Mining electronic health records: towards better research applications and clinical care

By Peter Bjødstrup Jensen, Lars Juhl Jensen, Søren Brunak. 2012

1,637 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Health Information Management, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Health Information Management

In Molecular Biology

Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, background): “EHRs are designed chiefly for clinical, administrative and financial purposes, such as patient care, insurance and billing [97].”
Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records (2016, background): “EHR data is challenging to represent and model due to its high dimensionality, noise, heterogeneity, sparseness, incompleteness, random errors, and systematic biases 7,10,11 .”
Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review (2019, methodology): “Methods based in analysis of EHRs [2] are resulting in improved understanding of patient clinical trajectories [3] while enabling better patient stratification and risk prediction [4-6].”
Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives (2018, background): “These data sets can be explored and mined in numerous ways [11].”
Using Electronic Health Records for Population Health Research: A Review of Methods and Applications (2015, methodology): “The convergence of machine learning tools and big data methods is motivating development of predictive models that can readily use diverse, high-volume EHR data to guide decision making for individual patients (56).”
Identification of Adverse Drug Events from Free Text Electronic Patient Records and Information in a Large Mental Health Case Register (2015, background): “Despite these challenges, a number of studies have made use of the rich data in EHRs to mine details relating to adverse drug events (ADEs) for example [1].”
Unobtrusive Sensing and Wearable Devices for Health Informatics (2014, methodology): “data in a computer-readable form, which can be used for clinical decision support [146].”
$\mathtt {Deepr}$: A Convolutional Net for Medical Records (2016, methodology): “t of irregular data. Existing EMR-driven predictive work often relies on highdimensional sparse feature representation, where features are engineered to capture certain regularities of the data [11], [20] This feature engineering practice is effort intensive and nonadaptive to varying medical record”
DISEASES: Text mining and data integration of disease–gene associations (2014, background): “int/classifications/icd/) are almost ubiquitously used for coding diagnoses in electronic health records (EHRs) and derived health registries [10].”
Human symptoms–disease network (2014, background): “In addition, a large-scale data integration aiming at comprehensive disease and population coverage will also meet difﬁculties pertaining to privacy issues and semantic interoperability across institutions or countries 56 .”