 Connection
Kathryn Colborn to Electronic Health Records
This is a "connection" page, showing publications Kathryn Colborn has written about Electronic Health Records.
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Connection Strength |
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3.108 |
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Colborn KL, Zhuang Y, Dyas AR, Henderson WG, Madsen HJ, Bronsert MR, Matheny ME, Lambert-Kerzner A, Myers QWO, Meguid RA. Development and validation of models for detection of postoperative infections using structured electronic health records data and machine learning. Surgery. 2023 02; 173(2):464-471.
Score: 0.542
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Dyas AR, Zhuang Y, Meguid RA, Henderson WG, Madsen HJ, Bronsert MR, Colborn KL. Development and validation of a model for surveillance of postoperative bleeding complications using structured electronic health records data. Surgery. 2022 12; 172(6):1728-1732.
Score: 0.535
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Bronsert M, Singh AB, Henderson WG, Hammermeister K, Meguid RA, Colborn KL. Identification of postoperative complications using electronic health record data and machine learning. Am J Surg. 2020 07; 220(1):114-119.
Score: 0.436
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Colborn KL, Bronsert M, Hammermeister K, Henderson WG, Singh AB, Meguid RA. Identification of urinary tract infections using electronic health record data. Am J Infect Control. 2019 04; 47(4):371-375.
Score: 0.411
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Colborn KL, Bronsert M, Amioka E, Hammermeister K, Henderson WG, Meguid R. Identification of surgical site infections using electronic health record data. Am J Infect Control. 2018 11; 46(11):1230-1235.
Score: 0.398
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Calcaterra SL, Scarbro S, Hull ML, Forber AD, Binswanger IA, Colborn KL. Prediction of Future Chronic Opioid Use Among Hospitalized Patients. J Gen Intern Med. 2018 06; 33(6):898-905.
Score: 0.388
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Zhuang Y, Dyas A, Meguid RA, Henderson WG, Bronsert M, Madsen H, Colborn KL. Preoperative Prediction of Postoperative Infections Using Machine Learning and Electronic Health Record Data. Ann Surg. 2024 Apr 01; 279(4):720-726.
Score: 0.143
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Mandair D, Tiwari P, Simon S, Colborn KL, Rosenberg MA. Prediction of incident myocardial infarction using machine learning applied to harmonized electronic health record data. BMC Med Inform Decis Mak. 2020 10 02; 20(1):252.
Score: 0.117
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Tiwari P, Colborn KL, Smith DE, Xing F, Ghosh D, Rosenberg MA. Assessment of a Machine Learning Model Applied to Harmonized Electronic Health Record Data for the Prediction of Incident Atrial Fibrillation. JAMA Netw Open. 2020 01 03; 3(1):e1919396.
Score: 0.111
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Scott HF, Colborn KL, Sevick CJ, Bajaj L, Kissoon N, Deakyne Davies SJ, Kempe A. Development and Validation of a Predictive Model of the Risk of Pediatric Septic Shock Using Data Known at the Time of Hospital Arrival. J Pediatr. 2020 02; 217:145-151.e6.
Score: 0.027
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Connection Strength
The connection strength for concepts is the sum of the scores for each matching publication.
Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.
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