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   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

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3. Data breach remediation efforts and their implications for hospital quality

   https://doi.org/10.1111/1475-6773.13203  

   Choi, Sung J. ; Johnson, M. Eric ; Lehmann, Christoph U. ( September 2019 , Health Services Research)

   To estimate the relationship between breach remediation efforts and hospital care quality.

   Department of Health and Human Services’ (HHS) public database on hospital data breaches and Medicare Compare's public data on hospital quality measures for 2012‐2016.

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   Hospital time‐to‐electrocardiogram increased as much as 2.7 minutes and 30‐day acute myocardial infarction mortality increased as much as 0.36 percentage points during the 3‐year window following a breach.

   Breach remediation efforts were associated with deterioration in timeliness of care and patient outcomes. Thus, breached hospitals and HHS oversight should carefully evaluate remedial security initiatives to achieve better data security without negatively affecting patient outcomes.

    

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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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