4 research outputs found
auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data
Applications of machine learning in healthcare often require working with
time-to-event prediction tasks including prognostication of an adverse event,
re-hospitalization or death. Such outcomes are typically subject to censoring
due to loss of follow up. Standard machine learning methods cannot be applied
in a straightforward manner to datasets with censored outcomes. In this paper,
we present auton-survival, an open-source repository of tools to streamline
working with censored time-to-event or survival data. auton-survival includes
tools for survival regression, adjustment in the presence of domain shift,
counterfactual estimation, phenotyping for risk stratification, evaluation, as
well as estimation of treatment effects. Through real world case studies
employing a large subset of the SEER oncology incidence data, we demonstrate
the ability of auton-survival to rapidly support data scientists in answering
complex health and epidemiological questions
Forecasting Response to Treatment with Deep Learning and Pharmacokinetic Priors
Forecasting healthcare time series is crucial for early detection of adverse
outcomes and for patient monitoring. Forecasting, however, can be difficult in
practice due to noisy and intermittent data. The challenges are often
exacerbated by change points induced via extrinsic factors, such as the
administration of medication. We propose a novel encoder that informs deep
learning models of the pharmacokinetic effects of drugs to allow for accurate
forecasting of time series affected by treatment. We showcase the effectiveness
of our approach in a task to forecast blood glucose using both realistically
simulated and real-world data. Our pharmacokinetic encoder helps deep learning
models surpass baselines by approximately 11% on simulated data and 8% on
real-world data. The proposed approach can have multiple beneficial
applications in clinical practice, such as issuing early warnings about
unexpected treatment responses, or helping to characterize patient-specific
treatment effects in terms of drug absorption and elimination characteristics
Robust Rule Learning for Reliable and Interpretable Insight into Expertise Transfer Opportunities
Intensive care in hospitals is distributed to different units that care for patient populations reflecting specific comorbidities, treatments, and outcomes. Unit expertise can be shared to potentially improve the quality of methods and outcomes for patients across units. We propose an algorithmic rule pruning approach for use in building short lists of human-interpretable rules that reliably identify patient beneficiaries of expertise transfers in the form of machine learning risk models. Our experimental results, obtained with two intensive care monitoring datasets, demonstrate the potential utility of the proposed method in practice