Tyche: Stochastic In-Context Learning for Medical Image Segmentation

Existing learning-based solutions to medical image segmentation have two important shortcomings. First, for most new segmentation task, a new model has to be trained or fine-tuned. This requires extensive resources and machine learning expertise, and is therefore often infeasible for medical researchers and clinicians. Second, most existing segmentation methods produce a single deterministic segmentation mask for a given image. In practice however, there is often considerable uncertainty about what constitutes the correct segmentation, and different expert annotators will often segment the same image differently. We tackle both of these problems with Tyche, a model that uses a context set to generate stochastic predictions for previously unseen tasks without the need to retrain. Tyche differs from other in-context segmentation methods in two important ways. (1) We introduce a novel convolution block architecture that enables interactions among predictions. (2) We introduce in-context test-time augmentation, a new mechanism to provide prediction stochasticity. When combined with appropriate model design and loss functions, Tyche can predict a set of plausible diverse segmentation candidates for new or unseen medical images and segmentation tasks without the need to retrain

Real-world and natural history data for drug evaluation in Duchenne muscular dystrophy: suitability of the North Star Ambulatory Assessment for comparisons with external controls

Using external controls based on real-world or natural history data (RWD/NHD) for drug evaluations in Duchenne muscular dystrophy (DMD) is appealing given the challenges of enrolling placebo-controlled trials, especially for multi-year trials. Comparisons to external controls, however, face risks of bias due to differences in outcomes between trial and RWD/NHD settings. To assess this bias empirically, we conducted a multi-institution study comparing mean 48-week changes in North Star Ambulatory Assessment (NSAA) total score between trial placebo arms and RWD/NHD sources, with and without adjustment for baseline prognostic factors. Analyses used data from three placebo arms (235 48-week intervals, N = 235 patients) and three RWD/NHD sources (348 intervals, N = 202 patients). Differences in mean ΔNSAA between placebo arms and RWD/NHD sources were small before adjustment (-1.2 units, 95% CI: [-2.0 -0.5]) and were attenuated and no longer statistically significant after adjustment (0.1 units (95% CI: [-0.6, 0.8]). Results were similar whether adjusting using multivariable regression or propensity score matching. This consistency in ΔNSAA between trial placebo arms and RWD/NHD sources accords with prior findings for the six-minute walk distance, provides a well-validated framework for baseline adjustment of prognostic factors, and supports the suitability of RWD/NHD external controls for drug evaluations in ambulatory DMD

Evaluating Learned and Rule-Based Policies for Hospital Bed Assignment

In many complex sequential decision making problems in healthcare such as hospital bed assignment, resources are limited and shared between patients. Hospital bed assignment is an important decision making problem because a patient's bed assignment influences their medical outcomes, including their risk of developing a healthcare associated infection (HAI). In this thesis, we consider the problem of assigning patients to hospital beds with the goal of reducing the incidence of HAIs. We propose a two part approach to this task: first, use reinforcement learning to learn a function from logged data for assessing different patient and bed pairs, then use this function to design policies for sequentially assigning batches of patients to beds. We develop a simulation to demonstrate this approach and conduct experiments exploring how assumptions about the environment affect the performance of learned and rule-based policies. We examine the performance of weighted importance sampling for off-policy evaluation. Our results show that policies that prioritize patients with the highest risk of poor outcomes outperform purely greedy policies.S.M

Burden of Comorbidities and Healthcare Resource Utilization Among Medicaid-Enrolled Extremely Premature Infants

**Background:** The effect of gestational age (GA) on comorbidity prevalence, healthcare resource utilization (HCRU), and all-cause costs is significant for extremely premature (EP) infants in the United States. **Objectives:** To characterize real-world patient characteristics, prevalence of comorbidities, rates of HCRU, and direct healthcare charges and societal costs among premature infants in US Medicaid programs, with respect to GA and the presence of respiratory comorbidities. **Methods:** Using _International Classification of Diseases, Ninth/Tenth Revision, Clinical Modification_ codes, diagnosis and medical claims data from 6 state Medicaid databases (1997-2018) of infants born at less than 37 weeks of GA (wGA) were collected retrospectively. Data from the index date (birth) up to 2 years corrected age or death, stratified by GA (EP, ≤28 wGA; very premature $$VP$$, >28 to <32 wGA; and moderate to late premature $$M-LP$$, ≥32 to <37 wGA), were compared using unadjusted and adjusted generalized linear models. **Results:** Among 25 573 premature infants (46.1% female; 4462 $$17.4$$ EP; 2904 $$11.4$$ VP; 18&#8239;207 $$71.2$$ M-LP), comorbidity prevalence, HCRU, and all-cause costs increased with decreasing GA and were highest for EP. Total healthcare charges, excluding index hospitalization and all-cause societal costs (US dollars), were 2 to 3 times higher for EP than for M-LP (EP 74 436 vs M-LP 27&#8239;541 and EP 28 504 vs M-LP 15&#8239;892, respectively). **Conclusions:** Complications of preterm birth, including prevalence of comorbidities, HCRU, and costs, increased with decreasing GA and were highest among EP infants during the first 2 years in this US analysis