Sunil V. Kalmady

2.3k total citations
89 papers, 1.3k citations indexed

About

Sunil V. Kalmady is a scholar working on Cognitive Neuroscience, Psychiatry and Mental health and Clinical Psychology. According to data from OpenAlex, Sunil V. Kalmady has authored 89 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Cognitive Neuroscience, 18 papers in Psychiatry and Mental health and 17 papers in Clinical Psychology. Recurrent topics in Sunil V. Kalmady's work include Functional Brain Connectivity Studies (17 papers), Obsessive-Compulsive Spectrum Disorders (14 papers) and Neural and Behavioral Psychology Studies (14 papers). Sunil V. Kalmady is often cited by papers focused on Functional Brain Connectivity Studies (17 papers), Obsessive-Compulsive Spectrum Disorders (14 papers) and Neural and Behavioral Psychology Studies (14 papers). Sunil V. Kalmady collaborates with scholars based in India, Canada and United States. Sunil V. Kalmady's co-authors include Ganesan Venkatasubramanian, Venkataram Shivakumar, Janardhanan C. Narayanaswamy, Bangalore N. Gangadhar, Sri Mahavir Agarwal, Vasanthapuram Ravi, Anushree Bose, Dania Jose, Anekal C. Amaresha and Aditi Subramaniam and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and European Heart Journal.

In The Last Decade

Sunil V. Kalmady

86 papers receiving 1.3k citations

Peers

Sunil V. Kalmady
Sunil V. Kalmady
Citations per year, relative to Sunil V. Kalmady Sunil V. Kalmady (= 1×) peers Venkataram Shivakumar

Countries citing papers authored by Sunil V. Kalmady

Since Specialization
Citations

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

Fields of papers citing papers by Sunil V. Kalmady

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sunil V. Kalmady. 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 Sunil V. Kalmady. The network helps show where Sunil V. Kalmady may publish in the future.

Co-authorship network of co-authors of Sunil V. Kalmady

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

All Works

20 of 20 papers shown
2.
Fine, Nowell M., Sunil V. Kalmady, Russell Greiner, et al.. (2024). Machine Learning For Risk Prediction After Heart Failure Emergency Department Visit or Hospital Admission Using Administrative Health Data. SHILAP Revista de lepidopterología. 3(10). e0000636–e0000636. 1 indexed citations
3.
Kalmady, Sunil V., Nariman Sepehrvand, Kevin R. Bainey, et al.. (2024). Development and validation of machine learning algorithms based on electrocardiograms for cardiovascular diagnoses at the population level. npj Digital Medicine. 7(1). 133–133. 15 indexed citations
4.
Kalmady, Sunil V., Nariman Sepehrvand, Kevin R. Bainey, et al.. (2023). Towards artificial intelligence-based learning health system for population-level mortality prediction using electrocardiograms. npj Digital Medicine. 6(1). 21–21. 19 indexed citations
5.
Kalmady, Sunil V., et al.. (2022). Analyzing biomarker discovery: Estimating the reproducibility of biomarker sets. PLoS ONE. 17(7). e0252697–e0252697. 3 indexed citations
6.
Lee, Douglas S., Finlay A. McAlister, Shihao Ma, et al.. (2022). Factors associated with SARS-CoV-2 test positivity in long-term care homes: A population-based cohort analysis using machine learning. The Lancet Regional Health - Americas. 6. 100146–100146. 6 indexed citations
7.
Lee, Douglas S., Shihao Ma, Anna Chu, et al.. (2021). Predictors of mortality among long‐term care residents with SARS‐CoV ‐2 infection. Journal of the American Geriatrics Society. 69(12). 3377–3388. 22 indexed citations
8.
Kalmady, Sunil V., Animesh Kumar Paul, Janardhanan C. Narayanaswamy, et al.. (2021). Prediction of Obsessive-Compulsive Disorder: Importance of Neurobiology-Aided Feature Design and Cross-Diagnosis Transfer Learning. Biological Psychiatry Cognitive Neuroscience and Neuroimaging. 7(7). 735–746. 14 indexed citations
9.
Shivakumar, Venkataram, Vanteemar S. Sreeraj, Sunil V. Kalmady, Bangalore N. Gangadhar, & Ganesan Venkatasubramanian. (2021). Pars Triangularis Volume Asymmetry and Schneiderian First Rank Symptoms in Antipsychotic-naïve Schizophrenia. Clinical Psychopharmacology and Neuroscience. 19(3). 507–513. 3 indexed citations
10.
Kalmady, Sunil V., Animesh Kumar Paul, Russell Greiner, et al.. (2020). Extending schizophrenia diagnostic model to predict schizotypy in first-degree relatives. Schizophrenia. 6(1). 30–30. 4 indexed citations
11.
Rajasekaran, Ashwini, Venkataram Shivakumar, Sunil V. Kalmady, et al.. (2020). Impact of NRG1 HapICE gene variants on digit ratio and dermatoglyphic measures in schizophrenia. Asian Journal of Psychiatry. 54. 102363–102363. 4 indexed citations
12.
Kalmady, Sunil V., Venkataram Shivakumar, Dania Jose, et al.. (2018). Plasma cytokines in minimally treated schizophrenia. Schizophrenia Research. 199. 292–296. 17 indexed citations
13.
Kalmady, Sunil V., Venkataram Shivakumar, Anekal C. Amaresha, et al.. (2018). CHRFAM7A gene expression in schizophrenia: clinical correlates and the effect of antipsychotic treatment. Journal of Neural Transmission. 125(4). 741–748. 11 indexed citations
14.
Narayanaswamy, Janardhanan C., Dania Jose, Sri Mahavir Agarwal, et al.. (2018). Neuro-hemodynamic endophenotypes of emotional interference in OCD: fMRI study using emotion counting stroop task. Asian Journal of Psychiatry. 39. 35–41. 3 indexed citations
15.
Shivakumar, Venkataram, Sunil V. Kalmady, Anekal C. Amaresha, et al.. (2018). Impact of antipsychotic treatment on methylation status of Interleukin-6 [IL-6] gene in Schizophrenia. Journal of Psychiatric Research. 104. 88–95. 17 indexed citations
16.
Shivakumar, Venkataram, Sunil V. Kalmady, Ashwini Rajasekaran, et al.. (2018). Telomere length and its association with hippocampal gray matter volume in antipsychotic-naïve/free schizophrenia patients. Psychiatry Research Neuroimaging. 282. 11–17. 7 indexed citations
17.
Chhabra, Harleen, Vanteemar S. Sreeraj, Sunil V. Kalmady, et al.. (2016). Auditory false perception in schizophrenia: Development and validation of auditory signal detection task. Asian Journal of Psychiatry. 24. 23–27. 16 indexed citations
18.
Jose, Dania, Janardhanan C. Narayanaswamy, Sri Mahavir Agarwal, et al.. (2015). Corpus callosum abnormalities in medication-naïve adult patients with obsessive compulsive disorder. Psychiatry Research Neuroimaging. 231(3). 341–345. 9 indexed citations
19.
Rao, Naren P., et al.. (2015). Plasma cytokine abnormalities in drug-naïve, comorbidity-free obsessive–compulsive disorder. Psychiatry Research. 229(3). 949–952. 74 indexed citations
20.
Agarwal, Sri Mahavir, Vijay Danivas, Anekal C. Amaresha, et al.. (2015). Cognitive mapping deficits in schizophrenia: Evidence from clinical correlates of visuospatial transformations. Psychiatry Research. 228(3). 304–311. 9 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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