Melissa Jay

1.4k total citations · 1 hit paper
11 papers, 925 citations indexed

About

Melissa Jay is a scholar working on Epidemiology, Artificial Intelligence and Surgery. According to data from OpenAlex, Melissa Jay has authored 11 papers receiving a total of 925 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Epidemiology, 6 papers in Artificial Intelligence and 4 papers in Surgery. Recurrent topics in Melissa Jay's work include Sepsis Diagnosis and Treatment (7 papers), Machine Learning in Healthcare (6 papers) and Hemodynamic Monitoring and Therapy (3 papers). Melissa Jay is often cited by papers focused on Sepsis Diagnosis and Treatment (7 papers), Machine Learning in Healthcare (6 papers) and Hemodynamic Monitoring and Therapy (3 papers). Melissa Jay collaborates with scholars based in United States and United Kingdom. Melissa Jay's co-authors include Ritankar Das, Jacob Calvert, Jana Hoffman, Uli K. Chettipally, Yaniv Kerem, Mitchell D. Feldman, David Shimabukuro, Lisa Shieh, Thomas Desautels and Qingqing Mao and has published in prestigious journals such as Statistics in Medicine, BMJ Open and Computers in Biology and Medicine.

In The Last Decade

Melissa Jay

10 papers receiving 879 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.

Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach

2016 339 citations Thomas Desautels, Jacob Calvert et al. JMIR Medical Informatics profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Melissa Jay United States	8	625	597	136	133	123	11	925
David Shimabukuro United States	11	593 0.9×	491 0.8×	150 1.1×	139 1.0×	116 0.9×	12	1.1k
Yaniv Kerem United States	12	436 0.7×	389 0.7×	171 1.3×	100 0.8×	92 0.7×	13	925
Matthew D. Stanley United States	6	381 0.6×	352 0.6×	131 1.0×	78 0.6×	65 0.5×	8	681
Patrick Thoral Netherlands	15	447 0.7×	351 0.6×	134 1.0×	60 0.5×	79 0.6×	37	966
Katharine E. Henry United States	8	354 0.6×	448 0.8×	99 0.7×	108 0.8×	92 0.7×	14	826
Thomas Desautels United States	9	348 0.6×	388 0.6×	82 0.6×	62 0.5×	91 0.7×	10	633
Andre L. Holder United States	15	678 1.1×	480 0.8×	363 2.7×	112 0.8×	88 0.7×	39	1.4k
Jana Hoffman United States	22	931 1.5×	840 1.4×	229 1.7×	171 1.3×	238 1.9×	41	1.7k
Cara O’Brien United States	14	538 0.9×	233 0.4×	143 1.1×	93 0.7×	79 0.6×	20	1.2k
Fereshteh Razmi United States	2	309 0.5×	275 0.5×	88 0.6×	67 0.5×	52 0.4×	2	516

Countries citing papers authored by Melissa Jay

Since Specialization

Citations

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

Fields of papers citing papers by Melissa Jay

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Melissa Jay

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

All Works

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11 of 11 papers shown

1.

Jay, Melissa, Jacob Oleson, Mary E. Charlton, & Ali Arab. (2021). A Bayesian approach for estimating age‐adjusted rates for low‐prevalence diseases over space and time. Statistics in Medicine. 40(12). 2922–2938. 1 indexed citations

2.

Jay, Melissa & Rebecca A. Betensky. (2021). Displaying survival of patient groups defined by covariate paths: Extensions of the Kaplan‐Meier estimator. Statistics in Medicine. 40(8). 2024–2036. 5 indexed citations

3.

Sewell, Daniel K., et al.. (2020). Predicting an optimal composite outcome variable for Huntington's disease clinical trials. Journal of Applied Statistics. 48(7). 1339–1348.

4.

Mao, Qingqing, Melissa Jay, Jana Hoffman, et al.. (2018). Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ Open. 8(1). e017833–e017833. 229 indexed citations

5.

Desautels, Thomas, Jacob Calvert, Jana Hoffman, et al.. (2017). Using Transfer Learning for Improved Mortality Prediction in a Data-Scarce Hospital Setting. PubMed. 9. 2718907427–2718907427. 40 indexed citations

6.

Calvert, Jacob, Jana Hoffman, Christopher Barton, et al.. (2017). Cost and mortality impact of an algorithm-driven sepsis prediction system. Journal of Medical Economics. 20(6). 646–651. 20 indexed citations

7.

Desautels, Thomas, Jacob Calvert, Jana Hoffman, et al.. (2016). Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach. JMIR Medical Informatics. 4(3). e28–e28. 339 indexed citations breakdown →

8.

Calvert, Jacob, Qingqing Mao, Angela J. Rogers, et al.. (2016). A computational approach to mortality prediction of alcohol use disorder inpatients. Computers in Biology and Medicine. 75. 74–79. 15 indexed citations

9.

Calvert, Jacob, Uli K. Chettipally, Christopher W. Barton, et al.. (2016). A computational approach to early sepsis detection. Computers in Biology and Medicine. 74. 69–73. 184 indexed citations

10.

Calvert, Jacob, Thomas Desautels, Uli K. Chettipally, et al.. (2016). High-performance detection and early prediction of septic shock for alcohol-use disorder patients. Annals of Medicine and Surgery. 8. 50–55. 42 indexed citations

11.

Calvert, Jacob, Qingqing Mao, Jana Hoffman, et al.. (2016). Using electronic health record collected clinical variables to predict medical intensive care unit mortality. Annals of Medicine and Surgery. 11. 52–57. 50 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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