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Harry Hochheiser on Muck Rack

Harry Hochheiser

As seen in: Nature, Wiley Online Library, PLOS, Elsevier, MDPI, Human Resources for Health, Medscape, Pittsburgh Post-Gazette, Pittsburgh Tribune-Review, medRxiv and
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Articles

An evaluation of a Bayesian method to track outbreaks of known and novel influenza-like illnesses

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By John M. Aronis, Marian G. Michaels, Harry Hochheiser, Gregory Cooper, Jessi Espino
| Nature Verified
Abstract Tracking influenza and similar respiratory diseases is an important problem in public health and clinical medicine. The problem is complicated by the clinical similarity and co-occurrence of many of these illnesses. Additionally, recent history has shown that detecting new or reemergent diseases, such as COVID-19, is of paramount importance.
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Multi‐Model Ensembles in Infectious Disease and Public Health: Methods, Interpretation, and Implementation in R

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By Li Shandross, Emily Howerton, Lucie Contamin, Harry Hochheiser
| Wiley Online Library Verified
1 Introduction Predictions of future outcomes are essential to planning and decision making, yet generating reliable predictions of the future is challenging. One method for overcoming this challenge is combining predictions across multiple, independent models. These combination methods (also called aggregation or ensembling) have been repeatedly shown to produce predictions that are more accurate [1, 2] and more consistent [3] than individual models.
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Raising awareness of potential biases in medical machine learning: Experience from a Datathon

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By Harry Hochheiser, Jesse Klug, Thomas Mathie, Tom Pollard
| PLOS
Discussion Despite significant awareness of the potential challenges and harms associated with inappropriately-biased medical AI/ML models, appropriate strategies for identifying and addressing bias are not as well understood.
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