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Siddharth Biswal

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AAAI Conference 2020 Conference Paper

CONAN: Complementary Pattern Augmentation for Rare Disease Detection

  • Limeng Cui
  • Siddharth Biswal
  • Lucas M. Glass
  • Greg Lever
  • Jimeng Sun
  • Cao Xiao

Rare diseases affect hundreds of millions of people worldwide but are hard to detect since they have extremely low prevalence rates (varying from 1/1, 000 to 1/200, 000 patients) and are massively underdiagnosed. How do we reliably detect rare diseases with such low prevalence rates? How to further leverage patients with possibly uncertain diagnosis to improve detection? In this paper, we propose a Complementary pattern Augmentation (CONAN) framework for rare disease detection. CONAN combines ideas from both adversarial training and max-margin classification. It first learns self-attentive and hierarchical embedding for patient pattern characterization. Then, we develop a complementary generative adversarial networks (GAN) model to generate candidate positive and negative samples from the uncertain patients by encouraging a max-margin between classes. In addition, CONAN has a disease detector that serves as the discriminator during the adversarial training for identifying rare diseases. We evaluated CONAN on two disease detection tasks. For low prevalence inflammatory bowel disease (IBD) detection, CONAN achieved. 96 precision recall area under the curve (PR-AUC) and 50. 1% relative improvement over the best baseline. For rare disease idiopathic pulmonary fibrosis (IPF) detection, CONAN achieves. 22 PR-AUC with 41. 3% relative improvement over the best baseline.

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AAAI Conference 2020 Conference Paper

Doctor2Vec: Dynamic Doctor Representation Learning for Clinical Trial Recruitment

  • Siddharth Biswal
  • Cao Xiao
  • Lucas M. Glass
  • Elizabeth Milkovits
  • Jimeng Sun

Massive electronic health records (EHRs) enable the success of learning accurate patient representations to support various predictive health applications. In contrast, doctor representation was not well studied despite that doctors play pivotal roles in healthcare. How to construct the right doctor representations? How to use doctor representation to solve important health analytic problems? In this work, we study the problem on clinical trial recruitment, which is about identifying the right doctors to help conduct the trials based on the trial description and patient EHR data of those doctors. We propose Doctor2Vec which simultaneously learns 1) doctor representations from EHR data and 2) trial representations from the description and categorical information about the trials. In particular, Doctor2Vec utilizes a dynamic memory network where the doctor’s experience with patients are stored in the memory bank and the network will dynamically assign weights based on the trial representation via an attention mechanism. Validated on large real-world trials and EHR data including 2, 609 trials, 25K doctors and 430K patients, Doctor2Vec demonstrated improved performance over the best baseline by up to 8. 7% in PR-AUC. We also demonstrated that the Doctor2Vec embedding can be transferred to benefit data insufficiency settings including trial recruitment in less populated/newly explored country with 13. 7% improvement or for rare diseases with 8. 1% improvement in PR-AUC.

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