Arash Kia

1.8k total citations
23 papers, 345 citations indexed

About

Arash Kia is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Critical Care and Intensive Care Medicine. According to data from OpenAlex, Arash Kia has authored 23 papers receiving a total of 345 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 5 papers in Radiology, Nuclear Medicine and Imaging and 4 papers in Critical Care and Intensive Care Medicine. Recurrent topics in Arash Kia's work include Machine Learning in Healthcare (5 papers), Frailty in Older Adults (4 papers) and Nutrition and Health in Aging (4 papers). Arash Kia is often cited by papers focused on Machine Learning in Healthcare (5 papers), Frailty in Older Adults (4 papers) and Nutrition and Health in Aging (4 papers). Arash Kia collaborates with scholars based in United States, Israel and Canada. Arash Kia's co-authors include Prem Timsina, Robert Freeman, Matthew A. Levin, Madhu Mazumdar, David L. Reich, Himanshu Joshi, Roopa Kohli‐Seth, Pranai Tandon, Eyal Klang and Fabíola M. Ribeiro and has published in prestigious journals such as Scientific Reports, Journal of Neurochemistry and Critical Care Medicine.

In The Last Decade

Arash Kia

21 papers receiving 340 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Arash Kia United States 8 123 122 68 66 59 23 345
Anthony Lin United States 7 43 0.3× 99 0.8× 82 1.2× 93 1.4× 25 0.4× 16 433
Angier Allen United States 10 62 0.5× 103 0.8× 49 0.7× 114 1.7× 23 0.4× 12 448
Kristin Corey United States 9 71 0.6× 116 1.0× 97 1.4× 133 2.0× 37 0.6× 20 516
Benjamin Y. Li United States 10 40 0.3× 47 0.4× 39 0.6× 74 1.1× 51 0.9× 13 385
Markus Lingman Sweden 12 39 0.3× 127 1.0× 49 0.7× 58 0.9× 24 0.4× 29 424
Mary Saltz United States 11 55 0.4× 111 0.9× 24 0.4× 83 1.3× 31 0.5× 18 326
William Mitchell United States 9 110 0.9× 59 0.5× 137 2.0× 46 0.7× 6 0.1× 22 421
Eduardo Pontes Reis Brazil 7 111 0.9× 184 1.5× 134 2.0× 12 0.2× 24 0.4× 13 491
Takanobu Hirosawa Japan 9 161 1.3× 158 1.3× 309 4.5× 32 0.5× 9 0.2× 31 489
Renata R. Almeida United States 11 119 1.0× 22 0.2× 56 0.8× 53 0.8× 26 0.4× 22 348

Countries citing papers authored by Arash Kia

Since Specialization
Citations

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

Fields of papers citing papers by Arash Kia

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Arash Kia

This figure shows the co-authorship network connecting the top 25 collaborators of Arash Kia. A scholar is included among the top collaborators of Arash Kia 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 Arash Kia. Arash Kia 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
1.
Tandon, Pranai, Gary Oldenburg, Neha Dangayach, et al.. (2025). Impact of a Real-Time Ventilator Management Dashboard With Alerts. 3(4). 100167–100167.
2.
Jumreornvong, Oranicha, et al.. (2025). Biases in Artificial Intelligence Application in Pain Medicine. Journal of Pain Research. Volume 18. 1021–1033. 3 indexed citations
3.
Patel, Dhavalkumar D., Prem Timsina, Robert Freeman, et al.. (2025). Electronic-Medical-Record-Driven Machine Learning Predictive Model for Hospital-Acquired Pressure Injuries: Development and External Validation. Journal of Clinical Medicine. 14(4). 1175–1175.
4.
Friedman, Joseph I., Weijia Fu, Prem Timsina, et al.. (2025). Machine Learning Multimodal Model for Delirium Risk Stratification. JAMA Network Open. 8(5). e258874–e258874. 2 indexed citations
5.
Patel, Dhavalkumar D., Prem Timsina, Benjamin S. Glicksberg, et al.. (2024). Traditional Machine Learning, Deep Learning, and BERT (Large Language Model) Approaches for Predicting Hospitalizations From Nurse Triage Notes: Comparative Evaluation of Resource Management. PubMed. 3. e52190–e52190. 5 indexed citations
6.
Timsina, Prem, et al.. (2024). Malnutrition risk assessment using a machine learning‐based screening tool: A multicentre retrospective cohort. Journal of Human Nutrition and Dietetics. 37(3). 622–632. 4 indexed citations
7.
Tandon, Pranai, David L. Reich, Robert Freeman, et al.. (2024). Development and Validation of a Deep Learning Classifier Using Chest Radiographs to Predict Extubation Success in Patients Undergoing Invasive Mechanical Ventilation. Bioengineering. 11(6). 626–626. 1 indexed citations
8.
Scott, Erick R., Natalia Egorova, Robert Freeman, et al.. (2024). Assessing calibration and bias of a deployed machine learning malnutrition prediction model within a large healthcare system. npj Digital Medicine. 7(1). 149–149. 5 indexed citations
9.
Levin, Matthew A., Arash Kia, Prem Timsina, et al.. (2024). Real-Time Machine Learning Alerts to Prevent Escalation of Care: A Nonrandomized Clustered Pragmatic Clinical Trial*. Critical Care Medicine. 52(7). 1007–1020. 3 indexed citations
10.
11.
12.
Patel, Dhavalkumar D., Arash Kia, Benjamin S. Glicksberg, et al.. (2022). Predicting Adult Hospital Admission from Emergency Department Using Machine Learning: An Inclusive Gradient Boosting Model. Journal of Clinical Medicine. 11(23). 6888–6888. 9 indexed citations
13.
14.
Vaid, Akhil, Lili Chan, Kumardeep Chaudhary, et al.. (2021). Predictive Approaches for Acute Dialysis Requirement and Death in COVID-19. Clinical Journal of the American Society of Nephrology. 16(8). 1158–1168. 17 indexed citations
15.
Klang, Eyal, Benjamin Kummer, Neha Dangayach, et al.. (2021). Predicting adult neuroscience intensive care unit admission from emergency department triage using a retrospective, tabular-free text machine learning approach. Scientific Reports. 11(1). 29 indexed citations
16.
Joshi, Himanshu, Kavita V. Dharmarajan, Robert Freeman, et al.. (2020). Development and validation of a machine learning-based prediction model for near-term in-hospital mortality among patients with COVID-19. BMJ Supportive & Palliative Care. 12(e3). e424–e431. 39 indexed citations
17.
Timsina, Prem, Himanshu Joshi, Robert Freeman, et al.. (2020). MUST-Plus: A Machine Learning Classifier That Improves Malnutrition Screening in Acute Care Facilities. Journal of the American College of Nutrition. 40(1). 3–12. 24 indexed citations
18.
Joshi, Himanshu, Pranai Tandon, Robert Freeman, et al.. (2020). Using Machine Learning to Predict ICU Transfer in Hospitalized COVID-19 Patients. Journal of Clinical Medicine. 9(6). 1668–1668. 116 indexed citations
19.
Kia, Arash, et al.. (2020). A Machine Learning Model Enhances Prediction of Discharge for Surgical Patients. Journal of the American College of Surgeons. 231(4). S132–S132. 4 indexed citations
20.
Kia, Arash, et al.. (2010). Kindling alters neurosteroid‐induced modulation of phasic and tonic GABAAreceptor‐mediated currents: role of phosphorylation. Journal of Neurochemistry. 116(6). 1043–1056. 28 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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