Ritankar Das

4.1k total citations · 1 hit paper
64 papers, 2.6k citations indexed

About

Ritankar Das is a scholar working on Epidemiology, Artificial Intelligence and Surgery. According to data from OpenAlex, Ritankar Das has authored 64 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Epidemiology, 21 papers in Artificial Intelligence and 9 papers in Surgery. Recurrent topics in Ritankar Das's work include Sepsis Diagnosis and Treatment (27 papers), Machine Learning in Healthcare (19 papers) and Hemodynamic Monitoring and Therapy (6 papers). Ritankar Das is often cited by papers focused on Sepsis Diagnosis and Treatment (27 papers), Machine Learning in Healthcare (19 papers) and Hemodynamic Monitoring and Therapy (6 papers). Ritankar Das collaborates with scholars based in United States, United Kingdom and Belgium. Ritankar Das's co-authors include Jacob Calvert, Jana Hoffman, Uli K. Chettipally, Mitchell D. Feldman, David Shimabukuro, Melissa Jay, Qingqing Mao, Andrea J. McCoy, Christopher W. Barton and Christopher Barton and has published in prestigious journals such as Circulation, The Journal of Chemical Physics and Scientific Reports.

In The Last Decade

Ritankar Das

60 papers receiving 2.5k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach

2016 339 citations Thomas Desautels, Jacob Calvert et al. JMIR Medical Informatics profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ritankar Das United States	27	1.3k	1.2k	372	348	346	64	2.6k
Jacob Calvert United States	23	1.0k 0.8×	928 0.8×	273 0.7×	238 0.7×	217 0.6×	57	2.1k
Jana Hoffman United States	22	931 0.7×	840 0.7×	277 0.7×	238 0.7×	196 0.6×	41	1.7k
Uli K. Chettipally United States	21	888 0.7×	709 0.6×	232 0.6×	203 0.6×	134 0.4×	42	1.9k
Steven Horng United States	14	657 0.5×	1.2k 1.0×	607 1.6×	298 0.9×	336 1.0×	34	2.8k
Omar Badawi United States	20	1.1k 0.8×	854 0.7×	155 0.4×	224 0.6×	316 0.9×	53	2.7k
Matthieu Komorowski United Kingdom	20	470 0.4×	694 0.6×	431 1.2×	156 0.4×	617 1.8×	63	2.3k
Karandeep Singh United States	27	427 0.3×	572 0.5×	266 0.7×	234 0.7×	523 1.5×	106	2.6k
Richard A. Taylor United States	24	529 0.4×	863 0.7×	823 2.2×	238 0.7×	1.3k 3.8×	105	3.4k
Jie Ma China	22	449 0.3×	744 0.6×	485 1.3×	293 0.8×	607 1.8×	120	3.2k
Jenna Wiens United States	26	396 0.3×	775 0.6×	396 1.1×	298 0.9×	552 1.6×	81	2.4k

Countries citing papers authored by Ritankar Das

Since Specialization

Citations

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

Fields of papers citing papers by Ritankar Das

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ritankar Das

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

Garikipati, Anurag, Yunfan Zhou, Mădălina Ciobanu, et al.. (2024). Machine Learning Approach with Harmonized Multinational Datasets for Enhanced Prediction of Hypothyroidism in Patients with Type 2 Diabetes. Diagnostics. 14(11). 1152–1152.

Garikipati, Anurag, Mădălina Ciobanu, Gina Barnes, et al.. (2024). Parent-Led Applied Behavior Analysis to Impact Clinical Outcomes for Individuals on the Autism Spectrum: Retrospective Chart Review. JMIR Pediatrics and Parenting. 7. e62878–e62878.

Ciobanu, Mădălina, Anurag Garikipati, Gina Barnes, et al.. (2024). Family-Centric Applied Behavior Analysis Facilitates Improved Treatment Utilization and Outcomes. Journal of Clinical Medicine. 13(8). 2409–2409. 1 indexed citations

Garikipati, Anurag, Mădălina Ciobanu, Gina Barnes, et al.. (2023). Machine Learning Approach for Improved Longitudinal Prediction of Progression from Mild Cognitive Impairment to Alzheimer’s Disease. Diagnostics. 14(1). 13–13. 7 indexed citations

Garikipati, Anurag, et al.. (2023). Clinical Outcomes of a Hybrid Model Approach to Applied Behavioral Analysis Treatment. Cureus. 15(3). e36727–e36727. 4 indexed citations

Thapa, Resham, Anurag Garikipati, M. Ciobanu, et al.. (2023). Machine Learning Differentiation of Autism Spectrum Sub-Classifications. Journal of Autism and Developmental Disorders. 54(11). 4216–4231. 6 indexed citations

Allen, Angier, et al.. (2022). Prediction of diabetic kidney disease with machine learning algorithms, upon the initial diagnosis of type 2 diabetes mellitus. BMJ Open Diabetes Research & Care. 10(1). e002560–e002560. 50 indexed citations

Thapa, Rahul, Anurag Garikipati, Sepideh Shokouhi, et al.. (2022). Predicting Falls in Long-term Care Facilities: Machine Learning Study. JMIR Aging. 5(2). e35373–e35373. 22 indexed citations

Tso, Chak Foon, Anurag Garikipati, Abigail Green‐Saxena, Qingqing Mao, & Ritankar Das. (2021). Correlation of Population SARS-CoV-2 Cycle Threshold Values to Local Disease Dynamics: Exploratory Observational Study. JMIR Public Health and Surveillance. 7(6). e28265–e28265. 19 indexed citations

10.

Lam, Carson, Chak Foon Tso, Abigail Green‐Saxena, et al.. (2021). Semisupervised Deep Learning Techniques for Predicting Acute Respiratory Distress Syndrome From Time-Series Clinical Data: Model Development and Validation Study. JMIR Formative Research. 5(9). e28028–e28028. 13 indexed citations

11.

Thapa, Rahul, Anurag Garikipati, Anna Siefkas, et al.. (2021). Early prediction of severe acute pancreatitis using machine learning. Pancreatology. 22(1). 43–50. 32 indexed citations

12.

Allen, Angier, Samson Mataraso, Anna Siefkas, et al.. (2020). A Racially Unbiased, Machine Learning Approach to Prediction of Mortality: Algorithm Development Study. JMIR Public Health and Surveillance. 6(4). e22400–e22400. 35 indexed citations

13.

Burdick, Hoyt, Carson Lam, Samson Mataraso, et al.. (2020). Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial. Computers in Biology and Medicine. 124. 103949–103949. 96 indexed citations

14.

Mohamadlou, Hamid, J.R. Calvert, Sidney Le, et al.. (2019). Multicenter validation of a machine-learning algorithm for 48-h all-cause mortality prediction. Health Informatics Journal. 26(3). 1912–1925. 15 indexed citations

15.

Mao, Qingqing, Melissa Jay, Jana Hoffman, et al.. (2018). Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ Open. 8(1). e017833–e017833. 229 indexed citations

16.

Mohamadlou, Hamid, Christopher Barton, Uli K. Chettipally, et al.. (2018). Prediction of Acute Kidney Injury With a Machine Learning Algorithm Using Electronic Health Record Data. Canadian Journal of Kidney Health and Disease. 5. 2246655494–2246655494. 102 indexed citations

17.

McCoy, Andrea J. & Ritankar Das. (2017). Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit and hospital floor units. BMJ Open Quality. 6(2). e000158–e000158. 111 indexed citations

18.

Desautels, Thomas, Ritankar Das, Jacob Calvert, et al.. (2017). Prediction of early unplanned intensive care unit readmission in a UK tertiary care hospital: a cross-sectional machine learning approach. BMJ Open. 7(9). e017199–e017199. 83 indexed citations

19.

Ballard, Andrew J., Jacob D. Stevenson, Ritankar Das, & David J. Wales. (2016). Energy landscapes for a machine learning application to series data. The Journal of Chemical Physics. 144(12). 124119–124119. 19 indexed citations

20.

Desautels, Thomas, Jacob Calvert, Jana Hoffman, et al.. (2016). Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach. JMIR Medical Informatics. 4(3). e28–e28. 339 indexed citations breakdown →

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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