens Center for Innovation

(213) 821-3550

traitler@usc.edu

Files

File Name Description
NCD 2017-057 - Interpretable Deep Learning Framework for Predictive Modeling.pdf None Download