Mary Saltz
- Artificial Intelligence top 10%
- Public Health, Environmental and Occupational Health
- Epidemiology
- Radiology, Nuclear Medicine and Imaging
- Infectious Diseases
- Co-authors
- Joel SaltzFusheng WangSina RashidianJanos HajagosRichard N. RosenthalWei HouYu WangJonas S. Almeida
- Topics
- Opioid Use Disorder Treatment (7 papers)Machine Learning in Healthcare (6 papers)Substance Abuse Treatment and Outcomes (4 papers)
- Partner nations
- United States
In The Last Decade
Mary Saltz
18 papers receiving 319 citations
Peers
Comparison fields: 5 of 82
- Artificial Intelligence 111
- Public Health, Environmental and Occupational Health 103
- Epidemiology 83
- Radiology, Nuclear Medicine and Imaging 55
- Infectious Diseases 31
Countries citing papers authored by Mary Saltz
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
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
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 29 | |
| 3 | 5 | |
| 4 | 2 | |
| 5 | 27 | |
| 6 | 4 | |
| 7 | 39 | |
| 8 | 1 | |
| 9 | 17 | |
| 10 | Deep Learning on Electronic Health Records to Improve Disease Coding Accuracy. | 16 |
| 11 | 5 | |
| 12 | Machine Learning Based Opioid Overdose Prediction Using Electronic Health Records. | 36 |
| 13 | Social Media Based Analysis of Opioid Epidemic Using Reddit. | 43 |
| 14 | 33 | |
| 15 | Spatio-temporal Analysis for New York State SPARCS Data. | 16 |
| 16 | Large-scale Analysis of Opioid Poisoning Related Hospital Visits in New York State. | 11 |
| 17 | Towards Generation, Management, and Exploration of Combined Radiomics and Pathomics Datasets for Cancer Research. | 36 |
| 18 | OpenHealth Platform for Interactive Contextualization of Population Health Open Data. | 3 |
About Mary Saltz
Mary Saltz is a scholar working on Health Information Management, Information Systems and Management and Artificial Intelligence, having authored 18 papers that have together received 326 indexed citations. Recurring topics across this work include Opioid Use Disorder Treatment (7 papers), Machine Learning in Healthcare (6 papers) and Substance Abuse Treatment and Outcomes (4 papers). The work is most often cited by research in Health Informatics (24 citations), Toxicology (19 citations) and Health Information Management (22 citations). Mary Saltz has collaborated with scholars based in United States. Frequent co-authors include Joel Saltz, Fusheng Wang, Sina Rashidian, Janos Hajagos, Richard N. Rosenthal, Wei Hou, Yu Wang, Jonas S. Almeida, Jianyuan Deng and Yi Gao. Their work appears in journals such as Cancer Research, Scientific Reports and American Journal of Preventive Medicine.
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.