Saqib Ejaz Awan

431 total citations
8 papers, 256 citations indexed

About

Saqib Ejaz Awan is a scholar working on Health Information Management, Pathology and Forensic Medicine and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Saqib Ejaz Awan has authored 8 papers receiving a total of 256 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Health Information Management, 2 papers in Pathology and Forensic Medicine and 2 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Saqib Ejaz Awan's work include Artificial Intelligence in Healthcare (4 papers), Nitric Oxide and Endothelin Effects (2 papers) and Machine Learning in Healthcare (2 papers). Saqib Ejaz Awan is often cited by papers focused on Artificial Intelligence in Healthcare (4 papers), Nitric Oxide and Endothelin Effects (2 papers) and Machine Learning in Healthcare (2 papers). Saqib Ejaz Awan collaborates with scholars based in Australia, United States and Spain. Saqib Ejaz Awan's co-authors include Ferdous Sohel, Mohammed Bennamoun, Girish Dwivedi, Frank Sanfilippo, Benjamin J.W. Chow, Octavian Toma, Benedikt Preckel, W. Schlack, Nina C. Weber and Jan Fräßdorf and has published in prestigious journals such as PLoS ONE, Anesthesiology and Neural Computing and Applications.

In The Last Decade

Saqib Ejaz Awan

7 papers receiving 248 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Saqib Ejaz Awan Australia	5	106	90	80	32	24	8	256
Tao Shan China	11	68 0.6×	47 0.5×	21 0.3×	37 1.2×	17 0.7×	37	357
Basma Mohamed United States	9	88 0.8×	54 0.6×	26 0.3×	20 0.6×	89 3.7×	37	422
Yuri Ahuja United States	9	45 0.4×	104 1.2×	51 0.6×	17 0.5×	24 1.0×	14	244
Min Jeong Kim South Korea	9	234 2.2×	53 0.6×	44 0.6×	45 1.4×	36 1.5×	27	460
Byungjin Choi South Korea	10	135 1.3×	40 0.4×	7 0.1×	18 0.6×	20 0.8×	41	357
Radiyati Umi Partan Indonesia	9	179 1.7×	31 0.3×	19 0.2×	58 1.8×	19 0.8×	35	334
Ju Gao China	10	31 0.3×	135 1.5×	42 0.5×	27 0.8×	4 0.2×	29	242
Álvaro Camilo Dias Faria Brazil	16	25 0.2×	44 0.5×	28 0.3×	26 0.8×	17 0.7×	29	569
Ilke Ӧzcan United States	9	164 1.5×	24 0.3×	26 0.3×	28 0.9×	18 0.8×	31	280

Countries citing papers authored by Saqib Ejaz Awan

Since Specialization

Citations

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

Fields of papers citing papers by Saqib Ejaz Awan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Saqib Ejaz Awan. 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 Saqib Ejaz Awan. The network helps show where Saqib Ejaz Awan may publish in the future.

Co-authorship network of co-authors of Saqib Ejaz Awan

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

All Works

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8 of 8 papers shown

1.

Awan, Saqib Ejaz, Mohammed Bennamoun, Ferdous Sohel, Frank Sanfilippo, & Girish Dwivedi. (2022). A reinforcement learning-based approach for imputing missing data. Neural Computing and Applications. 34(12). 9701–9716. 20 indexed citations

2.

Awan, Saqib Ejaz, Mohammed Bennamoun, Ferdous Sohel, et al.. (2019). Feature selection and transformation by machine learning reduce variable numbers and improve prediction for heart failure readmission or death. PLoS ONE. 14(6). e0218760–e0218760. 48 indexed citations

3.

Awan, Saqib Ejaz, Mohammed Bennamoun, Ferdous Sohel, Frank Sanfilippo, & Girish Dwivedi. (2019). Machine Learning-Based Prediction of Heart Failure Readmission or Death: Implications of Choosing the Right Model and the Right Metrics. ESC Heart Failure. 6(2). 428–435. 90 indexed citations

4.

Awan, Saqib Ejaz, Ferdous Sohel, Frank Sanfilippo, Mohammed Bennamoun, & Girish Dwivedi. (2017). Machine learning in heart failure. Current Opinion in Cardiology. 33(2). 190–195. 74 indexed citations

5.

Awan, Saqib Ejaz, Ferdous Sohel, Frank Sanfilippo, Mohammed Bennamoun, & Girish Dwivedi. (2017). Machine learning in heart failure. Murdoch Research Repository (Murdoch University).

6.

Awan, Saqib Ejaz, Sajid Gul Khawaja, Muazzam A. Khan, & Muhammad Usman Akram. (2016). A surrogate channel based analysis of EEG signals for detection of epileptic seizure. 384–388. 1 indexed citations

7.

Weber, Nina C., et al.. (2005). Effects of Nitrous Oxide on the Rat Heart In Vivo. Anesthesiology. 103(6). 1174–1182. 1 indexed citations

8.

Weber, Nina C., Octavian Toma, Saqib Ejaz Awan, et al.. (2005). Effects of Nitrous Oxide on the Rat Heart In Vivo . Anesthesiology. 103(6). 1174–1182. 22 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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