Domain generalization for enhanced predictions of hospital readmission on unseen domains among patients with diabetes.

A prediction model to assess the risk of hospital readmission can be valuable to identify patients who may benefit from extra care. Developing hospital-specific readmission risk prediction models using local data is not feasible for many institutions. Models developed on data from one hospital may not generalize well to another hospital.

Deep Learning vs Traditional Models for Predicting Hospital Readmission among Patients with Diabetes.

A hospital readmission risk prediction tool for patients with diabetes based on electronic health record (EHR) data is needed. The optimal modeling approach, however, is unclear. In 2,836,569 encounters of 36,641 diabetes patients, deep learning (DL) long short-term memory (LSTM) models predicting unplanned, all-cause, 30-day readmission were developed and compared to several traditional models.

Association of antibiotics in infancy with early childhood obesity.

Importance: Obesity in children and adults is associated with significant health burdens, making prevention a public health imperative. Infancy may be a critical period when environmental factors exert a lasting effect on the risk for obesity; identifying modifiable factors may help to reduce this risk.

Objective: To assess the impact of antibiotics prescribed in infancy (ages 0-23 months) on obesity in early childhood (ages 24-59 months).

Using RxNorm for cross-institutional formulary data normalization within a distributed grid-computing environment.

Within the CTSA (Clinical Translational Sciences Awards) program, academic medical centers are tasked with the storage of clinical formulary data within an Integrated Data Repository (IDR) and the subsequent exposure of that data over grid computing environments for hypothesis generation and cohort selection. Formulary data collected over long periods of time across multiple institutions requires normalization of terms before those data sets can be aggregated and compared. This paper sets forth a solution to the challenge of generating derived aggregated normalized views from large, distributed data sets of clinical formulary data intended for re-use within clinical translational research.

A case-control study of home foreclosure, health conditions, and health care utilization.

Though rates of foreclosure are at a historic high, relatively little is known about the link between foreclosure and health. We performed a case-control study to examine health conditions and health care utilization in the time period prior to foreclosure. Homeowners who received a home foreclosure notice from 2005 to 2008 were matched (by name and address) to a university hospital system in Philadelphia and compared with controls who received care from the hospital system and who lived in the same zip code as cases.

Derivation of malignancy status from ICD-9 codes.

To assess the severity of illness of oncology patients, it is necessary to distinguish patients with a single primary tumor from patients with metastatic disease occurring at a secondary location remote from the primary site. We developed a ranked list of cancer groupings and an algorithm that could distinguish patients with primary and metastatic cancer even if no specific code for secondary cancer was recorded. In patients with metastatic disease, the algorithm should also distinguish the primary site from the secondary site.