Daniel Stamate

479 total citations
23 papers, 107 citations indexed

About

Daniel Stamate is a scholar working on Artificial Intelligence, Psychiatry and Mental health and Computer Networks and Communications. According to data from OpenAlex, Daniel Stamate has authored 23 papers receiving a total of 107 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 5 papers in Psychiatry and Mental health and 4 papers in Computer Networks and Communications. Recurrent topics in Daniel Stamate's work include Machine Learning in Healthcare (5 papers), Logic, Reasoning, and Knowledge (4 papers) and Dementia and Cognitive Impairment Research (4 papers). Daniel Stamate is often cited by papers focused on Machine Learning in Healthcare (5 papers), Logic, Reasoning, and Knowledge (4 papers) and Dementia and Cognitive Impairment Research (4 papers). Daniel Stamate collaborates with scholars based in United Kingdom, Netherlands and Germany. Daniel Stamate's co-authors include Nicolas Spyratos, Daniel Ståhl, Fionn Murtagh, Robert Zimmer, Robin Murray, Marta Di Forti, Wajdi Alghamdi, Philippe Delespaul, Sinan Gülöksüz and Doina Logofătu and has published in prestigious journals such as PLoS ONE, Schizophrenia Research and Applied Soft Computing.

In The Last Decade

Daniel Stamate

21 papers receiving 104 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel Stamate United Kingdom 7 47 23 15 12 11 23 107
Konstantinos Lazaros Greece 6 48 1.0× 24 1.0× 11 0.7× 2 0.2× 6 0.5× 12 185
Heekyong Park South Korea 6 22 0.5× 5 0.2× 15 1.0× 8 0.7× 26 2.4× 14 125
Chester Holtz United States 8 36 0.8× 3 0.1× 14 0.9× 3 0.3× 9 0.8× 14 131
Imanol Pérez Arribas United Kingdom 6 27 0.6× 14 0.6× 2 0.1× 16 1.3× 1 0.1× 7 124
Rishabh Jain United States 8 25 0.5× 4 0.2× 27 1.8× 4 0.4× 41 169
Minh Nguyen United States 5 105 2.2× 62 2.7× 2 0.1× 6 0.5× 39 3.5× 18 211
Raghavendra Pappagari United States 7 132 2.8× 10 0.4× 3 0.2× 67 5.6× 3 0.3× 10 194
Tomás Barros Chile 6 46 1.0× 2 0.1× 35 2.3× 20 1.7× 18 204
Scott Neu United States 7 22 0.5× 27 1.2× 3 0.2× 1 0.1× 6 0.5× 15 139
Najoung Kim United States 10 289 6.1× 13 0.6× 3 0.2× 10 0.8× 19 354

Countries citing papers authored by Daniel Stamate

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Stamate

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Stamate

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Stamate. A scholar is included among the top collaborators of Daniel Stamate 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 Daniel Stamate. Daniel Stamate 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.
Stamate, Daniel, et al.. (2024). Balancing accuracy and Interpretability: An R package assessing complex relationships beyond the Cox model and applications to clinical prediction. International Journal of Medical Informatics. 194. 105700–105700. 4 indexed citations
2.
Reeves, David, Catharine Morgan, Daniel Stamate, et al.. (2024). Identifying individuals at high risk for dementia in primary care: Development and validation of the DemRisk risk prediction model using routinely collected patient data. PLoS ONE. 19(10). e0310712–e0310712. 1 indexed citations
3.
4.
Breaban, Mihaela, et al.. (2023). Joint Decision Making in Ant Colony Systems for Solving the Multiple Traveling Salesman Problem. Procedia Computer Science. 225. 3498–3507. 1 indexed citations
5.
Stamate, Daniel, Fionn Murtagh, Catharine Morgan, et al.. (2021). Predicting risk of dementia with machine learning and survival models using routine primary care records. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 3036–3042. 2 indexed citations
6.
Stamate, Daniel, et al.. (2021). A Machine Learning Approach for Predicting Deterioration in Alzheimer’s Disease. 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). 1443–1448. 3 indexed citations
7.
Stamate, Daniel, Daniel Ståhl, Simone Verhagen, et al.. (2019). Identifying psychosis spectrum disorder from experience sampling data using machine learning approaches. Schizophrenia Research. 209. 156–163. 10 indexed citations
8.
Smirnov, Evgueni, et al.. (2019). A regime-switching recurrent neural network model applied to wind time series. Applied Soft Computing. 80. 723–734. 6 indexed citations
9.
Logofătu, Doina, et al.. (2018). Particle swarm optimization algorithms for autonomous robots with deterministic leaders using space filling movements. Evolving Systems. 11(3). 383–396. 3 indexed citations
10.
Stamate, Daniel, et al.. (2018). A Machine Learning Framework for Predicting Dementia and Mild Cognitive Impairment. Huddersfield Research Portal (University of Huddersfield). 671–678. 15 indexed citations
11.
Ajnakina, Olesya, John Lally, Marta Di Forti, et al.. (2017). Utilising symptom dimensions with diagnostic categories improves prediction of time to first remission in first-episode psychosis. Schizophrenia Research. 193. 391–398. 9 indexed citations
12.
Stamate, Daniel, Wajdi Alghamdi, Daniel Ståhl, et al.. (2017). Predicting Psychosis Using the Experience Sampling Method with Mobile Apps. Research Publications (Maastricht University). 667–673. 5 indexed citations
13.
Belgrave, Danielle, Rachel Cassidy, Daniel Stamate, et al.. (2017). Predictive Modelling Strategies to Understand Heterogeneous Manifestations of Asthma in Early Life. LSHTM Research Online (London School of Hygiene and Tropical Medicine). 784. 68–75. 5 indexed citations
14.
Alghamdi, Wajdi, Daniel Stamate, Daniel Ståhl, et al.. (2016). A Prediction Modelling and Pattern Detection Approach for the First-Episode Psychosis Associated to Cannabis Use. Goldsmiths (University of London). 825–830. 4 indexed citations
15.
Stamate, Daniel, et al.. (2015). Sentiment and stock market volatility predictive modelling — A hybrid approach. 1–10. 5 indexed citations
16.
Stamate, Daniel, et al.. (2013). Improving time-efficiency in blocking expanding ring search for mobile ad hoc networks. Journal of Discrete Algorithms. 24. 59–67. 12 indexed citations
17.
Stamate, Daniel. (2006). Assumption based multi-valued semantics for extended logic programs. 10–10. 1 indexed citations
18.
Spyratos, Nicolas, et al.. (2003). Parametrized semantics of logic programs—a unifying framework. Theoretical Computer Science. 308(1-3). 429–447. 6 indexed citations
19.
Spyratos, Nicolas & Daniel Stamate. (1998). Multivalued stable semantics for databases with uncertain information. IOS Press eBooks. 129–144. 2 indexed citations
20.
Luchian, Henri & Daniel Stamate. (1992). Statistical protection for statistical databases. 160–177. 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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