Scott McLachlan

1.3k total citations
30 papers, 583 citations indexed

About

Scott McLachlan is a scholar working on Health Information Management, General Health Professions and Public Health, Environmental and Occupational Health. According to data from OpenAlex, Scott McLachlan has authored 30 papers receiving a total of 583 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Health Information Management, 8 papers in General Health Professions and 8 papers in Public Health, Environmental and Occupational Health. Recurrent topics in Scott McLachlan's work include Electronic Health Records Systems (9 papers), Machine Learning in Healthcare (5 papers) and Clinical practice guidelines implementation (5 papers). Scott McLachlan is often cited by papers focused on Electronic Health Records Systems (9 papers), Machine Learning in Healthcare (5 papers) and Clinical practice guidelines implementation (5 papers). Scott McLachlan collaborates with scholars based in United Kingdom, New Zealand and United States. Scott McLachlan's co-authors include Kudakwashe Dube, Norman Fenton, Thomas Gallagher, Martin Neil, Jason Walonoski, M Krámer, Joseph C. Nichols, Dylan Hall, Andre Quina and Magda Osman and has published in prestigious journals such as Trends in Cognitive Sciences, Journal of the American Medical Informatics Association and BMJ Open.

In The Last Decade

Scott McLachlan

30 papers receiving 560 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Scott McLachlan United Kingdom 12 217 105 95 67 44 30 583
Kudakwashe Dube New Zealand 9 206 0.9× 115 1.1× 63 0.7× 54 0.8× 53 1.2× 33 479
Samina Abidi Canada 15 175 0.8× 106 1.0× 78 0.8× 88 1.3× 117 2.7× 60 544
Sanjeev P. Bhavnani United States 15 186 0.9× 66 0.6× 158 1.7× 122 1.8× 28 0.6× 53 985
Dimitrios G. Katehakis Greece 13 112 0.5× 168 1.6× 157 1.7× 60 0.9× 32 0.7× 60 648
Pekka Ruotsalainen Finland 12 104 0.5× 93 0.9× 81 0.9× 92 1.4× 61 1.4× 62 460
Thomas Gallagher United States 6 170 0.8× 82 0.8× 47 0.5× 31 0.5× 39 0.9× 14 332
Arash Shaban‐Nejad United States 15 255 1.2× 58 0.6× 137 1.4× 70 1.0× 88 2.0× 91 859
Johan Gustav Bellika Norway 16 148 0.7× 146 1.4× 257 2.7× 166 2.5× 58 1.3× 70 856
Curtis L. Cole United States 11 77 0.4× 148 1.4× 143 1.5× 137 2.0× 40 0.9× 27 504
Enea Parimbelli Italy 12 140 0.6× 89 0.8× 85 0.9× 88 1.3× 62 1.4× 47 485

Countries citing papers authored by Scott McLachlan

Since Specialization
Citations

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

Fields of papers citing papers by Scott McLachlan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Scott McLachlan

This figure shows the co-authorship network connecting the top 25 collaborators of Scott McLachlan. A scholar is included among the top collaborators of Scott McLachlan 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 Scott McLachlan. Scott McLachlan 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.
McLachlan, Scott, Evangelia Kyrimi, Kudakwashe Dube, et al.. (2024). Approach and Method for Bayesian Network Modelling: The Case for Pregnancy Outcomes in England and Wales. 604–612. 1 indexed citations
2.
McLachlan, Scott, Evangelia Kyrimi, Kudakwashe Dube, Norman Fenton, & Lisa Webley. (2022). Lawmaps: enabling legal AI development through visualisation of the implicit structure of legislation and lawyerly process. Artificial Intelligence and Law. 31(1). 169–194. 3 indexed citations
3.
Huda, M. S. B., et al.. (2021). mHealth apps for gestational diabetes mellitus that provide clinical decision support or artificial intelligence: A scoping review. Diabetic Medicine. 39(1). e14735–e14735. 28 indexed citations
4.
Kyrimi, Evangelia, et al.. (2021). Bayesian networks in healthcare: What is preventing their adoption?. Artificial Intelligence in Medicine. 116. 102079–102079. 28 indexed citations
5.
6.
Kyrimi, Evangelia, et al.. (2020). Medical idioms for clinical Bayesian network development. Journal of Biomedical Informatics. 108. 103495–103495. 22 indexed citations
7.
Neil, Martin, Norman Fenton, Magda Osman, & Scott McLachlan. (2020). Bayesian network analysis of Covid-19 data reveals higher infection prevalence rates and lower fatality rates than widely reported. Journal of Risk Research. 23(7-8). 866–879. 18 indexed citations
8.
McLachlan, Scott, Evangelia Kyrimi, Kudakwashe Dube, et al.. (2020). Incorporating Clinical Decisions into Standardised Caremaps. 123. 1–2. 1 indexed citations
9.
Osman, Magda, Scott McLachlan, Norman Fenton, et al.. (2020). Learning from Behavioural Changes That Fail. Trends in Cognitive Sciences. 24(12). 969–980. 43 indexed citations
10.
Kyrimi, Evangelia, et al.. (2020). Causal Bayesian Networks for Medical Diagnosis: A Case Study in Rheumatoid Arthritis. Research Explorer (The University of Manchester). 1–7. 4 indexed citations
11.
Fenton, Norman, Martin Neil, Magda Osman, & Scott McLachlan. (2020). COVID-19 infection and death rates: the need to incorporate causal explanations for the data and avoid bias in testing. Journal of Risk Research. 23(7-8). 862–865. 29 indexed citations
12.
Fenton, Norman, Magda Osman, Scott McLachlan, & Martin Neil. (2020). Coronavirus: country comparisons are pointless unless we account for these biases in testing. 1 indexed citations
13.
14.
McLachlan, Scott, Evangelia Kyrimi, Kudakwashe Dube, & Norman Fenton. (2019). Clinical Caremap Development: How Can Caremaps Standardise Care When They Are Not Standardised?. 123–134. 7 indexed citations
15.
Gallagher, Thomas, Kudakwashe Dube, & Scott McLachlan. (2018). Ethical Issues in Secondary Use of Personal Health Information. 3(3). 1–5. 2 indexed citations
16.
McLachlan, Scott, Kudakwashe Dube, Owen Johnson, et al.. (2018). Learning health systems: the research community awareness challenge. BMJ Health & Care Informatics. 25(1). 38–40. 4 indexed citations
17.
McLachlan, Scott, Andrew Harvey, & Jamie E. Newman. (2017). Delivering an integrated system of care in Western New South Wales, Australia. International Journal of Integrated Care. 17(3). 33–33. 1 indexed citations
18.
Walonoski, Jason, M Krámer, Joseph C. Nichols, et al.. (2017). Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record. Journal of the American Medical Informatics Association. 25(3). 230–238. 213 indexed citations
19.
McLachlan, Scott, Kudakwashe Dube, & Thomas Gallagher. (2016). Using the CareMap with Health Incidents Statistics for Generating the Realistic Synthetic Electronic Healthcare Record. 439–448. 28 indexed citations
20.
McElwaine, Kathleen, Megan Freund, Elizabeth Campbell, et al.. (2013). The delivery of preventive care to clients of community health services. BMC Health Services Research. 13(1). 167–167. 25 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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