Jana Hoffman

2.6k total citations · 1 hit paper
41 papers, 1.7k citations indexed

About

Jana Hoffman is a scholar working on Epidemiology, Artificial Intelligence and Surgery. According to data from OpenAlex, Jana Hoffman has authored 41 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Epidemiology, 13 papers in Artificial Intelligence and 7 papers in Surgery. Recurrent topics in Jana Hoffman's work include Sepsis Diagnosis and Treatment (21 papers), Machine Learning in Healthcare (13 papers) and COVID-19 diagnosis using AI (5 papers). Jana Hoffman is often cited by papers focused on Sepsis Diagnosis and Treatment (21 papers), Machine Learning in Healthcare (13 papers) and COVID-19 diagnosis using AI (5 papers). Jana Hoffman collaborates with scholars based in United States, United Kingdom and Belgium. Jana Hoffman's co-authors include Ritankar Das, Jacob Calvert, Melissa Jay, Uli K. Chettipally, Qingqing Mao, Emily Pellegrini, Yaniv Kerem, Mitchell D. Feldman, Abigail Green‐Saxena and David Shimabukuro and has published in prestigious journals such as Circulation, Critical Care Medicine and Medicine.

In The Last Decade

Jana Hoffman

40 papers receiving 1.6k 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
Jana Hoffman United States	22	931	840	277	238	229	41	1.7k
Jacob Calvert United States	23	1.0k 1.1×	928 1.1×	273 1.0×	238 1.0×	251 1.1×	57	2.1k
Uli K. Chettipally United States	21	888 1.0×	709 0.8×	232 0.8×	203 0.9×	325 1.4×	42	1.9k
Ritankar Das United States	27	1.3k 1.4×	1.2k 1.5×	372 1.3×	348 1.5×	343 1.5×	64	2.6k
Andre L. Holder United States	15	678 0.7×	480 0.6×	147 0.5×	88 0.4×	363 1.6×	39	1.4k
Steven Horng United States	14	657 0.7×	1.2k 1.5×	607 2.2×	298 1.3×	226 1.0×	34	2.8k
David Shimabukuro United States	11	593 0.6×	491 0.6×	106 0.4×	116 0.5×	150 0.7×	12	1.1k
Omar Badawi United States	20	1.1k 1.2×	854 1.0×	155 0.6×	224 0.9×	469 2.0×	53	2.7k
Melissa Jay United States	8	625 0.7×	597 0.7×	95 0.3×	123 0.5×	136 0.6×	11	925
Jesse D. Raffa United States	16	1.6k 1.7×	546 0.7×	84 0.3×	133 0.6×	230 1.0×	43	2.4k
Yaniv Kerem United States	12	436 0.5×	389 0.5×	91 0.3×	92 0.4×	171 0.7×	13	925

Countries citing papers authored by Jana Hoffman

Since Specialization

Citations

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

Fields of papers citing papers by Jana Hoffman

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jana Hoffman

This figure shows the co-authorship network connecting the top 25 collaborators of Jana Hoffman. A scholar is included among the top collaborators of Jana Hoffman 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 Jana Hoffman. Jana Hoffman 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

Allen, Angier, et al.. (2022). Prediction of diabetic kidney disease with machine learning algorithms, upon the initial diagnosis of type 2 diabetes mellitus. BMJ Open Diabetes Research & Care. 10(1). e002560–e002560. 50 indexed citations

Thapa, Rahul, Anurag Garikipati, Sepideh Shokouhi, et al.. (2022). Predicting Falls in Long-term Care Facilities: Machine Learning Study. JMIR Aging. 5(2). e35373–e35373. 22 indexed citations

Le, Sidney, Abigail Green‐Saxena, Jacob Calvert, et al.. (2022). Mortality, disease progression, and disease burden of acute kidney injury in alcohol use disorder subpopulation. The American Journal of the Medical Sciences. 364(1). 46–52. 2 indexed citations

Zelin, Nicole S., Anurag Garikipati, Emily Pellegrini, et al.. (2022). A comparative analysis of machine learning approaches to predict C. difficile infection in hospitalized patients. American Journal of Infection Control. 50(3). 250–257. 10 indexed citations

Lam, Carson, Chak Foon Tso, Abigail Green‐Saxena, et al.. (2021). Semisupervised Deep Learning Techniques for Predicting Acute Respiratory Distress Syndrome From Time-Series Clinical Data: Model Development and Validation Study. JMIR Formative Research. 5(9). e28028–e28028. 13 indexed citations

Thapa, Rahul, Anurag Garikipati, Anna Siefkas, et al.. (2021). Early prediction of severe acute pancreatitis using machine learning. Pancreatology. 22(1). 43–50. 32 indexed citations

Lam, Charles, Anna Siefkas, Nicole S. Zelin, et al.. (2021). Using Machine Learning as a Precision Medicine Approach for Remdesivir and Corticosteroids as COVID-19 Pharmacotherapies. Clinical Therapeutics. 1 indexed citations

Garikipati, Anurag, Nicole S. Zelin, Emily Pellegrini, et al.. (2021). Prediction of short-term mortality in acute heart failure patients using minimal electronic health record data. BioData Mining. 14(1). 23–23. 10 indexed citations

Rahmani, Keyvan, Anurag Garikipati, Gina Barnes, et al.. (2021). Early prediction of central line associated bloodstream infection using machine learning. American Journal of Infection Control. 50(4). 440–445. 24 indexed citations

10.

Garikipati, Anurag, Anna Siefkas, Gina Barnes, et al.. (2021). A machine learning approach to predicting risk of myelodysplastic syndrome. Leukemia Research. 109. 106639–106639. 15 indexed citations

11.

Lam, Carson, Anna Siefkas, Nicole S. Zelin, et al.. (2021). Machine Learning as a Precision-Medicine Approach to Prescribing COVID-19 Pharmacotherapy with Remdesivir or Corticosteroids. Clinical Therapeutics. 43(5). 871–885. 11 indexed citations

12.

Allen, Angier, Samson Mataraso, Anna Siefkas, et al.. (2020). A Racially Unbiased, Machine Learning Approach to Prediction of Mortality: Algorithm Development Study. JMIR Public Health and Surveillance. 6(4). e22400–e22400. 35 indexed citations

13.

Burdick, Hoyt, Carson Lam, Samson Mataraso, et al.. (2020). Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial. Computers in Biology and Medicine. 124. 103949–103949. 96 indexed citations

14.

Burdick, Hoyt, Carol Gu, Jonathan Roberts, et al.. (2020). Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals. BMC Medical Informatics and Decision Making. 20(1). 276–276. 29 indexed citations

15.

Le, Sidney, Emily Pellegrini, Abigail Green‐Saxena, et al.. (2020). Supervised machine learning for the early prediction of acute respiratory distress syndrome (ARDS). Journal of Critical Care. 60. 96–102. 58 indexed citations

16.

Le, Sidney, Jana Hoffman, Christopher Barton, et al.. (2019). Pediatric Severe Sepsis Prediction Using Machine Learning. Frontiers in Pediatrics. 7. 413–413. 83 indexed citations

17.

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

18.

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 →

19.

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

20.

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

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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