Corey Chivers

2.8k citations

33 papers · 1.8k indexed · 1 hit paper · h-index 17

Ecology top 5%
Artificial Intelligence top 5%
Public Health, Environmental and Occupational Health top 5%
Insect Science top 2%
Epidemiology top 10%

Co-authors: Michael Draugelis Brian Leung Susan Harkness Regli Nina O’Connor Juliann E. Aukema Kerry O. Britton Kent Kovacs Jeffrey Englin
Topics: Palliative Care and End-of-Life Issues (12 papers)Machine Learning in Healthcare (7 papers)Cancer survivorship and care (6 papers)
Cited by: Health Informatics Insect Science Family Practice
Journals: Journal of Clinical Oncology Annals of Internal Medicine PLoS ONE
Partner nations: United States Canada United Kingdom

In The Last Decade

Corey Chivers

31 papers receiving 1.7k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Economic Impacts of Non-Native Forest Insects in the Continental United States

2011 478 citations Juliann E. Aukema, Brian Leung et al. PLoS ONE profile →

Peers

Countries citing papers authored by Corey Chivers

Since Specialization

Citations

This map shows the geographic impact of Corey Chivers'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 Corey Chivers with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Corey Chivers more than expected).

Fields of papers citing papers by Corey Chivers

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Corey Chivers. 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 Corey Chivers. The network helps show where Corey Chivers may publish in the future.

Co-authorship network of co-authors of Corey Chivers

This figure shows the co-authorship network connecting the top 25 collaborators of Corey Chivers. A scholar is included among the top collaborators of Corey Chivers 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 Corey Chivers. Corey Chivers is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Intrapartum Electronic Fetal Heart Rate Monitoring to Predict Acidemia at Birth With the Use of Deep Learning 2025 ·Obstetric Anesthesia Digest ·(unknown),(unknown),Gwo‐Hwa Wan,Corey Chivers,(unknown),(unknown)	0
2	Intrapartum electronic fetal heart rate monitoring to predict acidemia at birth with the use of deep learning 2024 ·American Journal of Obstetrics and Gynecology ·(unknown),(unknown),(unknown),Corey Chivers,(unknown),William La Cava	10
3	Long-term Effect of Machine Learning–Triggered Behavioral Nudges on Serious Illness Conversations and End-of-Life Outcomes Among Patients With Cancer 2023 ·JAMA Oncology ·Christopher R. Manz,(unknown),Kan Chen,(unknown),Dylan S. Small,Chalanda N. Evans,Corey Chivers,Susan Harkness Regli,C. William Hanson,Justin E. Bekelman,Jennifer Braun,Charles Rareshide,Nina O’Connor,Pallavi Kumar,Lynn M. Schuchter,Lawrence N. Shulman,Mitesh S. Patel,Ravi B. Parikh	62
4	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
5	Oncologist phenotypes and associations with response to a machine learning-based intervention to increase advance care planning: Secondary analysis of a randomized clinical trial 2022 ·PLoS ONE ·Eric Li,Christopher R. Manz,Yijie Liu,Jinbo Chen,Corey Chivers,Jennifer Braun,Lynn M. Schuchter,Pallavi Kumar,Mitesh S. Patel,Lawrence N. Shulman,Ravi B. Parikh	10
6	Performance drift in a mortality prediction algorithm among patients with cancer during the SARS-CoV-2 pandemic 2022 ·Journal of the American Medical Informatics Association ·Ravi B. Parikh,Yichen Zhang,Likhitha Kolla,Corey Chivers,Katherine R. Courtright,Jingsan Zhu,Amol S. Navathe,Jinbo Chen	12
7	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
8	Two-Stage Approaches to Accounting for Patient Heterogeneity in Machine Learning Risk Prediction Models in Oncology 2021 ·JCO Clinical Cancer Informatics ·(unknown),Ravi B. Parikh,Corey Chivers,Jinbo Chen	0
9	Patient Interaction Phenotypes With an Automated Remote Hypertension Monitoring Program and Their Association With Blood Pressure Control: Observational Study 2020 ·Journal of Medical Internet Research ·Anahita Davoudi,(unknown),Corey Chivers,(unknown),(unknown),Catherine Reitz,Shivan J. Mehta,Krisda H. Chaiyachati,Danielle L. Mowery	10
10	Integrating machine-generated mortality estimates and behavioral nudges to promote serious illness conversations for cancer patients: Design and methods for a stepped-wedge cluster randomized controlled trial 2020 ·Contemporary Clinical Trials ·Christopher R. Manz,Ravi B. Parikh,Chalanda N. Evans,Corey Chivers,Susan Harkness Regli,Justin E. Bekelman,Dylan S. Small,Charles Rareshide,Nina O’Connor,Lynn M. Schuchter,Lawrence N. Shulman,Mitesh S. Patel	12
11	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
12	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
13	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
14	Effect of integrating machine learning mortality estimates with behavioral nudges to increase serious illness conversions among patients with cancer: A stepped-wedge cluster randomized trial. 2020 ·Journal of Clinical Oncology ·(unknown),Ravi B. Parikh,Chalanda N. Evans,Corey Chivers,(unknown),(unknown),Justin E. Bekelman,Dylan S. Small,Charles Rareshide,Nina O’Connor,Lynn M. Schuchter,Lawrence N. Shulman,Mitesh S. Patel	4
15	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
16	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
17	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
18	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
19	Rising complexity and falling explanatory power in ecology 2014 ·Frontiers in Ecology and the Environment ·Étienne Low‐Décarie,Corey Chivers,Mónica Granados	65
20	Economic Impacts of Non-Native Forest Insects in the Continental United Statesbreakdown → 2011 ·PLoS ONE ·Juliann E. Aukema,Brian Leung,Kent Kovacs,Corey Chivers,Kerry O. Britton,Jeffrey Englin,Susan J. Frankel,Robert G. Haight,Thomas P. Holmes,Andrew M. Liebhold,Deborah G. McCullough,Betsy Von Holle	478

About Corey Chivers

Corey Chivers is a scholar working on Health Informatics, Family Practice and Geriatrics and Gerontology, having authored 33 papers that have together received 1.8k indexed citations. Recurring topics across this work include Palliative Care and End-of-Life Issues (12 papers), Machine Learning in Healthcare (7 papers) and Cancer survivorship and care (6 papers). The work is most often cited by research in Health Informatics (221 citations), Insect Science (327 citations) and Family Practice (57 citations). Corey Chivers has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include Michael Draugelis, Brian Leung, Susan Harkness Regli, Nina O’Connor, Juliann E. Aukema, Kerry O. Britton, Kent Kovacs, Jeffrey Englin, Deborah G. McCullough and Susan J. Frankel. Their work appears in journals such as Journal of Clinical Oncology, Annals of Internal Medicine and PLoS ONE.

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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