Željko Kraljević

1.0k total citations
20 papers, 379 citations indexed

About

Željko Kraljević is a scholar working on Artificial Intelligence, Infectious Diseases and Molecular Biology. According to data from OpenAlex, Željko Kraljević has authored 20 papers receiving a total of 379 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 5 papers in Infectious Diseases and 5 papers in Molecular Biology. Recurrent topics in Željko Kraljević's work include Machine Learning in Healthcare (8 papers), Topic Modeling (7 papers) and Biomedical Text Mining and Ontologies (5 papers). Željko Kraljević is often cited by papers focused on Machine Learning in Healthcare (8 papers), Topic Modeling (7 papers) and Biomedical Text Mining and Ontologies (5 papers). Željko Kraljević collaborates with scholars based in United Kingdom, United States and Germany. Željko Kraljević's co-authors include James Teo, Richard Dobson, Joshua Au Yeung, Daniel Bean, Anthony Shek, Rebecca Bendayan, Thomas Searle, Ajay M. Shah, Rosita Zakeri and Łukasz Roguski and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Epilepsia.

In The Last Decade

Željko Kraljević

18 papers receiving 374 citations

Peers

Željko Kraljević

NEURO

Anthony Shek United Kingdom

Anthony Lin United States

Syed Muhammad Ismail Shah Pakistan

Evan Sholle United States

Patrick Botting United States

Thomas F. Osborne United States

Jung Hyun Chang South Korea

Eduardo Pontes Reis Brazil

Mary Saltz United States

Shawheen J. Rezaei United States

Anthony Shek United Kingdom

Željko Kraljević

154 ×1.1

27 ×0.3

49 ×0.5

80 ×1.1

NEURO

40 ×1.0

Citations per year, relative to Željko Kraljević Željko Kraljević (= 1×) peers Anthony Shek

Countries citing papers authored by Željko Kraljević

Since Specialization

Citations

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

Fields of papers citing papers by Željko Kraljević

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Željko Kraljević

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

All Works

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20 of 20 papers shown

Laiou, Petroula, Željko Kraljević, Antonio Valentı́n, et al.. (2025). Seizure forecasting by tracking cortical response to electrical stimulation. Epilepsia. 66(10). 3937–3947.

Ross, Jack, Yogini Jani, Enrico Costanza, et al.. (2025). Design and implementation of a natural language processing system at the point of care: MiADE (medical information AI data extractor). BMC Medical Informatics and Decision Making. 25(1). 365–365.

Kraljević, Željko, Daniel Bean, Anthony Shek, et al.. (2024). Foresight—a generative pretrained transformer for modelling of patient timelines using electronic health records: a retrospective modelling study. The Lancet Digital Health. 6(4). e281–e290. 43 indexed citations

Yeung, Joshua Au, Anthony Shek, Thomas Searle, et al.. (2024). Natural language processing data services for healthcare providers. BMC Medical Informatics and Decision Making. 24(1). 356–356. 7 indexed citations

Postiglione, Marco, Daniel Bean, Željko Kraljević, Richard Dobson, & Vincenzo Moscato. (2024). Predicting Future Disorders via Temporal Knowledge Graphs and Medical Ontologies. IEEE Journal of Biomedical and Health Informatics. 28(7). 4238–4248. 2 indexed citations

Roguski, Łukasz, et al.. (2024). Challenges and Opportunities of Using Transformer-Based Multi-Task Learning in NLP Through ML Lifecycle: A Position Paper. 7. 100076–100076. 5 indexed citations

Yeung, Joshua Au, et al.. (2023). AI chatbots not yet ready for clinical use. Frontiers in Digital Health. 5. 1161098–1161098. 99 indexed citations

Bean, Daniel, Željko Kraljević, Anthony Shek, James Teo, & Richard Dobson. (2023). Hospital-wide natural language processing summarising the health data of 1 million patients. SHILAP Revista de lepidopterología. 2(5). e0000218–e0000218. 7 indexed citations

Yeung, Joshua Au, et al.. (2023). Artificial intelligence (AI) for neurologists: do digital neurones dream of electric sheep?. Practical Neurology. 23(6). 476–488. 6 indexed citations

10.

Kraljević, Željko, Anthony Shek, Joshua Au Yeung, et al.. (2023). Validating Transformers for Redaction of Text from Electronic Health Records in Real-World Healthcare. 544–549. 2 indexed citations

11.

Bendayan, Rebecca, Željko Kraljević, Jayati Das‐Munshi, et al.. (2022). Mapping multimorbidity in individuals with schizophrenia and bipolar disorders: evidence from the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLAM BRC) case register. BMJ Open. 12(1). e054414–e054414. 14 indexed citations

12.

Farajidavar, Nazli, Kevin O’Gallagher, Daniel Bean, et al.. (2022). Diagnostic signature for heart failure with preserved ejection fraction (HFpEF): a machine learning approach using multi-modality electronic health record data. BMC Cardiovascular Disorders. 22(1). 567–567. 8 indexed citations

13.

Bechman, Katie, Mark Yates, Deepak Nagra, et al.. (2022). Inpatient COVID-19 mortality has reduced over time: Results from an observational cohort. PLoS ONE. 17(1). e0261142–e0261142. 15 indexed citations

14.

Kraljević, Željko, Harold G. Parkes, Victoria Metaxa, et al.. (2021). Natural language word embeddings as a glimpse into healthcare language and associated mortality surrounding end of life. BMJ Health & Care Informatics. 28(1). e100464–e100464. 4 indexed citations

15.

Das‐Munshi, Jayati, Honghan Wu, Željko Kraljević, et al.. (2021). Investigating the association between physical health comorbidities and disability in individuals with severe mental illness. European Psychiatry. 64(1). e77–e77. 9 indexed citations

16.

Vitelli, Valeria, Ewan Carr, Aleksander Rygh Holten, et al.. (2021). Regional performance variation in external validation of four prediction models for severity of COVID-19 at hospital admission: An observational multi-centre cohort study. PLoS ONE. 16(8). e0255748–e0255748. 2 indexed citations

17.

Bechman, Katie, Mark Yates, Deepak Nagra, et al.. (2021). Inpatient COVID-19 Mortality Has Reduced Over Time: Results from an Observational Cohort. SSRN Electronic Journal. 2 indexed citations

18.

Bean, Daniel, Željko Kraljević, Thomas Searle, et al.. (2020). Angiotensin-Converting Enzyme Inhibitors and Angiotensin II Receptor Blockers are Not Associated with Severe COVID-19 Infection in a Multi-Site UK Acute Hospital Trust. European Journal of Heart Failure. 22(6). 967–974. 152 indexed citations

19.

Zakeri, Rosita, Ewan Carr, Daniel Bean, et al.. (2020). Biological Responses to COVID-19 Insights from Physiological and Blood Biomarker Profiles. SSRN Electronic Journal. 1 indexed citations

20.

Kraljević, Željko, et al.. (2013). Integration Of Cloud Services With Invenio Digital Library. Zenodo (CERN European Organization for Nuclear Research). 1 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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