Michael T. Lash

Michael T. Lash

♦ Assistant Professor at the University of Kansas School of Busines ♦ Technology, Operations, and AI Consultant

Publications

Predicting mobility using limited data during early stages of a pandemic

Published in Journal of Business Research (JBR), 2023

We examine the factors driving retail mobility in disparate geographical locations across the USA during the COVID-19 pandemic using linear predictive models; we propose a novel method to conduct this analysis.

Recommended citation: M.T. Lash, S. Sajeesh, O.M. Araz, Predicting mobility using limited data during early stages of a pandemic, Journal of Business Research (JBR), 157, 2023. http://michael-lash.github.io/files/jbr-2023.pdf

Optimal Sepsis Patient Treatment using Human-in-the-loop Artificial Intelligence

Published in Expert Systems with Applications (ESWA), 2021

In this paper we propose a method for optimizing IV fluid dosages for septic patients in the ICU.

Recommended citation: A. Gupta, M.T. Lash, S.K. Nachimuthu, Optimal Sepsis Patient Treatment using Human-in-the-oop Artificial Intelligence, Expert Systems with Applications (ESWA), 169:1-14, 2021. http://michael-lash.github.io/files/optimal_sepsis_patient_treatment.pdf

Personalized Cardiovascular Disease Risk Mitigation via Longitudinal Inverse Classification

Published in 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2020

We propose an inverse classification framework for longitudinal data to take advantage of an instance's progression over time.

Recommended citation: M.T. Lash and W.N. Street, Personalized Cardiovascular Disease Risk Mitigation via Longitudinal Inverse Classification, in Bioinformatics and Biomedicine (BIBM), 2020 IEEE International Conference on, pp. 2610-2617, 2020. http://michael-lash.github.io/files/personalized_cvd_ic_bibm2020.pdf

21 Million Opportunities: A 19 Facility Investigation of Factors Affecting Hand Hygiene Compliance via Linear Predictive Models

Published in Journal of Healthcare Informatics Research (JHIR), 2019

This paper extends the analysis of our 2017 ICHI paper investigating health care worker hand hygiene compliance.

Recommended citation: M.T. Lash, J. Slater, P.M. Polgreen, and A.M. Segre, 21 Million Opportunities: A 19 Facility Investigation of Factors Affecting Hand Hygiene Compliance via Linear Predictive Models, Journal of Healthcare Informatics Research (JHIR), 3(4):393-413, 2019. http://michael-lash.github.io/files/21_million_opportunities.pdf

Deriving Enhanced Geographical Representations via Similarity-based Spectral Analysis: Predicting Colorectal Cancer Survival Curves in Iowa

Published in International Journal of Data Mining and Bioinformatics (IJDMB), 2019

This work extends the method and analysis of our 2017 BIBM paper; we propose a method to learn from geographical data to predict colorectal cancer survival curves for patients in the US state of Iowa.

Recommended citation: M.T. Lash, M. Zhang, X. Zhou, C.F. Lynch, and W.N. Street, Deriving Enhanced Geographical Representations via Similarity-based Spectral Analysis: Predicting Colorectal Cancer SurvivalCurves in Iowa, International Journal of Data Mining and Bioinformatics (IJDMB), 21(3):183-211, 2018. http://michael-lash.github.io/files/ijdmb-2019.pdf

Learning Rich Geographical Representations: Predicting Colorectal Cancer Survival in the State of Iowa

Published in 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2017

In this work, we propose a method to learn latent representations from spatial (georaphic) data to predict colorectal cancer survival curves, specifically focusing on data covering the US state of Iowa.

Recommended citation: M.T. Lash, Y. Sun, X. Zhou, C.F. Lynch, and W.N. Street, Learning Rich Geographical Representations: Predicting Colorectal Cancer Survival in the State of Iowa, in Bioinformatics and Biomedicine (BIBM), 2017 IEEE International Conference on, pp. 778-785, 2017. http://michael-lash.github.io/files/rich_geo_reps_bibm_2017.pdf

A budget-constrained inverse classification framework for smooth classifiers

Published in 2017 IEEE International Conference on Data Mining Workshops (ICDMW), 2017

We propose a method and framework for the inverse classification problem that assumes the model in question is differentiable with an L-Lipschitz continuous gradient.

Recommended citation: M.T. Lash, Q. Lin, W.N. Street and J.G. Robinson, A budget-constrained inverse classification framework for smooth classifiers, in Data Mining Workshops (ICDMW), 2017 IEEE International Conference on, pp. 1184-1193, 2017. http://michael-lash.github.io/files/budget_constrained_icdmw2017.pdf

A Large-Scale Exploration of Factors Affecting Hand Hygiene Compliance Using Linear Predictive Models

Published in 2017 International Conference on ealthcare Informatics (ICHI), 2017

We examine the factors that influence hand hygiene compliance in hospitals utilizing real-world hand hygiene sensor data from 19 different facilities.

Recommended citation: M.T. Lash, J. Slater, P.M. Polgreen, and A.M. Segre, A Large-Scale Exploration of Factors Affecting Hand Hygiene Compliance Using Linear Predictive Models, in Healthcare Informatics (ICHI), 2017 International Conference on, pp. 66-73, 2017. http://michael-lash.github.io/files/hh_linear_models_ichi_2017_pub.pdf

A web-based registry for patients with sarcoidosis

Published in Sarcoidosis, vasculitis, and diffuse lung diseases, 2017

This work proposes a data repository for patients suffering from the disease, sarcoidosis. The data is subsequently analyzed to tease out insights.

Recommended citation: A.K. Gerke, F. Tang, M.T. Lash, J. Schappet, E. Phillips and P.M. Polgreen, A web-based registry for patients with sarcoidosis, Sarcoidosis vasculitis and diffuse lung diseases (SVDLD), 34(1):26-34, 2017. http://michael-lash.github.io/files/sarcoid_paper.pdf

Generalized Inverse Classification

Published in Proceedings of the 2017 SIAM International Conference on Data Mining (SDM), 2017

We propose an updated optimization methodology to generalize the inverse classification involving only very minor assupmtions. The proposed method and framework can be used with virtually any classifier.

Recommended citation: M.T. Lash, Q. Lin, W.N. Street, J.G. Robinson and J. Ohlmann, Generalized Inverse Classification, in Proceedings of the 2017 SIAM International Conference on Data Mining (SDM), pp. 162-170, 2017. http://michael-lash.github.io/files/lash_sdm_2017.pdf

Early predictions of movie success: The who, what, and when of profitability

Published in Journal of Management Information Systems (JMIS), 2016

This paper investigates the predictability of movie success (profit) utilizing features (variables) strictly available during the pre-production stage. We investigate the features that are important to the prediction problem and propose a "Movie Investor Assurance System" (MIAS).

Recommended citation: M.T. Lash and K. Zhao, Early predictions of movie success: The who, what, and when of profitability, Journal of Management Information Systems (JMIS), 33(3):874-903, 2016. https://www.tandfonline.com/doi/abs/10.1080/07421222.2016.1243969

Early prediction of movie success-what, who, and when

Published in Proceedings of the 2015 International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction (SBP), 2015

This paper is a preliminary study investigating the drivers of movie success from a pre-production disposition.

Recommended citation: M.T. Lash, S. Fu, S. Wang and K. Zhao, Early prediction of movie success-What, who, and when, in Proceedings of the 2015 International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction (SBP), 2015. http://michael-lash.github.io/files/sbp-2015.pdf