scholarly journals Lossless integration of multiple electronic health records for identifying pleiotropy using summary statistics

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ruowang Li ◽  
Rui Duan ◽  
Xinyuan Zhang ◽  
Thomas Lumley ◽  
Sarah Pendergrass ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Genetic Data ◽  
Test Statistics ◽  
Health Records ◽  
Privacy Concerns ◽  
Individual Level ◽  
Multiple Datasets ◽  
Level Data ◽  
Electronic Health ◽  
Identical Test

AbstractIncreasingly, clinical phenotypes with matched genetic data from bio-bank linked electronic health records (EHRs) have been used for pleiotropy analyses. Thus far, pleiotropy analysis using individual-level EHR data has been limited to data from one site. However, it is desirable to integrate EHR data from multiple sites to improve the detection power and generalizability of the results. Due to privacy concerns, individual-level patients’ data are not easily shared across institutions. As a result, we introduce Sum-Share, a method designed to efficiently integrate EHR and genetic data from multiple sites to perform pleiotropy analysis. Sum-Share requires only summary-level data and one round of communication from each site, yet it produces identical test statistics compared with that of pooled individual-level data. Consequently, Sum-Share can achieve lossless integration of multiple datasets. Using real EHR data from eMERGE, Sum-Share is able to identify 1734 potential pleiotropic SNPs for five cardiovascular diseases.

scholarly journals Leveraging Genetic Reports and Electronic Health Records for Predicting Primary Cancers Based on FHIR and RDF (Preprint)

2020 ◽  
Author(s):  
Nansu Zong ◽  
Victoria Ngo ◽  
Daniel J. Stone ◽  
Andrew Wen ◽  
Yiqing Zhao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Health Records ◽  
Cancer Patients ◽  
Genetic Data ◽  
Precision Oncology ◽  
Primary Cancer ◽  
Health Records ◽  
Web Resource ◽  
Cancer Types ◽  
Electronic Health

BACKGROUND Precision oncology has the potential to leverage clinical and genomic data in advancing disease prevention, diagnose, and treatments. A key research area focuses on early detection of primary cancers and the potential prediction of cancers of unknown primary in order to facilitate optimal treatment decisions. OBJECTIVE This study presents a methodology to harmonize phenotypic and genetic data features to classify primary cancer types and predict unknown primaries. METHODS We extracted the genetic data elements from a collection of oncology genetic reports of 1,011 cancer patients, and corresponding phenotypical data from the Mayo Clinic electronic health records (EHRs). We modeled both genetic and EHR data with HL7 Fast Healthcare Interoperability Resources (FHIR). The semantic web Resource Description Framework (RDF) was employed to generate the network-based data representation (i.e., patient-phenotypic-genetic network). Based on RDF data graph, graph embedding algorithm Node2vec was applied to generate features, and then multiple machine learning and deep learning backbone models were adopted for cancer prediction. RESULTS With six machine-learning tasks designed in the experiment, we demonstrated the proposed method achieved favorable results in classifying primary cancer types and predicting unknown primaries. To demonstrate the interpretability, phenotypic and genetic features that contributed the most to the prediction of each cancer were identified and validated based on a literature review. CONCLUSIONS Accurate prediction of cancer types can be achieved with existing EHR data with satisfactory precision. The integration of genetic reports improves prediction, illustrating the translational values of incorporating genetic tests early at the diagnose stage for cancer patients.

Privacy-Friendly Management of Electronic Health Records in the eHealth Context

2015 ◽  
pp. 251-264
Author(s):  
Milica Milutinovic ◽  
Bart De Decker
Keyword(s):  
Electronic Health Records ◽  
Large Scale ◽  
Patient Data ◽  
Health Records ◽  
Privacy Concerns ◽  
Ubiquitous Technology ◽  
Emergency Situations ◽  
User Centric ◽  
Electronic Health

Electronic Health Records (EHRs) are becoming the ubiquitous technology for managing patients' records in many countries. They allow for easier transfer and analysis of patient data on a large scale. However, privacy concerns linked to this technology are emerging. Namely, patients rarely fully understand how EHRs are managed. Additionally, the records are not necessarily stored within the organization where the patient is receiving her healthcare. This service may be delegated to a remote provider, and it is not always clear which health-provisioning entities have access to this data. Therefore, in this chapter the authors propose an alternative where users can keep and manage their records in their existing eHealth systems. The approach is user-centric and enables the patients to have better control over their data while still allowing for special measures to be taken in case of emergency situations with the goal of providing the required care to the patient.

scholarly journals Linking electronic health records with community-level data to understand childhood obesity risk

2015 ◽  
Vol 10 (6) ◽  
pp. 436-441 ◽  
Author(s):  
E. J. Tomayko ◽  
T. L. Flood ◽  
A. Tandias ◽  
L. P. Hanrahan
Keyword(s):  
Childhood Obesity ◽  
Electronic Health Records ◽  
Community Level ◽  
Obesity Risk ◽  
Health Records ◽  
Level Data ◽  
Electronic Health

scholarly journals Security and Privacy Concerns Regarding Genetic Data in Mobile Health Record Systems: An Empirical Study from Turkey

2019 ◽  
Author(s):  
Özlem Özkan ◽  
Yeşim Aydin Son ◽  
Arsev Umur Aydinoğlu
Keyword(s):  
Genetic Testing ◽  
Mobile Health ◽  
Genetic Data ◽  
Security And Privacy ◽  
Health Record ◽  
Privacy And Security ◽  
Negative Experience ◽  
Health Records ◽  
Privacy Concerns ◽  
Electronic Health

AbstractWith the increasing use of genetic testing and applications of bioinformatics in healthcare, genetic and genomic data needs to be integrated into electronic health systems. We administered a descriptive survey to 174 participants to elicit their views on the privacy and security of mobile health record systems and inclusion of their genetic data in these systems. A survey was implemented online and on site in two genetic diagnostic centres. Nearly half of the participants or their close family members had undergone genetic testing. Doctors constituted the only profession group that people trusted for the privacy of their health and genetic data; however, people chose to limit even their doctor’s access to their genetic/health records. The majority of the respondents preferred to keep full access for themselves. Several participants had negative experience or preconceptions about electronic health records: the medical reports of 9.7% of the respondents had been used or released without their consent, 15.1% stated that they avoided being tested due to violation risks, and 3.5% asked their doctors to enter a less embarrassing health status in their records. The participants wanted to see some regulations and security measurements before using any system for their health/genetic data. In addition, significantly more participants stating that storing genetic data in a mobile system was riskier compared to other health data. Furthermore, the comparative analysis revealed that being young, being a woman and having higher education were associated with having greater privacy concerns.

Privacy-Friendly Management of Electronic Health Records in the eHealth Context

2017 ◽  
pp. 528-542
Author(s):  
Milica Milutinovic ◽  
Bart De Decker
Keyword(s):  
Electronic Health Records ◽  
Large Scale ◽  
Patient Data ◽  
Health Records ◽  
Privacy Concerns ◽  
Ubiquitous Technology ◽  
Emergency Situations ◽  
User Centric ◽  
Electronic Health

Electronic Health Records (EHRs) are becoming the ubiquitous technology for managing patients' records in many countries. They allow for easier transfer and analysis of patient data on a large scale. However, privacy concerns linked to this technology are emerging. Namely, patients rarely fully understand how EHRs are managed. Additionally, the records are not necessarily stored within the organization where the patient is receiving her healthcare. This service may be delegated to a remote provider, and it is not always clear which health-provisioning entities have access to this data. Therefore, in this chapter the authors propose an alternative where users can keep and manage their records in their existing eHealth systems. The approach is user-centric and enables the patients to have better control over their data while still allowing for special measures to be taken in case of emergency situations with the goal of providing the required care to the patient.

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