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1. Predictive Modeling of Hospital Readmission: Challenges and Solutions

   https://doi.org/10.1109/TCBB.2021.3089682  

   Wang, Shuwen; Zhu, Xingquan (January 2021, IEEE/ACM Transactions on Computational Biology and Bioinformatics)

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2. Projecting COVID-19 cases and hospital burden in Ohio

   https://doi.org/10.1016/j.jtbi.2022.111404  

   KhudaBukhsh, Wasiur R.; Bastian, Caleb Deen; Wascher, Matthew; Klaus, Colin; Sahai, Saumya Yashmohini; Weir, Mark H.; Kenah, Eben; Root, Elisabeth; Tien, Joseph H.; Rempała, Grzegorz A. (March 2023, Journal of Theoretical Biology)
3. Hospital-Physician Integration and Cardiac Rehabilitation Following Major Cardiovascular Events

   https://doi.org/10.1001/jamanetworkopen.2024.62580  

   Thai, Ngoc H; Post, Brady; Young, Gary J; Noor-E-Alam, Md (March 2025, JAMA Network Open)

   Cardiac rehabilitation (CR) is a medically supervised program designed to improve heart health after a cardiac event. Despite its demonstrated clinical benefits, CR participation among eligible patients remains poor due to low referral rates and individual barriers to care. To evaluate CR participation by patients who receive care from hospital-integrated physicians compared with independent physicians, and subsequently, to examine CR and recurrent cardiac hospitalizations. This retrospective cohort study evaluated Medicare Part A and Part B claims data from calendar years 2016 to 2019. All analyses were conducted between January 1 and April 30, 2024. Patients were included if they had a qualifying event for CR between 2017 and 2018, and qualifying events were identified using diagnosis codes on inpatient claims and procedure codes on outpatient and carrier claims. Eligible patients also had to continuously enroll in fee-for-service Medicare for 12 months or more before and after the index event. Physicians’ integration status and patients’ CR participation were determined during the 12-month follow-up period. The study covariates were ascertained during the 12 months before the index event. ExposureHospital-integration status of the treating physician during follow-up. Main Outcomes and MeasuresPostindex CR participation was determined by qualifying procedure codes on outpatient and carrier claims. ResultsThe study consisted of 28 596 Medicare patients eligible for CR. Their mean (SD) age was 74.0 (9.6) years; 16 839 (58.9%) were male. A total of 9037 patients (31.6%) were treated by a hospital-integrated physician, of which 2995 (33.1%) received CR during follow-up. Logistic regression via propensity score weighting showed that having a hospital-integrated physician was associated with an 11% increase in the odds of receiving CR (odds ratio [OR], 1.11; 95% CI, 1.05-1.18). Additionally, CR participation was associated with a 14% decrease in the odds of recurrent cardiovascular-related hospitalizations (OR, 0.86; 95% CI, 0.81-0.91). The findings of this cohort study suggest that hospital integration has the potential to facilitate greater CR participation and improve heart care. Several factors may help explain this positive association, including enhanced care coordination and value-based payment policies. Further research is needed to assess the association of integration with other appropriate high-quality care activities. 

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4. Microbiome Profiles of Nebulizers in Hospital Use

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5. The Temple University Hospital Digital Pathology Corpus

   https://doi.org/10.1109/SPMB.2018.8615619  

   Houser, D.; Shadhin, G.; Anstotz, R.; Campbell, C.; Obeid, I.; Picone, J.; Farkas, T.; Persidsky, Y.; Jhala, N. (December 2018, Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium)

   Digital pathology is a relatively new field that stands to gain from modern big data and machine learning techniques. In the United States alone, millions of pathology slides are created and interpreted by a human expert each year, suggesting that there is ample data available to support machine learning research. However, the relevant corpora that currently exist contain only hundreds of images, not enough to develop sophisticated deep learning models. This lack of publicly accessible data also hinders the advancement of clinical science. Our digital pathology corpus is an effort to place a large amount of clinical pathology images collected at Temple University Hospital into the public domain to support the development of automatic interpretation technology. The goal of this ambitious project is to create a corpus of 1M images. We have already released 10,000 images from 600 clinical cases. In this paper, we describe the corpus under development and discuss some of the underlying technology that was developed to support this project. 

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