Thusitha Mabotuwana

523 total citations
35 papers, 357 citations indexed

About

Thusitha Mabotuwana is a scholar working on Radiology, Nuclear Medicine and Imaging, Cardiology and Cardiovascular Medicine and General Health Professions. According to data from OpenAlex, Thusitha Mabotuwana has authored 35 papers receiving a total of 357 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Radiology, Nuclear Medicine and Imaging, 9 papers in Cardiology and Cardiovascular Medicine and 9 papers in General Health Professions. Recurrent topics in Thusitha Mabotuwana's work include Radiology practices and education (9 papers), Medication Adherence and Compliance (8 papers) and Blood Pressure and Hypertension Studies (8 papers). Thusitha Mabotuwana is often cited by papers focused on Radiology practices and education (9 papers), Medication Adherence and Compliance (8 papers) and Blood Pressure and Hypertension Studies (8 papers). Thusitha Mabotuwana collaborates with scholars based in New Zealand, United States and Finland. Thusitha Mabotuwana's co-authors include Jim Warren, Christopher S. Hall, Martin L. Gunn, Michael Lee, Timothy Kenealy, Sandeep Dalal, Jeff Harrison, Christoph Wald, Joel S. Tieder and Paul J. Chang and has published in prestigious journals such as American Journal of Roentgenology, International Journal of Medical Informatics and Journal of Biomedical Informatics.

In The Last Decade

Thusitha Mabotuwana

34 papers receiving 348 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Thusitha Mabotuwana New Zealand 10 145 68 60 60 50 35 357
Marianne Johansson Jørgensen Denmark 9 44 0.3× 251 3.7× 68 1.1× 35 0.6× 21 0.4× 15 492
Rosy Tsopra France 13 25 0.2× 71 1.0× 83 1.4× 18 0.3× 43 0.9× 42 401
Stephan A. Gaehde United States 10 29 0.2× 86 1.3× 51 0.8× 24 0.4× 19 0.4× 11 452
Shaun T Alfreds United States 8 43 0.3× 134 2.0× 60 1.0× 9 0.1× 16 0.3× 12 455
Donald Levick United States 8 23 0.2× 50 0.7× 113 1.9× 66 1.1× 46 0.9× 17 442
Morgan Simons United States 6 96 0.7× 155 2.3× 19 0.3× 19 0.3× 16 0.3× 6 353
Suzanne V. Blackley United States 11 45 0.3× 76 1.1× 41 0.7× 10 0.2× 28 0.6× 25 351
Esteban Gershanik United States 9 92 0.6× 25 0.4× 81 1.4× 20 0.3× 16 0.3× 20 305
Gina Barnes United States 13 109 0.8× 129 1.9× 20 0.3× 8 0.1× 17 0.3× 31 486
Jeremy A. Balch United States 13 42 0.3× 97 1.4× 28 0.5× 11 0.2× 14 0.3× 45 455

Countries citing papers authored by Thusitha Mabotuwana

Since Specialization
Citations

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

Fields of papers citing papers by Thusitha Mabotuwana

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Thusitha Mabotuwana

This figure shows the co-authorship network connecting the top 25 collaborators of Thusitha Mabotuwana. A scholar is included among the top collaborators of Thusitha Mabotuwana 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 Thusitha Mabotuwana. Thusitha Mabotuwana 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.
Mabotuwana, Thusitha, et al.. (2022). Impact of Follow-Up Imaging Recommendation Specificity on Adherence. Studies in health technology and informatics. 295. 87–90. 4 indexed citations
2.
Raghavan, Usha Nandini, et al.. (2019). Probabilistic Modeling of Exam Durations in Radiology Procedures. Journal of Digital Imaging. 32(3). 386–395. 1 indexed citations
3.
Dalal, Sandeep, Wei‐Hung Weng, Thusitha Mabotuwana, et al.. (2019). Determining Follow-Up Imaging Study Using Radiology Reports. Journal of Digital Imaging. 33(1). 121–130. 15 indexed citations
4.
Mabotuwana, Thusitha, et al.. (2018). Determining Adherence to Follow-up Imaging Recommendations. Journal of the American College of Radiology. 15(3). 422–428. 46 indexed citations
5.
Mabotuwana, Thusitha, et al.. (2018). Detecting Technical Image Quality in Radiology Reports.. PubMed. 2018. 780–788. 3 indexed citations
6.
Mabotuwana, Thusitha, Christopher S. Hall, Sandeep Dalal, Joel S. Tieder, & Martin L. Gunn. (2017). Extracting Follow-Up Recommendations and Associated Anatomy from Radiology Reports.. PubMed. 245. 1090–1094. 6 indexed citations
7.
Mabotuwana, Thusitha, Christopher S. Hall, Sebastian Flacke, Shiby Thomas, & Christoph Wald. (2017). Inpatient Complexity in Radiology—a Practical Application of the Case Mix Index Metric. Journal of Digital Imaging. 30(3). 301–308. 12 indexed citations
8.
Mabotuwana, Thusitha, et al.. (2015). An HL7 Data Pseudonymization Pipeline. 17. 303–309. 1 indexed citations
9.
Mabotuwana, Thusitha, et al.. (2014). Mapping Institution-Specific Study Descriptions to RadLex Playbook Entries. Journal of Digital Imaging. 27(3). 321–330. 14 indexed citations
10.
Mabotuwana, Thusitha, et al.. (2013). An ontology-based similarity measure for biomedical data – Application to radiology reports. Journal of Biomedical Informatics. 46(5). 857–868. 39 indexed citations
11.
Warren, Jim, et al.. (2012). Using the general practice EMR for improving blood pressure medication adherence. Studies in health technology and informatics. 178. 228–34. 7 indexed citations
12.
Mabotuwana, Thusitha, Jim Warren, Martin Orr, Timothy Kenealy, & Jeff Harrison. (2011). Using primary care prescribing data to improve GP awareness ofantidepressant adherence issues. Journal of Innovation in Health Informatics. 19(1). 7–15. 7 indexed citations
13.
Elley, C. Raina, et al.. (2010). Perspectives on adherence to blood pressure–lowering medications among Samoan patients: qualitative interviews. Journal of Primary Health Care. 2(3). 217–224. 8 indexed citations
14.
Mabotuwana, Thusitha, et al.. (2010). Quality indicators to measure blood pressure management over a timeinterval. Journal of Innovation in Health Informatics. 18(3). 149–156.
15.
Mabotuwana, Thusitha & Jim Warren. (2009). An ontology-based approach to enhance querying capabilities of general practice medicine for better management of hypertension. Artificial Intelligence in Medicine. 47(2). 87–103. 30 indexed citations
16.
Mabotuwana, Thusitha, Jim Warren, Jeff Harrison, & Timothy Kenealy. (2009). What can primary care prescribing data tell us about individual adherence to long‐term medication?—comparison to pharmacy dispensing data. Pharmacoepidemiology and Drug Safety. 18(10). 956–964. 40 indexed citations
17.
Mabotuwana, Thusitha & Jim Warren. (2009). ChronoMedIt – A computational quality audit framework for better management of patients with chronic conditions. Journal of Biomedical Informatics. 43(1). 144–158. 9 indexed citations
18.
Mabotuwana, Thusitha, et al.. (2009). A computational framework to identify patients with poor adherence to blood pressure lowering medication. International Journal of Medical Informatics. 78(11). 745–756. 17 indexed citations
19.
Warren, Jim, et al.. (2008). Utilising practice management system data for quality improvement in use of blood pressure lowering medications in general practice.. PubMed. 121(1285). 53–62. 7 indexed citations
20.
Mabotuwana, Thusitha & Jim Warren. (2008). A Semantic Web Technology Based Approach to Identify Hypertensive Patients for Follow-Up/Recall. 117. 318–323. 4 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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