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Article ; Online: Polygenic risk alters the penetrance of monogenic kidney disease

Atlas Khan / Ning Shang / Jordan G. Nestor / Chunhua Weng / George Hripcsak / Peter C. Harris / Ali G. Gharavi / Krzysztof Kiryluk

Nature Communications, Vol 14, Iss 1, Pp 1-

2023 Volume 10

Abstract: Abstract Chronic kidney disease (CKD) is determined by an interplay of monogenic, polygenic, and environmental risks. Autosomal dominant polycystic kidney disease (ADPKD) and COL4A-associated nephropathy (COL4A-AN) represent the most common forms of ... ...

Abstract	Abstract Chronic kidney disease (CKD) is determined by an interplay of monogenic, polygenic, and environmental risks. Autosomal dominant polycystic kidney disease (ADPKD) and COL4A-associated nephropathy (COL4A-AN) represent the most common forms of monogenic kidney diseases. These disorders have incomplete penetrance and variable expressivity, and we hypothesize that polygenic factors explain some of this variability. By combining SNP array, exome/genome sequence, and electronic health record data from the UK Biobank and All-of-Us cohorts, we demonstrate that the genome-wide polygenic score (GPS) significantly predicts CKD among ADPKD monogenic variant carriers. Compared to the middle tertile of the GPS for noncarriers, ADPKD variant carriers in the top tertile have a 54-fold increased risk of CKD, while ADPKD variant carriers in the bottom tertile have only a 3-fold increased risk of CKD. Similarly, the GPS significantly predicts CKD in COL4A-AN carriers. The carriers in the top tertile of the GPS have a 2.5-fold higher risk of CKD, while the risk for carriers in the bottom tertile is not different from the average population risk. These results suggest that accounting for polygenic risk improves risk stratification in monogenic kidney disease.
Keywords	Science ; Q
Subject code	610
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Correction

David J Albers / Matthew Levine / Bruce Gluckman / Henry Ginsberg / George Hripcsak / Lena Mamykina

PLoS Computational Biology, Vol 17, Iss 8, p e

Personalized glucose forecasting for type 2 diabetes using data assimilation.

2021 Volume 1009325

Abstract: This corrects the article DOI:10.1371/journal.pcbi.1005232.]. ...

Abstract	[This corrects the article DOI:10.1371/journal.pcbi.1005232.].
Keywords	Biology (General) ; QH301-705.5
Language	English
Publishing date	2021-08-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Outlier concepts auditing methodology for a large family of biomedical ontologies

Ling Zheng / Hua Min / Yan Chen / Vipina Keloth / James Geller / Yehoshua Perl / George Hripcsak

BMC Medical Informatics and Decision Making, Vol 20, Iss S10, Pp 1-

2020 Volume 15

Abstract: Abstract Background Summarization networks are compact summaries of ontologies. The “Big Picture” view offered by summarization networks enables to identify sets of concepts that are more likely to have errors than control concepts. For ontologies that ... ...

Abstract	Abstract Background Summarization networks are compact summaries of ontologies. The “Big Picture” view offered by summarization networks enables to identify sets of concepts that are more likely to have errors than control concepts. For ontologies that have outgoing lateral relationships, we have developed the "partial-area taxonomy" summarization network. Prior research has identified one kind of outlier concepts, concepts of small partials-areas within partial-area taxonomies. Previously we have shown that the small partial-area technique works successfully for four ontologies (or their hierarchies). Methods To improve the Quality Assurance (QA) scalability, a family-based QA framework, where one QA technique is potentially applicable to a whole family of ontologies with similar structural features, was developed. The 373 ontologies hosted at the NCBO BioPortal in 2015 were classified into a collection of families based on structural features. A meta-ontology represents this family collection, including one family of ontologies having outgoing lateral relationships. The process of updating the current meta-ontology is described. To conclude that one QA technique is applicable for at least half of the members for a family F, this technique should be demonstrated as successful for six out of six ontologies in F. We describe a hypothesis setting the condition required for a technique to be successful for a given ontology. The process of a study to demonstrate such success is described. This paper intends to prove the scalability of the small partial-area technique. Results We first updated the meta-ontology classifying 566 BioPortal ontologies. There were 371 ontologies in the family with outgoing lateral relationships. We demonstrated the success of the small partial-area technique for two ontology hierarchies which belong to this family, SNOMED CT’s Specimen hierarchy and NCIt’s Gene hierarchy. Together with the four previous ontologies from the same family, we fulfilled the “six out of six” condition required ...
Keywords	Biomedical ontologies ; Ontology quality assurance ; Auditing BioPortal ontologies ; Ontology auditing scalability ; Summarization network ; Ontology error concentration ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	006
Language	English
Publishing date	2020-12-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A deep database of medical abbreviations and acronyms for natural language processing

Lisa Grossman Liu / Raymond H. Grossman / Elliot G. Mitchell / Chunhua Weng / Karthik Natarajan / George Hripcsak / David K. Vawdrey

Scientific Data, Vol 8, Iss 1, Pp 1-

2021 Volume 9

Abstract: Measurement(s) Controlled Vocabulary • Linguistic Form Technology Type(s) digital curation • data combination Sample Characteristic - Location United States of America Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/ ...

Abstract	Measurement(s) Controlled Vocabulary • Linguistic Form Technology Type(s) digital curation • data combination Sample Characteristic - Location United States of America Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14068949
Keywords	Science ; Q
Language	English
Publishing date	2021-06-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A Data Quality Assessment Guideline for Electronic Health Record Data Reuse

Nicole G. Weiskopf / Suzanne Bakken / George Hripcsak / Chunhua Weng

eGEMs, Vol 5, Iss

2017 Volume 1

Abstract: Introduction: We describe the formulation, development, and initial expert review of 3x3 Data Quality Assessment (DQA), a dynamic, evidence-based guideline to enable electronic health record (EHR) data quality assessment and reporting for clinical ... ...

Abstract	Introduction: We describe the formulation, development, and initial expert review of 3x3 Data Quality Assessment (DQA), a dynamic, evidence-based guideline to enable electronic health record (EHR) data quality assessment and reporting for clinical research. Methods: 3x3 DQA was developed through the triangulation results from three studies: a review of the literature on EHR data quality assessment, a quantitative study of EHR data completeness, and a set of interviews with clinical researchers. Following initial development, the guideline was reviewed by a panel of EHR data quality experts. Results: The guideline embraces the task-dependent nature of data quality and data quality assessment. The core framework includes three constructs of data quality: complete, correct, and current data. These constructs are operationalized according to the three primary dimensions of EHR data: patients, variables, and time. Each of the nine operationalized constructs maps to a methodological recommendation for EHR data quality assessment. The initial expert response to the framework was positive, but improvements are required. Discussion: The initial version of 3x3 DQA promises to enable explicit guideline-based best practices for EHR data quality assessment and reporting. Future work will focus on increasing clarity on how and when 3x3 DQA should be used during the research process, improving the feasibility and ease-of-use of recommendation execution, and clarifying the process for users to determine which operationalized constructs and recommendations are relevant for a given dataset and study.
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Language	English
Publishing date	2017-09-01T00:00:00Z
Publisher	Ubiquity Press
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Uncovering key clinical trial features influencing recruitment

Betina Idnay / Yilu Fang / Alex Butler / Joyce Moran / Ziran Li / Junghwan Lee / Casey Ta / Cong Liu / Chi Yuan / Huanyao Chen / Edward Stanley / George Hripcsak / Elaine Larson / Karen Marder / Wendy Chung / Brenda Ruotolo / Chunhua Weng

Journal of Clinical and Translational Science, Vol

2023 Volume 7

Abstract: Abstract Background: Randomized clinical trials (RCT) are the foundation for medical advances, but participant recruitment remains a persistent barrier to their success. This retrospective data analysis aims to (1) identify clinical trial features ... ...

Abstract	Abstract Background: Randomized clinical trials (RCT) are the foundation for medical advances, but participant recruitment remains a persistent barrier to their success. This retrospective data analysis aims to (1) identify clinical trial features associated with successful participant recruitment measured by accrual percentage and (2) compare the characteristics of the RCTs by assessing the most and least successful recruitment, which are indicated by varying thresholds of accrual percentage such as ≥ 90% vs ≤ 10%, ≥ 80% vs ≤ 20%, and ≥ 70% vs ≤ 30%. Methods: Data from the internal research registry at Columbia University Irving Medical Center and Aggregated Analysis of ClinicalTrials.gov were collected for 393 randomized interventional treatment studies closed to further enrollment. We compared two regularized linear regression and six tree-based machine learning models for accrual percentage (i.e., reported accrual to date divided by the target accrual) prediction. The outperforming model and Tree SHapley Additive exPlanations were used for feature importance analysis for participant recruitment. The identified features were compared between the two subgroups. Results: CatBoost regressor outperformed the others. Key features positively associated with recruitment success, as measured by accrual percentage, include government funding and compensation. Meanwhile, cancer research and non-conventional recruitment methods (e.g., websites) are negatively associated with recruitment success. Statistically significant subgroup differences (corrected p-value < .05) were found in 15 of the top 30 most important features. Conclusion: This multi-source retrospective study highlighted key features influencing RCT participant recruitment, offering actionable steps for improvement, including flexible recruitment infrastructure and appropriate participant compensation.
Keywords	Clinical trials ; research recruitment ; machine learning ; SHAP ; informatics ; Medicine ; R
Subject code	333
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	Cambridge University Press
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Large-scale evidence generation and evaluation across a network of databases for type 2 diabetes mellitus (LEGEND-T2DM)

Rohan Khera / Yuan Lu / Harlan M Krumholz / Martijn J Schuemie / Ruijun Chen / George Hripcsak / Marc A Suchard / Patrick B Ryan / Anna Ostropolets

BMJ Open, Vol 12, Iss

a protocol for a series of multinational, real-world comparative cardiovascular effectiveness and safety studies

2022 Volume 6

Keywords	Medicine ; R
Language	English
Publishing date	2022-06-01T00:00:00Z
Publisher	BMJ Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Application of Epidemiological Geographic Information System

Jaehyeong Cho / Seng Chan You / Seongwon Lee / DongSu Park / Bumhee Park / George Hripcsak / Rae Woong Park

International Journal of Environmental Research and Public Health, Vol 17, Iss 7824, p

An Open-Source Spatial Analysis Tool Based on the OMOP Common Data Model

2020 Volume 7824

Abstract: Background: Spatial epidemiology is used to evaluate geographical variations and disparities in health outcomes; however, constructing geographic statistical models requires a labor-intensive process that limits the overall utility. We developed an open- ... ...

Abstract	Background: Spatial epidemiology is used to evaluate geographical variations and disparities in health outcomes; however, constructing geographic statistical models requires a labor-intensive process that limits the overall utility. We developed an open-source software for spatial epidemiological analysis and demonstrated its applicability and quality. Methods: Based on standardized geocode and observational health data, the Application of Epidemiological Geographic Information System (AEGIS) provides two spatial analysis methods: disease mapping and detecting clustered medical conditions and outcomes. The AEGIS assesses the geographical distribution of incidences and health outcomes in Korea and the United States, specifically incidence of cancers and their mortality rates, endemic malarial areas, and heart diseases (only the United States). Results: The AEGIS-generated spatial distribution of incident cancer in Korea was consistent with previous reports. The incidence of liver cancer in women with the highest Moran’s I (0.44; p < 0.001) was 17.4 (10.3–26.9). The malarial endemic cluster was identified in Paju-si, Korea ( p < 0.001). When the AEGIS was applied to the database of the United States, a heart disease cluster was appropriately identified ( p < 0.001). Conclusions: As an open-source, cross-country, spatial analytics solution, AEGIS may globally assess the differences in geographical distribution of health outcomes through the use of standardized geocode and observational health databases.
Keywords	spatial epidemiology ; disease clustering ; geographical information system ; Medicine ; R
Subject code	910
Language	English
Publishing date	2020-10-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Improving Detection of Arrhythmia Drug-Drug Interactions in Pharmacovigilance Data through the Implementation of Similarity-Based Modeling.

Santiago Vilar / Tal Lorberbaum / George Hripcsak / Nicholas P Tatonetti

PLoS ONE, Vol 10, Iss 6, p e

2015 Volume 0129974

Abstract: Identification of Drug-Drug Interactions (DDIs) is a significant challenge during drug development and clinical practice. DDIs are responsible for many adverse drug effects (ADEs), decreasing patient quality of life and causing higher care expenses. DDIs ...

Abstract	Identification of Drug-Drug Interactions (DDIs) is a significant challenge during drug development and clinical practice. DDIs are responsible for many adverse drug effects (ADEs), decreasing patient quality of life and causing higher care expenses. DDIs are not systematically evaluated in pre-clinical or clinical trials and so the FDA U. S. Food and Drug Administration relies on post-marketing surveillance to monitor patient safety. However, existing pharmacovigilance algorithms show poor performance for detecting DDIs exhibiting prohibitively high false positive rates. Alternatively, methods based on chemical structure and pharmacological similarity have shown promise in adverse drug event detection. We hypothesize that the use of chemical biology data in a post hoc analysis of pharmacovigilance results will significantly improve the detection of dangerous interactions. Our model integrates a reference standard of DDIs known to cause arrhythmias with drug similarity data. To compare similarity between drugs we used chemical structure (both 2D and 3D molecular structure), adverse drug side effects, chemogenomic targets, drug indication classes, and known drug-drug interactions. We evaluated the method on external reference standards. Our results showed an enhancement of sensitivity, specificity and precision in different top positions with the use of similarity measures to rank the candidates extracted from pharmacovigilance data. For the top 100 DDI candidates, similarity-based modeling yielded close to twofold precision enhancement compared to the proportional reporting ratio (PRR). Moreover, the method helps in the DDI decision making through the identification of the DDI in the reference standard that generated the candidate.
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2015-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Personalized glucose forecasting for type 2 diabetes using data assimilation.

David J Albers / Matthew Levine / Bruce Gluckman / Henry Ginsberg / George Hripcsak / Lena Mamykina

PLoS Computational Biology, Vol 13, Iss 4, p e

2017 Volume 1005232

Abstract: Type 2 diabetes leads to premature death and reduced quality of life for 8% of Americans. Nutrition management is critical to maintaining glycemic control, yet it is difficult to achieve due to the high individual differences in glycemic response to ... ...

Abstract	Type 2 diabetes leads to premature death and reduced quality of life for 8% of Americans. Nutrition management is critical to maintaining glycemic control, yet it is difficult to achieve due to the high individual differences in glycemic response to nutrition. Anticipating glycemic impact of different meals can be challenging not only for individuals with diabetes, but also for expert diabetes educators. Personalized computational models that can accurately forecast an impact of a given meal on an individual's blood glucose levels can serve as the engine for a new generation of decision support tools for individuals with diabetes. However, to be useful in practice, these computational engines need to generate accurate forecasts based on limited datasets consistent with typical self-monitoring practices of individuals with type 2 diabetes. This paper uses three forecasting machines: (i) data assimilation, a technique borrowed from atmospheric physics and engineering that uses Bayesian modeling to infuse data with human knowledge represented in a mechanistic model, to generate real-time, personalized, adaptable glucose forecasts; (ii) model averaging of data assimilation output; and (iii) dynamical Gaussian process model regression. The proposed data assimilation machine, the primary focus of the paper, uses a modified dual unscented Kalman filter to estimate states and parameters, personalizing the mechanistic models. Model selection is used to make a personalized model selection for the individual and their measurement characteristics. The data assimilation forecasts are empirically evaluated against actual postprandial glucose measurements captured by individuals with type 2 diabetes, and against predictions generated by experienced diabetes educators after reviewing a set of historical nutritional records and glucose measurements for the same individual. The evaluation suggests that the data assimilation forecasts compare well with specific glucose measurements and match or exceed in accuracy expert forecasts. ...
Keywords	Biology (General) ; QH301-705.5
Language	English
Publishing date	2017-04-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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