Laura Moss

729 total citations
40 papers, 382 citations indexed

About

Laura Moss is a scholar working on Artificial Intelligence, Surgery and Neurology. According to data from OpenAlex, Laura Moss has authored 40 papers receiving a total of 382 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 10 papers in Surgery and 10 papers in Neurology. Recurrent topics in Laura Moss's work include Traumatic Brain Injury and Neurovascular Disturbances (10 papers), Semantic Web and Ontologies (8 papers) and Machine Learning in Healthcare (7 papers). Laura Moss is often cited by papers focused on Traumatic Brain Injury and Neurovascular Disturbances (10 papers), Semantic Web and Ontologies (8 papers) and Machine Learning in Healthcare (7 papers). Laura Moss collaborates with scholars based in United Kingdom, Sweden and Germany. Laura Moss's co-authors include Ian Piper, Greet Van den Berghe, Bart Feyen, Giuseppe Citerio, Rob Donald, John Kinsella, Per Enblad, Iain Chambers, Geert Meyfroidt and Bart Depreitere and has published in prestigious journals such as SHILAP Revista de lepidopterología, Critical Care Medicine and Intensive Care Medicine.

In The Last Decade

Laura Moss

35 papers receiving 372 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Laura Moss United Kingdom 9 250 111 90 55 54 40 382
Rob Donald United Kingdom 9 293 1.2× 156 1.4× 103 1.1× 58 1.1× 33 0.6× 19 370
Ajay Hegde India 12 176 0.7× 127 1.1× 19 0.2× 52 0.9× 34 0.6× 55 375
Ivan Janciak Austria 17 488 2.0× 204 1.8× 407 4.5× 17 0.3× 31 0.6× 37 738
James JM Loan United Kingdom 9 179 0.7× 112 1.0× 30 0.3× 8 0.1× 10 0.2× 22 296
Cheng Jiang China 10 36 0.1× 22 0.2× 74 0.8× 18 0.3× 11 0.2× 17 248
Hirofumi Obinata Japan 8 93 0.4× 41 0.4× 13 0.1× 125 2.3× 142 2.6× 17 495
Guang Zhang China 11 40 0.2× 51 0.5× 22 0.2× 66 1.2× 31 0.6× 43 265
Osamu Takaki Japan 6 53 0.2× 32 0.3× 32 0.4× 40 0.7× 51 0.9× 19 310
Gino Mongelluzzo United States 6 148 0.6× 116 1.0× 16 0.2× 124 2.3× 85 1.6× 15 358
J. J. Ross United Kingdom 11 43 0.2× 26 0.2× 7 0.1× 15 0.3× 29 0.5× 41 393

Countries citing papers authored by Laura Moss

Since Specialization
Citations

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

Fields of papers citing papers by Laura Moss

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Laura Moss

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

All Works

20 of 20 papers shown
1.
Hawthorne, Christopher, Laura Moss, Ian Piper, et al.. (2024). International e-Delphi survey to define best practice in the reporting of intracranial pressure monitoring recording data. SHILAP Revista de lepidopterología. 4. 102860–102860.
2.
Moss, Laura, Martin Shaw, Ian Piper, & Christopher Hawthorne. (2024). From bed to bench and back again: Challenges facing deployment of intracranial pressure data analysis in clinical environments. SHILAP Revista de lepidopterología. 4. 102858–102858. 1 indexed citations
3.
Moss, Laura, et al.. (2024). Feasibility of forecasting future critical care bed availability using bed management data. BMJ Health & Care Informatics. 31(1). e101096–e101096. 1 indexed citations
4.
Piper, Ian, Barbara Gregson, Per Enblad, et al.. (2023). Decompressive craniectomy as a second/third-tier intervention in traumatic brain injury: A multicenter observational study. Injury. 54(9). 110911–110911.
5.
Moss, Laura, David Corsar, Martin Shaw, Ian Piper, & Christopher Hawthorne. (2022). Demystifying the Black Box: The Importance of Interpretability of Predictive Models in Neurocritical Care. Neurocritical Care. 37(S2). 185–191. 14 indexed citations
6.
Shaw, Martin, Laura Moss, Christopher Hawthorne, John Kinsella, & Ian Piper. (2018). Investigation of the Relationship Between the Burden of Raised ICP and the Length of Stay in a Neuro-Intensive Care Unit. Acta neurochirurgica. Supplementum. 126. 205–208. 3 indexed citations
7.
Depreitere, Bart, Fabián Güiza, Ian Piper, et al.. (2018). Cerebral Perfusion Pressure Variability Between Patients and Between Centres. Acta neurochirurgica. Supplementum. 126. 3–6. 1 indexed citations
8.
Flechet, Marine, Geert Meyfroidt, Ian Piper, et al.. (2018). Visualizing Cerebrovascular Autoregulation Insults and Their Association with Outcome in Adult and Paediatric Traumatic Brain Injury. Acta neurochirurgica. Supplementum. 126. 291–295. 20 indexed citations
9.
Moss, Laura, Martin Shaw, Ian Piper, Christopher Hawthorne, & John Kinsella. (2016). Apache Spark for the Analysis of High Frequency Neurointensive Care Unit Data: Preliminary Comparison of Scala vs. R. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam). 1 indexed citations
10.
Moss, Laura, Martin Shaw, Ian Piper, D. K. Arvind, & Christopher Hawthorne. (2016). Automatic Calculation of Hydrostatic Pressure Gradient in Patients with Head Injury: A Pilot Study. Acta neurochirurgica. Supplementum. 122. 263–266. 1 indexed citations
11.
Kinsella, John, et al.. (2016). 1578: PUBLIC PERCEPTION OF THE USE OF CRITICAL CARE PATIENT DATA BEYOND TREATMENT: A PILOT STUDY. Critical Care Medicine. 44(12). 470–470. 2 indexed citations
12.
Güiza, Fabián, Bart Depreitere, Ian Piper, et al.. (2015). Visualizing the pressure and time burden of intracranial hypertension in adult and paediatric traumatic brain injury. Intensive Care Medicine. 41(6). 1067–1076. 178 indexed citations
13.
Bonner, Stephen, A. Stephen McGough, John Brennan, et al.. (2015). Data quality assessment and anomaly detection via map/reduce and linked data: A case study in the medical domain. Durham Research Online (Durham University). 737–746. 9 indexed citations
14.
Sleeman, Derek, Laura Moss, & John Kinsella. (2014). Temporal Discovery Workbench: a Case Study with ICU Patient Datasets. Electronic workshops in computing.
15.
Grando, Adela, Laura Moss, Derek Sleeman, & John Kinsella. (2013). Argumentation-logic for creating and explaining medical hypotheses. Artificial Intelligence in Medicine. 58(1). 1–13. 15 indexed citations
16.
Sleeman, Derek, et al.. (2012). INSIGHT: Helping domain experts make their knowledge more consistent. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam). 1 indexed citations
17.
Sleeman, Derek, et al.. (2012). Detecting and resolving inconsistencies between domain experts’ different perspectives on (classification) tasks. Artificial Intelligence in Medicine. 55(2). 71–86. 4 indexed citations
18.
19.
Sim, Malcolm A. B., et al.. (2009). Confusion matrices to refine a novel scoring system for cardiovascular instability in intensive care. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam). 1 indexed citations
20.
Moss, Laura, Derek Sleeman, Malcolm Sim, et al.. (2009). Ontology-driven hypothesis generation to explain anomalous patient responses to treatment. Knowledge-Based Systems. 23(4). 309–315. 11 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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