Laura Ruth Venable

606 total citations
14 papers, 394 citations indexed

About

Laura Ruth Venable is a scholar working on Cardiology and Cardiovascular Medicine, Pulmonary and Respiratory Medicine and Epidemiology. According to data from OpenAlex, Laura Ruth Venable has authored 14 papers receiving a total of 394 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Cardiology and Cardiovascular Medicine, 4 papers in Pulmonary and Respiratory Medicine and 4 papers in Epidemiology. Recurrent topics in Laura Ruth Venable's work include Chronic Obstructive Pulmonary Disease (COPD) Research (4 papers), Machine Learning in Healthcare (3 papers) and Heart Failure Treatment and Management (3 papers). Laura Ruth Venable is often cited by papers focused on Chronic Obstructive Pulmonary Disease (COPD) Research (4 papers), Machine Learning in Healthcare (3 papers) and Heart Failure Treatment and Management (3 papers). Laura Ruth Venable collaborates with scholars based in United States. Laura Ruth Venable's co-authors include Matthew M. Churpek, L. Nelson Sanchez‐Pinto, John Fahrenbach, Kyle A. Carey, Dana P. Edelson, J.C. Rojas, Michael D. Howell, Vineet M. Arora, Wei Wei Lee and Kristen Wroblewski and has published in prestigious journals such as Critical Care Medicine, Journal of General Internal Medicine and Journal of the American Medical Informatics Association.

In The Last Decade

Laura Ruth Venable

14 papers receiving 385 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Laura Ruth Venable United States	7	100	98	63	57	56	14	394
Shaun T Alfreds United States	8	134 1.3×	117 1.2×	101 1.6×	67 1.2×	103 1.8×	12	455
Chengyin Ye China	10	119 1.2×	99 1.0×	81 1.3×	24 0.4×	83 1.5×	36	553
Devore S Culver United States	6	109 1.1×	99 1.0×	88 1.4×	67 1.2×	96 1.7×	10	352
Muhammad Ghous Pakistan	3	176 1.8×	155 1.6×	54 0.9×	39 0.7×	28 0.5×	7	446
Shu-Xia Li United States	7	89 0.9×	75 0.8×	75 1.2×	67 1.2×	206 3.7×	7	406
Le Zheng China	8	77 0.8×	74 0.8×	48 0.8×	27 0.5×	33 0.6×	12	291
Jarrod Frizzell United States	4	82 0.8×	67 0.7×	67 1.1×	28 0.5×	144 2.6×	13	300
Nathan Brajer United States	5	157 1.6×	111 1.1×	70 1.1×	33 0.6×	26 0.5×	5	384
Corrado Lanera Italy	14	65 0.7×	53 0.5×	40 0.6×	28 0.5×	98 1.8×	55	534
Jeremy A. Balch United States	13	97 1.0×	120 1.2×	28 0.4×	25 0.4×	63 1.1×	45	455

Countries citing papers authored by Laura Ruth Venable

Since Specialization

Citations

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

Fields of papers citing papers by Laura Ruth Venable

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Laura Ruth Venable. 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 Laura Ruth Venable. The network helps show where Laura Ruth Venable may publish in the future.

Co-authorship network of co-authors of Laura Ruth Venable

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

All Works

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14 of 14 papers shown

Carey, Kyle A., et al.. (2025). Comparison of Chart Review and Administrative Data in Developing Predictive Models for Readmissions in Chronic Obstructive Pulmonary Disease. Chronic Obstructive Pulmonary Diseases Journal of the COPD Foundation. 12(2). 175–183. 1 indexed citations

Mayampurath, Anoop, et al.. (2022). Predicting neurological outcomes after in-hospital cardiac arrests for patients with Coronavirus Disease 2019. Resuscitation. 178. 55–62. 4 indexed citations

Rojas, J.C., et al.. (2022). Using Machine Learning to Predict Likelihood and Cause of Readmission After Hospitalization for Chronic Obstructive Pulmonary Disease Exacerbation. International Journal of COPD. Volume 17. 2701–2709. 7 indexed citations

Mayampurath, Anoop, et al.. (2021). Comparison of Machine Learning Methods for Predicting Outcomes After In-Hospital Cardiac Arrest. Critical Care Medicine. 50(2). e162–e172. 11 indexed citations

Farnan, Jeanne M., Shannon K. Martin, Laura Ruth Venable, et al.. (2020). A Peer-Led Social Media Intervention to Improve Interest in Research Careers Among Urban Youth: Mixed Methods Study. JMIR Medical Education. 6(1). e16392–e16392. 2 indexed citations

Venable, Laura Ruth, et al.. (2019). Matching Patients with Chronic Obstructive Pulmonary Disease (COPD) to Personalized Care: A Novel Machine Learning Tool to Predict Cause of 90-Day Readmission. A7119–A7119. 2 indexed citations

Rojas, J.C., Patrick G. Lyons, Kyle A. Carey, et al.. (2019). Man vs. Machine: Comparison of a Machine Learning Algorithm to Clinician Intuition for Predicting Intensive Care Unit Readmission. A2459–A2459. 1 indexed citations

Venable, Laura Ruth, et al.. (2019). Accuracy Comparison of a Machine Learning Readmission Prediction Model with HOSPITAL and PEARL Scores for Chronic Obstructive Pulmonary Disease (COPD) Inpatients. A7118–A7118. 2 indexed citations

Sanchez‐Pinto, L. Nelson, Laura Ruth Venable, John Fahrenbach, & Matthew M. Churpek. (2018). Comparison of variable selection methods for clinical predictive modeling. International Journal of Medical Informatics. 116. 10–17. 189 indexed citations

10.

Rojas, J.C., Kyle A. Carey, Dana P. Edelson, et al.. (2018). Predicting Intensive Care Unit Readmission with Machine Learning Using Electronic Health Record Data. Annals of the American Thoracic Society. 15(7). 846–853. 109 indexed citations

11.

Lee, Wei Wei, et al.. (2016). Patient Perceptions of Electronic Medical Record Use by Faculty and Resident Physicians: A Mixed Methods Study. Journal of General Internal Medicine. 31(11). 1315–1322. 35 indexed citations

12.

Padula, William V., Robert D. Gibbons, Peter J. Pronovost, et al.. (2016). Using clinical data to predict high-cost performance coding issues associated with pressure ulcers: a multilevel cohort model. Journal of the American Medical Informatics Association. 24(e1). e95–e102. 24 indexed citations

13.

Padula, William V., Laura Ruth Venable, Mary Beth Flynn Makic, et al.. (2015). USING EHR DATA TO DYNAMICALLY PREDICT INCIDENCE OF HOSPITAL-ACQUIRED PRESSURE ULCERS. BMJ Quality & Safety. 24(11). 726–727. 1 indexed citations

14.

Pincavage, Amber T., Wei Wei Lee, Laura Ruth Venable, et al.. (2014). “Ms. B Changes Doctors”: Using a Comic and Patient Transition Packet to Engineer Patient-Oriented Clinic Handoffs (EPOCH). Journal of General Internal Medicine. 30(2). 257–260. 6 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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