138 citations · 200 across the 9 of their papers we have counts for
12 papers
HEX: Human-in-the-loop Explainability via Deep Reinforcement Learning
Michael T. Lash
The use of machine learning (ML) models in decision-making contexts, particularly those used in high-stakes decision-making, are fraught with issue and peril since a person - not a…
Personalized Cardiovascular Disease Risk Mitigation via Longitudinal Inverse Classification
Michael T. Lash, W. Nick Street
Cardiovascular disease (CVD) is a serious illness affecting millions world-wide and is the leading cause of death in the US. Recent years, however, have seen tremendous growth in t…
Optimal Sepsis Patient Treatment using Human-in-the-loop Artificial Intelligence
Akash Gupta, Michael T. Lash, Senthil K. Nachimuthu
Sepsis is one of the leading causes of death in Intensive Care Units (ICU). The strategy for treating sepsis involves the infusion of intravenous (IV) fluids and administration of…
Deriving Enhanced Geographical Representations via Similarity-based Spectral Analysis: Predicting Colorectal Cancer Survival Curves in Iowa
Michael T. Lash, Min Zhang, Xun Zhou +2
Neural networks are capable of learning rich, nonlinear feature representations shown to be beneficial in many predictive tasks. In this work, we use such models to explore differe…
Prophit: Causal inverse classification for multiple continuously valued treatment policies
Michael T. Lash, Qihang Lin, W. Nick Street
Inverse classification uses an induced classifier as a queryable oracle to guide test instances towards a preferred posterior class label. The result produced from the process is a…
21 Million Opportunities: A 19 Facility Investigation of Factors Affecting Hand Hygiene Compliance via Linear Predictive Models
Michael T. Lash, Jason Slater, Philip M. Polgreen +1
This large-scale study, consisting of 21.3 million hand hygiene opportunities from 19 distinct facilities in 10 different states, uses linear predictive models to expose factors th…