David E. Liston

9 papers receiving 1.4k citations

David E. Liston's Hit Papers

Explainable machine-learning predictions for the prevention of hypoxaemia during surgery 2018 · 1.3k citations
1.3k0+2+5Years since publication4008001.2k

Peers

David E. Liston
Comparison fields: 5 of 171
  • Health Informatics 117
  • Health Information Management 62
  • Artificial Intelligence 287
  • Environmental Engineering 68
  • Cardiology and Cardiovascular Medicine 92
Replace Michael J. Eisses with:
Michael J. Eisses United States
Shu-Fang Newman United States
Mayumi Horibe United States
Hong Kuan Kok Australia
Alex Bui United States
Guilan Kong China
Evangelia Christodoulou Germany
Yasunobu Nohara Japan
Siru Liu China
Thomas A. Lasko United States
David E. Liston relative to Michael J. Eisses United States Michael J. Eisses's profile →
Citations per field
00.5×1.5×
Michael J. Eisses · 1×
Citations per year

Countries citing papers authored by David E. Liston

Since Specialization
Citations

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

Fields of papers citing papers by David E. Liston

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David E. Liston. 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 David E. Liston. The network helps show where David E. Liston may publish in the future.

Co-authors

The 20 scholars most cited alongside David E. Liston, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with David E. Liston Line = papers co-authored together David E. Liston links everyone, so they are left out of the graph.

All Works

9 of 9 papers shown
#Work
1
Explainable machine-learning predictions for the prevention of hypoxaemia during surgery
Hit paper breakdown →
20181320
2 202038
3 202011
4 202011
5 20227
6 20145
7 20132
8 20142
9 20141

About David E. Liston

David E. Liston is a scholar working on Cardiology and Cardiovascular Medicine, Public Health, Environmental and Occupational Health, Developmental Neuroscience, Pulmonary and Respiratory Medicine and Health Informatics, having authored 9 papers that have together received 1.4k indexed citations. Recurring topics across this work include Cardiac, Anesthesia and Surgical Outcomes (4 papers), Health and Medical Research Impacts (3 papers), Anesthesia and Neurotoxicity Research (3 papers), Artificial Intelligence in Healthcare and Education (1 paper), Tracheal and airway disorders (1 paper), Respiratory Support and Mechanisms (1 paper), Mechanical Circulatory Support Devices (1 paper) and Machine Learning in Healthcare (1 paper). The work is most often cited by research in Health Informatics (117 citations), Health Information Management (62 citations), Artificial Intelligence (287 citations), Environmental Engineering (68 citations) and Cardiology and Cardiovascular Medicine (92 citations). David E. Liston has collaborated with scholars based in United States, Spain and United Kingdom. Frequent co-authors include Su‐In Lee, Jerry W. Kim, Shu-Fang Newman, Scott Lundberg, Monica S. Vavilala, Mayumi Horibe, Bala G. Nair, Michael J. Eisses, Trevor Adams and Daniel K. Low. Their work appears in journals such as Pediatric Anesthesia, Nature Biomedical Engineering, Journal of Cardiothoracic and Vascular Anesthesia, Journal of Pediatric Urology and Anesthesia & Analgesia.

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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