Michael Draugelis

1.6k total citations
15 papers, 839 citations indexed

About

Michael Draugelis is a scholar working on Oncology, Artificial Intelligence and Public Health, Environmental and Occupational Health. According to data from OpenAlex, Michael Draugelis has authored 15 papers receiving a total of 839 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Oncology, 6 papers in Artificial Intelligence and 5 papers in Public Health, Environmental and Occupational Health. Recurrent topics in Michael Draugelis's work include Machine Learning in Healthcare (6 papers), Palliative Care and End-of-Life Issues (5 papers) and Global Cancer Incidence and Screening (3 papers). Michael Draugelis is often cited by papers focused on Machine Learning in Healthcare (6 papers), Palliative Care and End-of-Life Issues (5 papers) and Global Cancer Incidence and Screening (3 papers). Michael Draugelis collaborates with scholars based in United States. Michael Draugelis's co-authors include Corey Chivers, Susan Harkness Regli, Nina O’Connor, Asaf Hanish, Michael J. Lynch, Craig A. Umscheid, William D. Schweickert, H.M. Giannini, Patrick J. Donnelly and Jennifer C. Ginestra and has published in prestigious journals such as Journal of Clinical Oncology, Annals of Internal Medicine and PLoS ONE.

In The Last Decade

Michael Draugelis

15 papers receiving 820 citations

Peers

Countries citing papers authored by Michael Draugelis

Since Specialization

Citations

This map shows the geographic impact of Michael Draugelis's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Michael Draugelis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Draugelis more than expected).

Fields of papers citing papers by Michael Draugelis

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael Draugelis. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Michael Draugelis. The network helps show where Michael Draugelis may publish in the future.

Co-authorship network of co-authors of Michael Draugelis

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Draugelis. A scholar is included among the top collaborators of Michael Draugelis based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Michael Draugelis. Michael Draugelis is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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15 of 15 papers shown

#	Work	Indexed citations
1	Evaluating equity in performance of an electronic health record-based 6-month mortality risk model to trigger palliative care consultation: a retrospective model validation analysis 2023 ·BMJ Quality & Safety ·Stephanie Teeple, Corey Chivers, Kristin A. Linn, Scott D. Halpern, Nwamaka D. Eneanya, Michael Draugelis, Katherine R. Courtright	3
2	Guiding Efficient, Effective, and Patient-Oriented Electrolyte Replacement in Critical Care: An Artificial Intelligence Reinforcement Learning Approach 2022 ·Journal of Personalized Medicine ·(unknown), (unknown), Corey Chivers, Michael Draugelis, C. William Hanson, Barbara E. Engelhardt, Krzysztof Laudański	6
3	Application of machine learning approaches to administrative claims data to predict clinical outcomes in medical and surgical patient populations 2021 ·PLoS ONE ·Emily J. MacKay, (unknown), Corey Chivers, Michael Draugelis, (unknown), Nimesh D. Desai, Peter W. Groeneveld	16
4	Remote Monitoring of Critically-Ill Post-Surgical Patients: Lessons from a Biosensor Implementation Trial 2021 ·Healthcare ·(unknown), (unknown), C. William Hanson, Michael Draugelis, Krzysztof Laudański	3
5	Sparse multi-output Gaussian processes for online medical time series prediction 2020 ·BMC Medical Informatics and Decision Making ·(unknown), Bianca Dumitrascu, (unknown), Corey Chivers, Michael Draugelis, Kai Li, Barbara E. Engelhardt	35
6	Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic 2020 ·Annals of Internal Medicine ·Gary E. Weissman, Andrew Crane‐Droesch, Corey Chivers, (unknown), Asaf Hanish, Michael Z. Levy, (unknown), (unknown), Michael Draugelis, George L. Anesi, Patrick J. Brennan, Jason D. Christie, C. William Hanson, Mark E. Mikkelsen, Scott D. Halpern	194
7	Prospective validation of a machine learning algorithm to predict short-term mortality among outpatients with cancer. 2020 ·Journal of Clinical Oncology ·(unknown), Corey Chivers, Yijie Liu, (unknown), (unknown), Chalanda N. Evans, Charles Rareshide, Michael Draugelis, Jennifer Braun, Amol S. Navathe, Pallavi Kumar, Justin E. Bekelman, Mitesh S. Patel, Nina O’Connor, Lynn M. Schuchter, Lawrence N. Shulman, Ravi B. Parikh	2
8	Validation of a Machine Learning Algorithm to Predict 180-Day Mortality for Outpatients With Cancer 2020 ·JAMA Oncology ·Christopher R. Manz, Jinbo Chen, Yijie Liu, Corey Chivers, Susan Harkness Regli, Jennifer Braun, Michael Draugelis, C. William Hanson, Lawrence N. Shulman, Lynn M. Schuchter, Nina O’Connor, Justin E. Bekelman, Mitesh S. Patel, Ravi B. Parikh	76
9	Electronic Health Record Mortality Prediction Model for Targeted Palliative Care Among Hospitalized Medical Patients: a Pilot Quasi-experimental Study 2019 ·Journal of General Internal Medicine ·Katherine R. Courtright, Corey Chivers, Michael Becker, Susan Harkness Regli, (unknown), Michael Draugelis, Nina O’Connor	51
10	Palliative Connect: Triggered Palliative Care Consultation Using an EHR Prediction Model (FR420A) 2019 ·Journal of Pain and Symptom Management ·(unknown), Corey Chivers, (unknown), Susan Harkness Regli, Michael Draugelis, Nina O’Connor	1
11	Derivation and implementation of a machine learning approach to prompt serious illness conversations among outpatients with cancer. 2019 ·Journal of Clinical Oncology ·Ravi B. Parikh, (unknown), Corey Chivers, (unknown), Jennifer Braun, Joshua Jones, Ronac Mamtani, Michael Draugelis, Justin E. Bekelman, Amol S. Navathe, Mitesh S. Patel, Lawrence N. Shulman, Lynn M. Schuchter, Nina O’Connor	2
12	Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer 2019 ·JAMA Network Open ·Ravi B. Parikh, Christopher R. Manz, Corey Chivers, Susan Harkness Regli, Jennifer Braun, Michael Draugelis, Lynn M. Schuchter, Lawrence N. Shulman, Amol S. Navathe, Mitesh S. Patel, Nina O’Connor	160
13	Clinician Perception of a Machine Learning–Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock* 2019 ·Critical Care Medicine ·Jennifer C. Ginestra, H.M. Giannini, William D. Schweickert, (unknown), Michael J. Lynch, (unknown), Corey Chivers, Michael Draugelis, Patrick J. Donnelly, Barry D. Fuchs, Craig A. Umscheid	114
14	A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock: Development, Implementation, and Impact on Clinical Practice* 2019 ·Critical Care Medicine ·H.M. Giannini, Jennifer C. Ginestra, Corey Chivers, Michael Draugelis, Asaf Hanish, William D. Schweickert, Barry D. Fuchs, (unknown), Michael J. Lynch, Patrick J. Donnelly, (unknown), Neil O. Fishman, C. William Hanson, Craig A. Umscheid	175
15	Development of a Trigger Tool for Identifying Emergency Department Visits in Patients With Lung Cancer 2017 ·International Journal of Radiation OncologyBiologyPhysics ·Jennifer Vogel, T. Evans, Joseph M. Braun, Asaf Hanish, Michael Draugelis, Susan Harkness Regli, Michael G. Becker, (unknown), John C. Kucharczuk, Lawrence N. Shulman, Peter Gabriel, Abigail T. Berman	1

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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