Richard C Kiefer
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Explore the profile of Richard C Kiefer including associated specialties, affiliations and a list of published articles.
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19
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261
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Recent Articles
1.
Xu Z, Vekaria V, Wang F, Cukor J, Su C, Adekkanattu P, et al.
Psychiatr Res Clin Pract
. 2023 Dec;
5(4):118-125.
PMID: 38077277
Objective: To evaluate if a machine learning approach can accurately predict antidepressant treatment outcome using electronic health records (EHRs) from patients with depression. Method: This study examined 808 patients with...
2.
Xu J, Wang F, Xu Z, Adekkanattu P, Brandt P, Jiang G, et al.
Learn Health Syst
. 2020 Oct;
4(4):e10246.
PMID: 33083543
Introduction: We sought to assess longitudinal electronic health records (EHRs) using machine learning (ML) methods to computationally derive probable Alzheimer's Disease (AD) and related dementia subphenotypes. Methods: A retrospective analysis...
3.
Xu Z, Wang F, Adekkanattu P, Bose B, Vekaria V, Brandt P, et al.
Learn Health Syst
. 2020 Oct;
4(4):e10241.
PMID: 33083540
Objective: To identify depression subphenotypes from Electronic Health Records (EHRs) using machine learning methods, and analyze their characteristics with respect to patient demographics, comorbidities, and medications. Materials And Methods: Using...
4.
Brandt P, Kiefer R, Pacheco J, Adekkanattu P, Sholle E, Ahmad F, et al.
Learn Health Syst
. 2020 Oct;
4(4):e10233.
PMID: 33083538
Introduction: Electronic health record (EHR)-driven phenotyping is a critical first step in generating biomedical knowledge from EHR data. Despite recent progress, current phenotyping approaches are manual, time-consuming, error-prone, and platform-specific....
5.
Rasmussen L, Brandt P, Jiang G, Kiefer R, Pacheco J, Adekkanattu P, et al.
AMIA Annu Symp Proc
. 2020 Apr;
2019:755-764.
PMID: 32308871
With the increased adoption of electronic health records, data collected for routine clinical care is used for health outcomes and population sciences research, including the identification of phenotypes. In recent...
6.
Adekkanattu P, Jiang G, Luo Y, Kingsbury P, Xu Z, Rasmussen L, et al.
AMIA Annu Symp Proc
. 2020 Apr;
2019:190-199.
PMID: 32308812
While natural language processing (NLP) of unstructured clinical narratives holds the potential for patient care and clinical research, portability of NLP approaches across multiple sites remains a major challenge. This...
7.
Xu Z, Chou J, Zhang X, Luo Y, Isakova T, Adekkanattu P, et al.
J Biomed Inform
. 2020 Jan;
102:103361.
PMID: 31911172
Acute Kidney Injury (AKI) is a common clinical syndrome characterized by the rapid loss of kidney excretory function, which aggravates the clinical severity of other diseases in a large number...
8.
Xu Z, Luo Y, Adekkanattu P, Ancker J, Jiang G, Kiefer R, et al.
Stud Health Technol Inform
. 2019 Aug;
264:462-466.
PMID: 31437966
Acute Kidney Injury (AKI) is the most common cause of organ dysfunction in critically ill adults and prior studies have shown AKI is associated with a significant increase of the...
9.
Xu Z, Feng Y, Li Y, Srivastava A, Adekkanattu P, Ancker J, et al.
AMIA Jt Summits Transl Sci Proc
. 2019 Jul;
2019:809-818.
PMID: 31259038
Acute Kidney Injury (AKI) in critical care is often a quickly-evolving clinical event with high morbidity and mortality. Early prediction of AKI risk in critical care setting can facilitate early...
10.
Pacheco J, Rasmussen L, Kiefer R, Campion T, Speltz P, Carroll R, et al.
J Am Med Inform Assoc
. 2018 Aug;
25(11):1540-1546.
PMID: 30124903
Electronic health record (EHR) algorithms for defining patient cohorts are commonly shared as free-text descriptions that require human intervention both to interpret and implement. We developed the Phenotype Execution and...