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 — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Michael Draugelis United States	8	280	261	176	147	142	15	839
Vincent X. Liu United States	20	135 0.5×	401 1.5×	114 0.6×	152 1.0×	149 1.0×	79	1.3k
Asaf Hanish United States	13	196 0.7×	458 1.8×	108 0.6×	83 0.6×	44 0.3×	16	1.0k
Gad Segal Israel	19	106 0.4×	177 0.7×	164 0.9×	65 0.4×	86 0.6×	96	1.3k
Mohsin Ali United States	15	147 0.5×	161 0.6×	85 0.5×	134 0.9×	194 1.4×	49	1.6k
Sayon Dutta United States	10	111 0.4×	142 0.5×	67 0.4×	71 0.5×	73 0.5×	42	552
Jeremiah S. Hinson United States	20	183 0.7×	382 1.5×	80 0.5×	86 0.6×	109 0.8×	62	1.8k
Jamie S. Hirsch United States	20	140 0.5×	179 0.7×	79 0.4×	215 1.5×	43 0.3×	61	1.3k
Yuan Xu Canada	19	92 0.3×	252 1.0×	78 0.4×	225 1.5×	29 0.2×	79	1.1k
Lili Chan United States	20	111 0.4×	171 0.7×	86 0.5×	56 0.4×	71 0.5×	82	1.2k
Santiago Romero‐Brufau United States	16	168 0.6×	291 1.1×	69 0.4×	14 0.1×	132 0.9×	39	817

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

Teeple, Stephanie, Corey Chivers, Kristin A. Linn, et al.. (2023). 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. BMJ Quality & Safety. 32(9). 503–516. 3 indexed citations

Chivers, Corey, et al.. (2022). Guiding Efficient, Effective, and Patient-Oriented Electrolyte Replacement in Critical Care: An Artificial Intelligence Reinforcement Learning Approach. Journal of Personalized Medicine. 12(5). 661–661. 6 indexed citations

MacKay, Emily J., et al.. (2021). Application of machine learning approaches to administrative claims data to predict clinical outcomes in medical and surgical patient populations. PLoS ONE. 16(6). e0252585–e0252585. 16 indexed citations

Hanson, C. William, et al.. (2021). Remote Monitoring of Critically-Ill Post-Surgical Patients: Lessons from a Biosensor Implementation Trial. Healthcare. 9(3). 343–343. 3 indexed citations

Dumitrascu, Bianca, et al.. (2020). Sparse multi-output Gaussian processes for online medical time series prediction. BMC Medical Informatics and Decision Making. 20(1). 152–152. 35 indexed citations

Weissman, Gary E., Andrew Crane‐Droesch, Corey Chivers, et al.. (2020). Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic. Annals of Internal Medicine. 173(1). 21–28. 194 indexed citations

Chivers, Corey, Yijie Liu, Chalanda N. Evans, et al.. (2020). Prospective validation of a machine learning algorithm to predict short-term mortality among outpatients with cancer.. Journal of Clinical Oncology. 38(15_suppl). 2009–2009. 2 indexed citations

Manz, Christopher R., Jinbo Chen, Yijie Liu, et al.. (2020). Validation of a Machine Learning Algorithm to Predict 180-Day Mortality for Outpatients With Cancer. JAMA Oncology. 6(11). 1723–1723. 76 indexed citations

Courtright, Katherine R., Corey Chivers, Michael Becker, et al.. (2019). Electronic Health Record Mortality Prediction Model for Targeted Palliative Care Among Hospitalized Medical Patients: a Pilot Quasi-experimental Study. Journal of General Internal Medicine. 34(9). 1841–1847. 51 indexed citations

10.

Chivers, Corey, et al.. (2019). Palliative Connect: Triggered Palliative Care Consultation Using an EHR Prediction Model (FR420A). Journal of Pain and Symptom Management. 57(2). 408–409. 1 indexed citations

11.

Parikh, Ravi B., Corey Chivers, Jennifer Braun, et al.. (2019). Derivation and implementation of a machine learning approach to prompt serious illness conversations among outpatients with cancer.. Journal of Clinical Oncology. 37(31_suppl). 131–131. 2 indexed citations

12.

Parikh, Ravi B., Christopher R. Manz, Corey Chivers, et al.. (2019). Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer. JAMA Network Open. 2(10). e1915997–e1915997. 160 indexed citations

13.

Ginestra, Jennifer C., H.M. Giannini, William D. Schweickert, et al.. (2019). Clinician Perception of a Machine Learning–Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock*. Critical Care Medicine. 47(11). 1477–1484. 114 indexed citations

14.

Giannini, H.M., Jennifer C. Ginestra, Corey Chivers, et al.. (2019). A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock: Development, Implementation, and Impact on Clinical Practice*. Critical Care Medicine. 47(11). 1485–1492. 175 indexed citations

15.

Vogel, Jennifer, T. Evans, Joseph M. Braun, et al.. (2017). Development of a Trigger Tool for Identifying Emergency Department Visits in Patients With Lung Cancer. International Journal of Radiation Oncology*Biology*Physics. 99(2). S117–S117. 1 indexed citations

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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