Nilakash Das

737 total citations
22 papers, 394 citations indexed

About

Nilakash Das is a scholar working on Pulmonary and Respiratory Medicine, Sociology and Political Science and Physiology. According to data from OpenAlex, Nilakash Das has authored 22 papers receiving a total of 394 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Pulmonary and Respiratory Medicine, 4 papers in Sociology and Political Science and 4 papers in Physiology. Recurrent topics in Nilakash Das's work include Chronic Obstructive Pulmonary Disease (COPD) Research (15 papers), Delphi Technique in Research (4 papers) and Respiratory Support and Mechanisms (4 papers). Nilakash Das is often cited by papers focused on Chronic Obstructive Pulmonary Disease (COPD) Research (15 papers), Delphi Technique in Research (4 papers) and Respiratory Support and Mechanisms (4 papers). Nilakash Das collaborates with scholars based in Belgium, Italy and Canada. Nilakash Das's co-authors include Wim Janssens, Marko Topalovic, Sherif Gonem, Jean‐Marie Aerts, Rob Janssen, Renaud Louis, Christel Haenebalcke, Piet Vercauter, Pierre‐Régis Burgel and Marc Daenen and has published in prestigious journals such as SHILAP Revista de lepidopterología, European Respiratory Journal and Thorax.

In The Last Decade

Nilakash Das

20 papers receiving 379 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Nilakash Das Belgium	9	257	98	87	58	45	22	394
Marko Topalovic Belgium	12	450 1.8×	111 1.1×	154 1.8×	66 1.1×	47 1.0×	28	584
Miguel Ángel Fernández Granero Spain	9	227 0.9×	81 0.8×	58 0.7×	19 0.3×	76 1.7×	11	370
Daniel Chamberlain United States	7	171 0.7×	49 0.5×	50 0.6×	22 0.4×	29 0.6×	12	274
Sai Praveen Haranath India	7	196 0.8×	49 0.5×	75 0.9×	12 0.2×	24 0.5×	14	316
Brooks Kuhn United States	10	159 0.6×	37 0.4×	70 0.8×	15 0.3×	27 0.6×	38	350
Dru Claar United States	10	187 0.7×	56 0.6×	68 0.8×	13 0.2×	13 0.3×	15	419
Davy van de Sande Netherlands	8	68 0.3×	78 0.8×	21 0.2×	152 2.6×	111 2.5×	17	394
Joanna Brisbane Australia	10	115 0.4×	37 0.4×	46 0.5×	12 0.2×	16 0.4×	16	323
Sivasubramanium V. Bhavani United States	12	247 1.0×	39 0.4×	24 0.3×	10 0.2×	56 1.2×	35	590
Renata R. Almeida United States	11	80 0.3×	119 1.2×	12 0.1×	56 1.0×	22 0.5×	22	348

Countries citing papers authored by Nilakash Das

Since Specialization

Citations

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

Fields of papers citing papers by Nilakash Das

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nilakash Das

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

Gyselinck, Iwein, et al.. (2023). Estimating individual treatment effects on COPD exacerbations by causal machine learning on randomised controlled trials. Thorax. 78(10). 983–989. 12 indexed citations

Das, Nilakash, Iwein Gyselinck, Thierry Troosters, et al.. (2023). Principal component analysis of flow-volume curves in COPDGene to link spirometry with phenotypes of COPD. Respiratory Research. 24(1). 20–20. 2 indexed citations

Gille, Thomas, Pradeesh Sivapalan, Georgios Kaltsakas, et al.. (2022). ERS International Congress 2021: highlights from the Respiratory Clinical Care and Physiology Assembly. ERJ Open Research. 8(2). 710–2021.

Montagna, Isabella, Massimo Corradi, Sanja Stanojevic, et al.. (2022). Artificial intelligence based software facilitates spirometry quality control in asthma and COPD clinical trials. ERJ Open Research. 9(1). 292–2022. 13 indexed citations

Murugadoss, Sivakumar, Nilakash Das, Lode Godderis, et al.. (2021). Identifying nanodescriptors to predict the toxicity of nanomaterials: a case study on titanium dioxide. Environmental Science Nano. 8(2). 580–590. 10 indexed citations

Das, Nilakash, et al.. (2021). Study of demographic profile of chronic kidney disease patients undergoing haemodialysis in a tertiary care center of Tripura and evaluation of the causative factors for chronic kidney disease. 11(1). 16–16.

Stanojevic, Sanja, Pippa Powell, Graham L. Hall, et al.. (2021). Real-world application of Spirometry Quality Control Deep-Learning Algorithm. OA2688–OA2688. 1 indexed citations

Montagna, Isabella, et al.. (2021). Artificial Intelligence Assists in Quality Assessment of Spirometry in Clinical Trials. A4606–A4606. 1 indexed citations

Montagna, Isabella, et al.. (2021). ArtiQ.QC facilitates spirometry quality control in asthma and COPD clinical trials. PA2505–PA2505. 1 indexed citations

10.

Das, Nilakash, et al.. (2021). Clinically applicable machine learning prediction model for pulmonary hypertension due to left heart disease. OA175–OA175. 1 indexed citations

11.

Das, Nilakash, et al.. (2020). Deep-learning algorithm helps to standardise ATS/ERS spirometric acceptability and usability criteria. European Respiratory Journal. 56(6). 2000603–2000603. 23 indexed citations

12.

Gonem, Sherif, Wim Janssens, Nilakash Das, & Marko Topalovic. (2020). Applications of artificial intelligence and machine learning in respiratory medicine. Thorax. 75(8). 695–701. 51 indexed citations

13.

Das, Nilakash, et al.. (2019). Estimating Airway Resistance from Forced Expiration in Spirometry. Applied Sciences. 9(14). 2842–2842. 4 indexed citations

14.

Topalovic, Marko, Nilakash Das, Pierre‐Régis Burgel, et al.. (2019). Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests. European Respiratory Journal. 53(4). 1801660–1801660. 128 indexed citations

15.

Das, Nilakash, Wim Janssens, Russell G. Buhr, et al.. (2019). Spirometric indices of early airflow impairment in individuals at risk of developing COPD: Spirometry beyond FEV1/FVC. Respiratory Medicine. 156. 58–68. 43 indexed citations

16.

Das, Nilakash, Marko Topalovic, Jean‐Marie Aerts, & Wim Janssens. (2019). <p>Area under the forced expiratory flow-volume loop in spirometry indicates severe hyperinflation in COPD patients</p>. International Journal of COPD. Volume 14. 409–418. 18 indexed citations

17.

Das, Nilakash, Marko Topalovic, Jo Raskin, et al.. (2019). Explaining predictions of an automated pulmonary function test interpretation algorithm. PA2227–PA2227. 1 indexed citations

18.

Das, Nilakash, Marko Topalovic, & Wim Janssens. (2017). Artificial intelligence in diagnosis of obstructive lung disease. Current Opinion in Pulmonary Medicine. 24(2). 117–123. 77 indexed citations

19.

Rath, Pratap Chandra, et al.. (2000). Carotid angioplasty under cerebral protection with "PercuSurge Guardwire".. PubMed. 52(4). 461–3. 2 indexed citations

20.

Das, Nilakash, et al.. (1983). Nephrobronchial fistula. Closure by thoraco-abdominal approach and the use of free fascia lata graft. (A case report).. SHILAP Revista de lepidopterología. 29(2). 108–10, 110A. 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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