7 research outputs found
Relative Sparsity for Medical Decision Problems
Existing statistical methods can estimate a policy, or a mapping from
covariates to decisions, which can then instruct decision makers (e.g., whether
to administer hypotension treatment based on covariates blood pressure and
heart rate). There is great interest in using such data-driven policies in
healthcare. However, it is often important to explain to the healthcare
provider, and to the patient, how a new policy differs from the current
standard of care. This end is facilitated if one can pinpoint the aspects of
the policy (i.e., the parameters for blood pressure and heart rate) that change
when moving from the standard of care to the new, suggested policy. To this
end, we adapt ideas from Trust Region Policy Optimization (TRPO). In our work,
however, unlike in TRPO, the difference between the suggested policy and
standard of care is required to be sparse, aiding with interpretability. This
yields ``relative sparsity," where, as a function of a tuning parameter,
, we can approximately control the number of parameters in our
suggested policy that differ from their counterparts in the standard of care
(e.g., heart rate only). We propose a criterion for selecting ,
perform simulations, and illustrate our method with a real, observational
healthcare dataset, deriving a policy that is easy to explain in the context of
the current standard of care. Our work promotes the adoption of data-driven
decision aids, which have great potential to improve health outcomes.Comment: 53 pages, 7 figures, 2 table
Predicting Acute Kidney Injury at Hospital Re-entry Using High-dimensional Electronic Health Record Data
Acute Kidney Injury (AKI), a sudden decline in kidney function, is associated
with increased mortality, morbidity, length of stay, and hospital cost. Since
AKI is sometimes preventable, there is great interest in prediction. Most
existing studies consider all patients and therefore restrict to features
available in the first hours of hospitalization. Here, the focus is instead on
rehospitalized patients, a cohort in which rich longitudinal features from
prior hospitalizations can be analyzed. Our objective is to provide a risk
score directly at hospital re-entry. Gradient boosting, penalized logistic
regression (with and without stability selection), and a recurrent neural
network are trained on two years of adult inpatient EHR data (3,387 attributes
for 34,505 patients who generated 90,013 training samples with 5,618 cases and
84,395 controls). Predictions are internally evaluated with 50 iterations of
5-fold grouped cross-validation with special emphasis on calibration, an
analysis of which is performed at the patient as well as hospitalization level.
Error is assessed with respect to diagnosis, race, age, gender, AKI
identification method, and hospital utilization. In an additional experiment,
the regularization penalty is severely increased to induce parsimony and
interpretability. Predictors identified for rehospitalized patients are also
reported with a special analysis of medications that might be modifiable risk
factors. Insights from this study might be used to construct a predictive tool
for AKI in rehospitalized patients. An accurate estimate of AKI risk at
hospital entry might serve as a prior for an admitting provider or another
predictive algorithm.Comment: In revisio
Visualizing nationwide variation in medicare Part D prescribing patterns
Abstract Background To characterize the regional and national variation in prescribing patterns in the Medicare Part D program using dimensional reduction visualization methods. Methods Using publicly available Medicare Part D claims data, we identified and visualized regional and national provider prescribing profile variation with unsupervised clustering and t-distributed stochastic neighbor embedding (t-SNE) dimensional reduction techniques. Additionally, we examined differences between regionally representative prescribing patterns for major metropolitan areas. Results Distributions of prescribing volume and medication diversity were highly skewed among over 800,000 Medicare Part D providers. Medical specialties had characteristic prescribing patterns. Although the number of Medicare providers in each state was highly correlated with the number of Medicare Part D enrollees, some states were enriched for providers with > 10,000 prescription claims annually. Dimension-reduction, hierarchical clustering and t-SNE visualization of drug- or drug-class prescribing patterns revealed that providers cluster strongly based on specialty and sub-specialty, with large regional variations in prescribing patterns. Major metropolitan areas had distinct prescribing patterns that tended to group by major geographical divisions. Conclusions This work demonstrates that unsupervised clustering, dimension-reduction and t-SNE visualization can be used to analyze and visualize variation in provider prescribing patterns on a national level across thousands of medications, revealing substantial prescribing variation both between and within specialties, regionally, and between major metropolitan areas. These methods offer an alternative system-wide and pattern-centric view of such data for hypothesis generation, visualization, and pattern identification