The digital revolution in phenotyping

By A. Oellrich, Nigel Collier, T. Groza, Dietrich Rebholz-Schuhmann, Nigam H. Shah, Olivier Bodenreider, Mary Regina Boland, Ivo I. Georgiev, Hongfang Liu, K. Livingston and 9 more. 2015

59 citations recorded by the source corpus.

Phenotypes have gained increased notoriety in the clinical and biological domain owing to their application in numerous areas such as the discovery of disease genes and drug targets, phylogenetics and pharmacogenomics. Phenotypes, defined as observable characteristics of organisms, can be seen as one of the bridges that lead to a translation of experimental findings into clinical applications and thereby support ‘bench to bedside’ efforts. However, to build this translational bridge, a common and universal understanding of phenotypes is required that goes beyond domain-specific definitions. To achieve this ambitious goal, a digital revolution is ongoing that enables the encoding of data in computer-readable formats and the data storage in specialized repositories, ready for integration, enabling translational research. While phenome research is an ongoing endeavor, the true potential hidden in the currently available data still needs to be unlocked, offering exciting opportunities for the forthcoming years. Here, we provide insights into the state-of-the-art in digital phenotyping, by means of representing, acquiring and analyzing phenotype data. In addition, we provide visions of this field for future research work that could enable better applications of phenotype data.

Subfields: Biophysics, Molecular Biology

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Citations in the corpus

Distilling Knowledge from Deep Networks with Applications to Healthcare Domain (2015, background): “Recently, there is a growing interest in applying deep learning techniques to computational phenotyping [33] due to the increasing availability of the Electronic Healthcare Records (EHR) and the need for Personalized Healthcare [4, 17].”
Learning to Diagnose with LSTM Recurrent Neural Networks (2015, background): “In this work, we are interested in recognizing diagnoses and, more broadly, the observable physiologic characteristics of patients, a task generally termed phenotyping (Oellrich et al., 2015).”
Directly Modeling Missing Data in Sequences with RNNs: Improved Classification of Clinical Time Series (2016, background): “Our work addresses the task of phenotyping (Oellrich et al., 2015), that is, recognizing diseases and other relevant clinical conditions, posed as a sequence classiﬁcation task.”
Multitask learning and benchmarking with clinical time series data (2017, methodology): “nt in a given patient record3. Phenotyping has applications in cohort construction for clinical studies, comorbidity detection and risk adjustment, quality improvement and surveillance, and diagnosis [69]. Traditional research phenotypes are identiﬁed via chart review based on criteria predeﬁned by ”
SCEHR: Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health Records (2021, background): “The phenotyping is key for diagnosis, comorbidity detection, and quality surveillance [34].”