Daniel Chamberlain

402 total citations
12 papers, 274 citations indexed

About

Daniel Chamberlain is a scholar working on Pulmonary and Respiratory Medicine, Signal Processing and Physiology. According to data from OpenAlex, Daniel Chamberlain has authored 12 papers receiving a total of 274 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Pulmonary and Respiratory Medicine, 4 papers in Signal Processing and 3 papers in Physiology. Recurrent topics in Daniel Chamberlain's work include Phonocardiography and Auscultation Techniques (6 papers), Music and Audio Processing (4 papers) and Respiratory and Cough-Related Research (3 papers). Daniel Chamberlain is often cited by papers focused on Phonocardiography and Auscultation Techniques (6 papers), Music and Audio Processing (4 papers) and Respiratory and Cough-Related Research (3 papers). Daniel Chamberlain collaborates with scholars based in United States, United Kingdom and India. Daniel Chamberlain's co-authors include R. Fletcher, Rahul Kodgule, James M. Chamberlain, Shilpa J. Patel, Vivek Miglani and Xinyue Deng and has published in prestigious journals such as Annals of Emergency Medicine, Academic Emergency Medicine and PubMed.

In The Last Decade

Daniel Chamberlain

11 papers receiving 270 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 Chamberlain United States 7 171 55 50 49 31 12 274
Miguel Ángel Fernández Granero Spain 9 227 1.3× 26 0.5× 58 1.2× 81 1.7× 29 0.9× 11 370
Amalia Setyati Indonesia 8 187 1.1× 34 0.6× 82 1.6× 31 0.6× 17 0.5× 25 268
Nilakash Das Belgium 9 257 1.5× 4 0.1× 87 1.7× 98 2.0× 19 0.6× 22 394
Anand K. Rajani United States 8 122 0.7× 7 0.1× 28 0.6× 7 0.1× 28 0.9× 9 295
Tudor Toma United Kingdom 12 661 3.9× 6 0.1× 141 2.8× 41 0.8× 22 0.7× 35 889
Renard Xaviero Adhi Pramono United Kingdom 7 362 2.1× 157 2.9× 81 1.6× 79 1.6× 28 0.9× 11 433
Marko Topalovic Belgium 12 450 2.6× 4 0.1× 154 3.1× 111 2.3× 25 0.8× 28 584
Zujin Luo China 12 248 1.5× 21 0.4× 27 0.5× 6 0.1× 30 1.0× 41 348
Sevgi Saryal Türkiye 10 417 2.4× 107 1.9× 68 1.4× 34 0.7× 4 0.1× 30 480
Scott Pappada United States 9 14 0.1× 8 0.1× 16 0.3× 17 0.3× 16 0.5× 21 376

Countries citing papers authored by Daniel Chamberlain

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Chamberlain

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Chamberlain

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

All Works

12 of 12 papers shown
1.
Patel, Shilpa J., Daniel Chamberlain, & James M. Chamberlain. (2018). A Machine Learning Approach to Predicting Need for Hospitalization for Pediatric Asthma Exacerbation at the Time of Emergency Department Triage. Academic Emergency Medicine. 25(12). 1463–1470. 89 indexed citations
2.
Chamberlain, Daniel, et al.. (2018). Pulmonary Screener: A Mobile Phone Screening Tool for Pulmonary and Respiratory Disease. 1–7. 5 indexed citations
3.
Chamberlain, Daniel, et al.. (2017). Classification of voluntary coughs applied to the screening of respiratory disease. PubMed. 2017. 1413–1416. 16 indexed citations
4.
Chamberlain, Daniel, et al.. (2017). Use of cough sounds for diagnosis and screening of pulmonary disease. 1–10. 34 indexed citations
5.
Chamberlain, Daniel & James M. Chamberlain. (2016). Making Sense of a Negative Clinical Trial Result: A Bayesian Analysis of a Clinical Trial of Lorazepam and Diazepam for Pediatric Status Epilepticus. Annals of Emergency Medicine. 69(1). 117–124. 5 indexed citations
6.
Chamberlain, Daniel, et al.. (2016). Application of semi-supervised deep learning to lung sound analysis. PubMed. 2016. 804–807. 71 indexed citations
7.
Chamberlain, Daniel, et al.. (2016). Applying Augmented Reality to Enable Automated and Low-Cost Data Capture from Medical Devices. 1–4. 4 indexed citations
8.
Chamberlain, Daniel, Rahul Kodgule, & R. Fletcher. (2016). A mobile platform for automated screening of asthma and chronic obstructive pulmonary disease. PubMed. 2016. 5192–5195. 20 indexed citations
9.
Chamberlain, Daniel, et al.. (2016). Smart phone-based auscultation platform. OA2000–OA2000. 3 indexed citations
10.
Chamberlain, Daniel, et al.. (2015). Mobile stethoscope and signal processing algorithms for pulmonary screening and diagnostics. 385–392. 20 indexed citations
11.
Fletcher, R., et al.. (2015). Implementation of smart phone video plethysmography and dependence on lighting parameters. PubMed. 2015. 3747–50. 7 indexed citations
12.

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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