Peggy Peissig
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Explore the profile of Peggy Peissig including associated specialties, affiliations and a list of published articles.
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51
Citations
910
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Recent Articles
1.
Hall M, Wallace J, Lucas A, Bradford Y, Verma S, Muller-Myhsok B, et al.
PLoS Genet
. 2021 Jun;
17(6):e1009534.
PMID: 34086673
Assumptions are made about the genetic model of single nucleotide polymorphisms (SNPs) when choosing a traditional genetic encoding: additive, dominant, and recessive. Furthermore, SNPs across the genome are unlikely to...
2.
Zhang W, Peissig P, Kuang Z, Page D
Proc ACM Conf Health Inference Learn (2020)
. 2020 Dec;
2020:30-39.
PMID: 33283213
Adverse drug reactions (ADRs) are detrimental and unexpected clinical incidents caused by drug intake. The increasing availability of massive quantities of longitudinal event data such as electronic health records (EHRs)...
3.
Leary E, Brilliant M, Peissig P, Griesbach S
Am J Health Syst Pharm
. 2019 Aug;
76(6):387-397.
PMID: 31415684
Purpose: As a preliminary evaluation of the outcomes of implementing pharmacogenetic testing within a large rural healthcare system, patients who received pre-emptive pharmacogenetic testing and warfarin dosing were monitored until...
4.
Geng S, Kuang Z, Peissig P, Page D
Proc Mach Learn Res
. 2019 Jul;
80:1714-1723.
PMID: 31355361
We propose temporal Poisson square root graphical models (TPSQRs), a generalization of Poisson square root graphical models (PSQRs) specifically designed for modeling longitudinal event data. By estimating the temporal relationships...
5.
Taylor C, Lemke K, Richards T, Roe K, He T, Arruda-Olson A, et al.
AMIA Jt Summits Transl Sci Proc
. 2019 Jul;
2019:145-152.
PMID: 31258966
Electronic health records (EHR) are valuable to define phenotype selection algorithms used to identify cohorts ofpatients for sequencing or genome wide association studies (GWAS). To date, the electronic medical records...
6.
Badger J, Larose E, Mayer J, Bashiri F, Page D, Peissig P
J Biomed Inform
. 2019 Apr;
94:103185.
PMID: 31028874
Objective: To develop machine learning models for classifying the severity of opioid overdose events from clinical data. Materials And Methods: Opioid overdoses were identified by diagnoses codes from the Marshfield...
7.
Feld S, Woo K, Alexandridis R, Wu Y, Liu J, Peissig P, et al.
AMIA Annu Symp Proc
. 2019 Mar;
2018:1253-1262.
PMID: 30815167
The predictive capability of combining demographic risk factors, germline genetic variants, and mammogram abnormality features for breast cancer risk prediction is poorly understood. We evaluated the predictive performance of combinations...
8.
Kuang Z, Bao Y, Thomson J, Caldwell M, Peissig P, Stewart R, et al.
Methods Mol Biol
. 2018 Dec;
1903:255-267.
PMID: 30547447
We present the baseline regularization model for computational drug repurposing using electronic health records (EHRs). In EHRs, drug prescriptions of various drugs are recorded throughout time for various patients. In...
9.
Bashiri F, Larose E, Peissig P, Tafti A
Data Brief
. 2018 Jun;
17:71-75.
PMID: 29876376
A fully-labeled image dataset provides a unique resource for reproducible research inquiries and data analyses in several computational fields, such as computer vision, machine learning and deep learning machine intelligence....
10.
McCarty C, Peissig P, Caldwell M, Wilke R
Per Med
. 2018 May;
5(5):529-542.
PMID: 29783440
The Marshfield Clinic Personalized Medicine Research Project is the largest population-based biobank in the USA, with the ability to recontact subjects to obtain additional information to facilitate gene-environment studies. Nearly...