6,771 research outputs found
CONAN: Complementary Pattern Augmentation for Rare Disease Detection
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 best baseline. For rare
disease idiopathic pulmonary fibrosis (IPF) detection, CONAN achieves .22
PR-AUC with 41.3% relative improvement over the best baseline
Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19
Forecasting influenza in a timely manner aids health organizations and
policymakers in adequate preparation and decision making. However, effective
influenza forecasting still remains a challenge despite increasing research
interest. It is even more challenging amidst the COVID pandemic, when the
influenza-like illness (ILI) counts are affected by various factors such as
symptomatic similarities with COVID-19 and shift in healthcare seeking patterns
of the general population. Under the current pandemic, historical influenza
models carry valuable expertise about the disease dynamics but face
difficulties adapting. Therefore, we propose CALI-Net, a neural transfer
learning architecture which allows us to 'steer' a historical disease
forecasting model to new scenarios where flu and COVID co-exist. Our framework
enables this adaptation by automatically learning when it should emphasize
learning from COVID-related signals and when it should learn from the
historical model. Thus, we exploit representations learned from historical ILI
data as well as the limited COVID-related signals. Our experiments demonstrate
that our approach is successful in adapting a historical forecasting model to
the current pandemic. In addition, we show that success in our primary goal,
adaptation, does not sacrifice overall performance as compared with
state-of-the-art influenza forecasting approaches.Comment: Appears in AAAI-2
Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning
Understanding human behavior from observed data is critical for transparency
and accountability in decision-making. Consider real-world settings such as
healthcare, in which modeling a decision-maker's policy is challenging -- with
no access to underlying states, no knowledge of environment dynamics, and no
allowance for live experimentation. We desire learning a data-driven
representation of decision-making behavior that (1) inheres transparency by
design, (2) accommodates partial observability, and (3) operates completely
offline. To satisfy these key criteria, we propose a novel model-based Bayesian
method for interpretable policy learning ("Interpole") that jointly estimates
an agent's (possibly biased) belief-update process together with their
(possibly suboptimal) belief-action mapping. Through experiments on both
simulated and real-world data for the problem of Alzheimer's disease diagnosis,
we illustrate the potential of our approach as an investigative device for
auditing, quantifying, and understanding human decision-making behavior
KindMed: Knowledge-Induced Medicine Prescribing Network for Medication Recommendation
Extensive adoption of electronic health records (EHRs) offers opportunities
for its use in various clinical analyses. We could acquire more comprehensive
insights by enriching an EHR cohort with external knowledge (e.g., standardized
medical ontology and wealthy semantics curated on the web) as it divulges a
spectrum of informative relations between observed medical codes. This paper
proposes a novel Knowledge-Induced Medicine Prescribing Network (KindMed)
framework to recommend medicines by inducing knowledge from myriad
medical-related external sources upon the EHR cohort, rendering them as medical
knowledge graphs (KGs). On top of relation-aware graph representation learning
to unravel an adequate embedding of such KGs, we leverage hierarchical sequence
learning to discover and fuse clinical and medicine temporal dynamics across
patients' historical admissions for encouraging personalized recommendations.
In predicting safe, precise, and personalized medicines, we devise an attentive
prescribing that accounts for and associates three essential aspects, i.e., a
summary of joint historical medical records, clinical condition progression,
and the current clinical state of patients. We exhibited the effectiveness of
our KindMed on the augmented real-world EHR cohorts, etching leading
performances against graph-driven competing baselines
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