I-Min Chiu

640 total citations
44 papers, 390 citations indexed

About

I-Min Chiu is a scholar working on Emergency Medicine, Epidemiology and Public Health, Environmental and Occupational Health. According to data from OpenAlex, I-Min Chiu has authored 44 papers receiving a total of 390 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Emergency Medicine, 15 papers in Epidemiology and 6 papers in Public Health, Environmental and Occupational Health. Recurrent topics in I-Min Chiu's work include Emergency and Acute Care Studies (14 papers), Trauma and Emergency Care Studies (11 papers) and Sepsis Diagnosis and Treatment (9 papers). I-Min Chiu is often cited by papers focused on Emergency and Acute Care Studies (14 papers), Trauma and Emergency Care Studies (11 papers) and Sepsis Diagnosis and Treatment (9 papers). I-Min Chiu collaborates with scholars based in Taiwan and United States. I-Min Chiu's co-authors include Chun‐Hung Richard Lin, Ying‐Hsien Huang, Chi-Yung Cheng, Kuan-Han Wu, Chao-Jui Li, Fu‐Jen Cheng, Chia‐Te Kung, Yan‐Ren Lin, Chih‐Min Tsai and Hsiu-Yung Pan and has published in prestigious journals such as Nature, Journal of the American College of Cardiology and International Journal of Environmental Research and Public Health.

In The Last Decade

I-Min Chiu

43 papers receiving 380 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
I-Min Chiu Taiwan	12	160	111	54	50	41	44	390
Maria Antonietta Bressan Italy	12	257 1.6×	61 0.5×	31 0.6×	32 0.6×	22 0.5×	20	445
Tommaso Scquizzato Italy	15	484 3.0×	64 0.6×	93 1.7×	49 1.0×	56 1.4×	49	640
Chi-Yung Cheng Taiwan	10	58 0.4×	105 0.9×	27 0.5×	33 0.7×	17 0.4×	27	295
Chao-Jui Li Taiwan	13	302 1.9×	79 0.7×	144 2.7×	64 1.3×	68 1.7×	46	549
Kenneth Boon Kiat Tan Singapore	8	163 1.0×	41 0.4×	22 0.4×	21 0.4×	17 0.4×	25	334
Giuseppe Maria Sechi Italy	14	350 2.2×	91 0.8×	51 0.9×	110 2.2×	44 1.1×	45	815
Mathieu Boutonnet France	14	282 1.8×	53 0.5×	122 2.3×	54 1.1×	40 1.0×	62	569
Se Uk Lee South Korea	10	135 0.8×	115 1.0×	75 1.4×	26 0.5×	31 0.8×	46	338
R. W. M. A. Madushani United States	7	33 0.2×	109 1.0×	60 1.1×	43 0.9×	89 2.2×	13	286
Aftab Azad Qatar	10	86 0.5×	80 0.7×	80 1.5×	35 0.7×	24 0.6×	36	345

Countries citing papers authored by I-Min Chiu

Since Specialization

Citations

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

Fields of papers citing papers by I-Min Chiu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by I-Min Chiu. 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 I-Min Chiu. The network helps show where I-Min Chiu may publish in the future.

Co-authorship network of co-authors of I-Min Chiu

This figure shows the co-authorship network connecting the top 25 collaborators of I-Min Chiu. A scholar is included among the top collaborators of I-Min Chiu 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 I-Min Chiu. I-Min Chiu 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

Chiu, I-Min, Neal Yuan, Tien‐Yu Chen, et al.. (2025). Comprehensive echocardiogram evaluation with view primed vision language AI. Nature. 650(8103). 970–977. 1 indexed citations

Wang, Yu-Hsin, et al.. (2024). Artificial Intelligence Application in Skull Bone Fracture with Segmentation Approach. Journal of Imaging Informatics in Medicine. 38(1). 31–46. 2 indexed citations

Hsu, Ting‐Yu, Chi-Yung Cheng, I-Min Chiu, et al.. (2024). Explainable Deep Learning Model for Predicting Serious Adverse Events in Hospitalized Geriatric Patients Within 72 Hours. Clinical Interventions in Aging. Volume 19. 1051–1063. 1 indexed citations

Wu, Cheng‐Ching, et al.. (2023). Development and validation of a deep learning pipeline to measure pericardial effusion in echocardiography. Frontiers in Cardiovascular Medicine. 10. 1195235–1195235. 8 indexed citations

Chiu, I-Min, et al.. (2023). Use of a Deep-Learning Algorithm to Guide Novices in Performing Focused Assessment With Sonography in Trauma. JAMA Network Open. 6(3). e235102–e235102. 13 indexed citations

Chiu, I-Min, et al.. (2022). Utilization of Personalized Machine-Learning to Screen for Dysglycemia from Ambulatory ECG, toward Noninvasive Blood Glucose Monitoring. Biosensors. 13(1). 23–23. 7 indexed citations

Kung, Chia‐Te, et al.. (2022). Machine learning models for predicting in-hospital mortality in patient with sepsis: Analysis of vital sign dynamics. Frontiers in Medicine. 9. 964667–964667. 14 indexed citations

Cheng, Chi-Yung, et al.. (2021). Machine Learning Models for Survival and Neurological Outcome Prediction of Out‐of‐Hospital Cardiac Arrest Patients. BioMed Research International. 2021(1). 9590131–9590131. 20 indexed citations

Chiu, I-Min, et al.. (2021). Using a Multiclass Machine Learning Model to Predict the Outcome of Acute Ischemic Stroke Requiring Reperfusion Therapy. Diagnostics. 11(1). 80–80. 11 indexed citations

10.

Huang, Yi-Syun, et al.. (2021). Delta Shock Index During Emergency Department Stay Is Associated With in Hospital Mortality in Critically Ill Patients. Frontiers in Medicine. 8. 648375–648375. 9 indexed citations

11.

Chiu, I-Min, et al.. (2021). Using Machine Learning to Predict Invasive Bacterial Infections in Young Febrile Infants Visiting the Emergency Department. Journal of Clinical Medicine. 10(9). 1875–1875. 11 indexed citations

12.

Huang, Kuo‐Chen, et al.. (2021). Shock Index, Pediatric Age-Adjusted Predicts Morbidity and Mortality in Children Admitted to the Intensive Care Unit. Frontiers in Pediatrics. 9. 727466–727466. 6 indexed citations

13.

Chiu, I-Min, et al.. (2021). Deep Learning Assisted Detection of Abdominal Free Fluid in Morison's Pouch During Focused Assessment With Sonography in Trauma. Frontiers in Medicine. 8. 707437–707437. 21 indexed citations

14.

Tsai, Chih‐Min, Chun‐Hung Richard Lin, Huan Zhang, et al.. (2020). Using Machine Learning to Predict Bacteremia in Febrile Children Presented to the Emergency Department. Diagnostics. 10(5). 307–307. 16 indexed citations

15.

Chiu, I-Min, et al.. (2020). Using multiclass machine learning model to improve outcome prediction of acute ischemic stroke patients after reperfusion therapy. 225–231. 2 indexed citations

16.

Kuo, Kuang-Che, et al.. (2019). The clinical features and therapy of community-acquired gram negative bacteremia in children less than three years old. Pediatrics & Neonatology. 61(1). 51–57. 5 indexed citations

17.

Cheng, Fu‐Jen, et al.. (2019). ICU admission following an unscheduled return visit to the pediatric emergency department within 72 hours. BMC Pediatrics. 19(1). 268–268. 7 indexed citations

18.

Chiu, I-Min, et al.. (2018). Diagnostic values of C-reactive protein and complete blood cell to identify invasive bacterial infection in young febrile infants. Pediatrics & Neonatology. 60(2). 197–200. 8 indexed citations

19.

Chiu, I-Min, et al.. (2017). The influence of crowding on clinical practice in the emergency department. The American Journal of Emergency Medicine. 36(1). 56–60. 52 indexed citations

20.

Chiu, I-Min, Chia‐Te Kung, Fu‐Jen Cheng, et al.. (2017). The influence of resident seniority on supervised practice in the emergency department. Medicine. 96(4). e5987–e5987. 5 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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