Mary Saltz

848 total citations
18 papers, 326 citations indexed

About

Mary Saltz is a scholar working on Artificial Intelligence, Public Health, Environmental and Occupational Health and Epidemiology. According to data from OpenAlex, Mary Saltz has authored 18 papers receiving a total of 326 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 7 papers in Public Health, Environmental and Occupational Health and 5 papers in Epidemiology. Recurrent topics in Mary Saltz's work include Opioid Use Disorder Treatment (7 papers), Machine Learning in Healthcare (6 papers) and Substance Abuse Treatment and Outcomes (4 papers). Mary Saltz is often cited by papers focused on Opioid Use Disorder Treatment (7 papers), Machine Learning in Healthcare (6 papers) and Substance Abuse Treatment and Outcomes (4 papers). Mary Saltz collaborates with scholars based in United States. Mary Saltz's co-authors include Joel Saltz, Fusheng Wang, Sina Rashidian, Richard N. Rosenthal, Janos Hajagos, Wei Hou, Yu Wang, Jonas S. Almeida, Jianyuan Deng and Tahsin Kurç and has published in prestigious journals such as Cancer Research, Scientific Reports and American Journal of Preventive Medicine.

In The Last Decade

Mary Saltz

18 papers receiving 319 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mary Saltz United States	11	111	103	83	55	31	18	326
Shengpu Tang United States	6	83 0.7×	41 0.4×	60 0.7×	33 0.6×	31 1.0×	10	230
Xuan Song China	8	47 0.4×	54 0.5×	54 0.7×	53 1.0×	9 0.3×	30	376
Anthony Lin United States	7	99 0.9×	67 0.7×	93 1.1×	43 0.8×	25 0.8×	16	433
Jeremy C. Weiss United States	10	137 1.2×	165 1.6×	125 1.5×	22 0.4×	9 0.3×	28	465
Nancy Gentry United States	3	103 0.9×	72 0.7×	67 0.8×	48 0.9×	13 0.4×	4	421
Marshall Nichols United States	14	201 1.8×	94 0.9×	186 2.2×	69 1.3×	28 0.9×	24	715
Brihat Sharma United States	12	103 0.9×	120 1.2×	83 1.0×	16 0.3×	15 0.5×	21	313
Qinyu Zhao China	13	50 0.5×	26 0.3×	141 1.7×	42 0.8×	13 0.4×	28	582
Shelley A. Rusincovitch United States	9	104 0.9×	73 0.7×	95 1.1×	17 0.3×	16 0.5×	14	451
Jennifer H. Garvin United States	14	177 1.6×	74 0.7×	71 0.9×	26 0.5×	8 0.3×	58	592

Countries citing papers authored by Mary Saltz

Since Specialization

Citations

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

Fields of papers citing papers by Mary Saltz

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mary Saltz

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

All Works

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18 of 18 papers shown

Suver, Christine, Jeremy Harper, Johanna Loomba, et al.. (2023). The N3C governance ecosystem: A model socio-technical partnership for the future of collaborative analytics at scale. Journal of Clinical and Translational Science. 7(1). e252–e252. 3 indexed citations

Deng, Jianyuan, Wei Hou, Sina Rashidian, et al.. (2021). Predicting opioid overdose risk of patients with opioid prescriptions using electronic health records based on temporal deep learning. Journal of Biomedical Informatics. 116. 103725–103725. 29 indexed citations

Deng, Jianyuan, Wei Hou, Janos Hajagos, et al.. (2021). A Large-Scale Observational Study on the Temporal Trends and Risk Factors of Opioid Overdose: Real-World Evidence for Better Opioids. Drugs - Real World Outcomes. 8(3). 393–406. 5 indexed citations

Deng, Jianyuan, Sina Rashidian, Wei Hou, et al.. (2021). Identifying risk of opioid use disorder for patients taking opioid medications with deep learning. Journal of the American Medical Informatics Association. 28(8). 1683–1693. 27 indexed citations

Chen, Xin, Wei Hou, Sina Rashidian, et al.. (2021). A large-scale retrospective study of opioid poisoning in New York State with implications for targeted interventions. Scientific Reports. 11(1). 5152–5152. 2 indexed citations

Rashidian, Sina, Janos Hajagos, Richard A. Moffitt, et al.. (2020). Detecting Miscoded Diabetes Diagnosis Codes in Electronic Health Records for Quality Improvement: Temporal Deep Learning Approach. JMIR Medical Informatics. 8(12). e22649–e22649. 4 indexed citations

Moffitt, Richard A., Minh Hoai, Janos Hajagos, et al.. (2020). Association of Proteinuria and Hematuria with Acute Kidney Injury and Mortality in Hospitalized Patients with COVID-19. Kidney & Blood Pressure Research. 45(6). 1018–1032. 39 indexed citations

Deng, Jianyuan, Wei Hou, Sina Rashidian, et al.. (2020). Predicting Opioid Overdose Risk of Patients with Opioid Prescriptions Using Electronic Health Records Based on Temporal Deep Learning. SSRN Electronic Journal. 1 indexed citations

Schoenfeld, Elinor, George S. Leibowitz, Yu Wang, et al.. (2019). Geographic, Temporal, and Sociodemographic Differences in Opioid Poisoning. American Journal of Preventive Medicine. 57(2). 153–164. 17 indexed citations

10.

Rashidian, Sina, Janos Hajagos, Richard A. Moffitt, et al.. (2019). Deep Learning on Electronic Health Records to Improve Disease Coding Accuracy.. PubMed. 2019. 620–629. 16 indexed citations

11.

Rashidian, Sina, Yu Wang, Janos Hajagos, et al.. (2019). Machine Learning Based Opioid Overdose Prediction Using Electronic Health Records.. PubMed. 2019. 389–398. 36 indexed citations

12.

Almeida, Jonas S., Janos Hajagos, Joel Saltz, & Mary Saltz. (2019). Serverless OpenHealth at data commons scale—traversing the 20 million patient records of New York’s SPARCS dataset in real-time. PeerJ. 7. e6230–e6230. 5 indexed citations

13.

Srivastava, Avi, et al.. (2018). Social Media Based Analysis of Opioid Epidemic Using Reddit.. PubMed. 2018. 867–876. 43 indexed citations

14.

Saltz, Joel, Ashish Sharma, Jonas S. Almeida, et al.. (2017). A Containerized Software System for Generation, Management, and Exploration of Features from Whole Slide Tissue Images. Cancer Research. 77(21). e79–e82. 33 indexed citations

15.

Chen, Xin, Yu Wang, Xiaxia Yu, et al.. (2017). Large-scale Analysis of Opioid Poisoning Related Hospital Visits in New York State.. PubMed. 2017. 545–554. 11 indexed citations

16.

Chen, Xin, Yu Wang, Elinor Schoenfeld, et al.. (2017). Spatio-temporal Analysis for New York State SPARCS Data.. PubMed. 2017. 483–492. 16 indexed citations

17.

Saltz, Joel, Jonas S. Almeida, Yi Gao, et al.. (2017). Towards Generation, Management, and Exploration of Combined Radiomics and Pathomics Datasets for Cancer Research.. PubMed. 2017. 85–94. 36 indexed citations

18.

Almeida, Jonas S., et al.. (2017). OpenHealth Platform for Interactive Contextualization of Population Health Open Data.. PubMed. 2015. 297–305. 3 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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