Madina Hasan

401 total citations
31 papers, 222 citations indexed

About

Madina Hasan is a scholar working on Artificial Intelligence, Signal Processing and Emergency Medicine. According to data from OpenAlex, Madina Hasan has authored 31 papers receiving a total of 222 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 6 papers in Signal Processing and 6 papers in Emergency Medicine. Recurrent topics in Madina Hasan's work include Speech Recognition and Synthesis (10 papers), Emergency and Acute Care Studies (6 papers) and Natural Language Processing Techniques (6 papers). Madina Hasan is often cited by papers focused on Speech Recognition and Synthesis (10 papers), Emergency and Acute Care Studies (6 papers) and Natural Language Processing Techniques (6 papers). Madina Hasan collaborates with scholars based in United Kingdom, South Africa and Spain. Madina Hasan's co-authors include Thomas Hain, Joab R. Winkler, Rama Doddipatla, Óscar Saz, Anthony J. H. Simons, Shelagh Brumfitt, Phil Green, Guy J. Brown, Ning Ma and Peter A. Bath and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and BMJ Open.

In The Last Decade

Madina Hasan

30 papers receiving 201 citations

Peers

Madina Hasan

CTM

ECP

PHYSI

Kosuke Tanabe Japan

James P. Bien United States

Fengyi Tang United States

Fengling He China

J. Yates United Kingdom

Łukasz Roguski United Kingdom

Daniel Howard United States

Teemu Lukkari Finland

Madhurananda Pahar South Africa

Alejandro Sáez Chile

Kosuke Tanabe Japan

Madina Hasan

40 ×0.4

27 ×0.6

37 ×1.1

CTM

38 ×1.5

ECP

14 ×0.6

PHYSI

Citations per year, relative to Madina Hasan Madina Hasan (= 1×) peers Kosuke Tanabe

Countries citing papers authored by Madina Hasan

Since Specialization

Citations

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

Fields of papers citing papers by Madina Hasan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Madina Hasan

This figure shows the co-authorship network connecting the top 25 collaborators of Madina Hasan. A scholar is included among the top collaborators of Madina Hasan 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 Madina Hasan. Madina Hasan 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

Patterson, Joanne, Catriona R Mayland, Peter A. Bath, et al.. (2025). A network approach to addressing the needs of patients with incurable head and neck cancer and their families. Health Technology Assessment. 1–29.

Kimenai, Dorien M., Sophie Williams, Felicity Evison, et al.. (2024). Adoption of high-sensitivity cardiac troponin for risk stratification of patients with suspected myocardial infarction: a multicentre cohort study. The Lancet Regional Health - Europe. 43. 100960–100960. 3 indexed citations

Marincowitz, Carl, Madina Hasan, Peter Hodkinson, et al.. (2024). Prognostic accuracy of eight triage scores in suspected COVID-19 in an Emergency Department low-income setting: An observational cohort study. African Journal of Emergency Medicine. 14(1). 51–57. 2 indexed citations

Marincowitz, Carl, Laura Sbaffi, Madina Hasan, et al.. (2023). External validation of triage tools for adults with suspected COVID-19 in a middle-income setting: an observational cohort study. Emergency Medicine Journal. 40(7). 509–517. 4 indexed citations

Marincowitz, Carl, Peter Hodkinson, David McAlpine, et al.. (2023). LMIC-PRIEST: Derivation and validation of a clinical severity score for acutely ill adults with suspected COVID-19 in a middle-income setting. PLoS ONE. 18(6). e0287091–e0287091. 3 indexed citations

Fuller, Gordon, Madina Hasan, Peter Hodkinson, et al.. (2023). Training and testing of a gradient boosted machine learning model to predict adverse outcome in patients presenting to emergency departments with suspected covid-19 infection in a middle-income setting. SHILAP Revista de lepidopterología. 2(9). e0000309–e0000309. 2 indexed citations

Marincowitz, Carl, Tony Stone, Peter A. Bath, et al.. (2022). Accuracy of telephone triage for predicting adverse outcomes in suspected COVID-19: an observational cohort study. BMJ Quality & Safety. 33(6). 375–385. 4 indexed citations

Marincowitz, Carl, Tony Stone, Madina Hasan, et al.. (2022). Accuracy of emergency medical service telephone triage of need for an ambulance response in suspected COVID-19: an observational cohort study. BMJ Open. 12(5). e058628–e058628. 1 indexed citations

Marincowitz, Carl, Laura Sutton, Tony Stone, et al.. (2022). Prognostic accuracy of triage tools for adults with suspected COVID-19 in a prehospital setting: an observational cohort study. Emergency Medicine Journal. 39(4). 317–324. 11 indexed citations

10.

Hasan, Madina, Peter A. Bath, Carl Marincowitz, et al.. (2022). Pre-Hospital Prediction of Adverse Outcomes in Patients with Suspected COVID-19: Development, Application and Comparison of Machine Learning and Deep Learning Methods. SSRN Electronic Journal. 2 indexed citations

11.

Stappen, Lukas, Georgios Rizos, Madina Hasan, Thomas Hain, & Björn W. Schuller. (2020). Uncertainty-Aware Machine Support for Paper Reviewing on the Interspeech 2019 Submission Corpus. OPUS (Augsburg University). 1808–1812. 7 indexed citations

12.

Ma, Ning, et al.. (2019). Deep Learning Features for Robust Detection of Acoustic Events in Sleep-disordered Breathing. 810–814. 14 indexed citations

13.

Hasan, Madina, et al.. (2018). A Lightly Supervised Approach to Detect Stuttering in Children's Speech. 3433–3437. 22 indexed citations

14.

Ng, Raymond W. M., et al.. (2016). The Sheffield language recognition system in NIST LRE 2015. 181–187. 3 indexed citations

15.

Liu, Yulan, Charles Fox, Madina Hasan, & Thomas Hain. (2016). The Sheffield Wargame Corpus — Day Two and Day Three. 3833–3837. 3 indexed citations

16.

Hasan, Madina, Rama Doddipatla, & Thomas Hain. (2015). Noise-matched training of CRF based sentence end detection models. 349–353. 7 indexed citations

17.

Hasan, Madina, Rama Doddipatla, & Thomas Hain. (2014). Multi-pass sentence-end detection of lecture speech. 2902–2906. 12 indexed citations

18.

Winkler, Joab R. & Madina Hasan. (2012). An improved non-linear method for the computation of a structured low rank approximation of the Sylvester resultant matrix. Journal of Computational and Applied Mathematics. 237(1). 253–268. 13 indexed citations

19.

Winkler, Joab R., et al.. (2012). The computation of multiple roots of a polynomial. Journal of Computational and Applied Mathematics. 236(14). 3478–3497. 10 indexed citations

20.

Winkler, Joab R. & Madina Hasan. (2010). A non-linear structure preserving matrix method for the low rank approximation of the Sylvester resultant matrix. Journal of Computational and Applied Mathematics. 234(12). 3226–3242. 12 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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