Clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods
By R. Richesson, Jimeng Sun, Jyotishman Pathak, Abel N. Kho, Joshua Charles Denny. 2016
96 citations recorded by the source corpus.
Objective The combination of phenomic data from electronic health records (EHR) and clinical data repositories with dense biological data has enabled genomic and pharmacogenomic discovery, a first step toward precision medicine. Computational methods for the identification of clinical phenotypes from EHR data will advance our understanding of disease risk and drug response, and support the practice of precision medicine on a national scale. Methods Based on our experience within three national research networks, we summarize the broad approaches to clinical phenotyping and highlight the important role of these networks in the progression of high-throughput phenotyping and precision medicine. We provide supporting literature in the form of a non-systematic review. Results The practice of clinical phenotyping is evolving to meet the growing demand for scalable, portable, and data driven methods and tools. The resources required for traditional phenotyping algorithms from expert defined rules are significant. In contrast, machine learning approaches that rely on data patterns will require fewer clinical domain experts and resources. Conclusions Machine learning approaches that generate phenotype definitions from patient features and clinical profiles will result in truly computational phenotypes, derived from data rather than experts. Research networks and phenotype developers should cooperate to develop methods, collaboration platforms, and data standards that will enable computational phenotyping and truly modernize biomedical research and precision medicine.
Subfields: Artificial Intelligence, Genetics, Pharmacology
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
- Jimeng Sun
- Walter F. Stewart
- Xiaoqian Jiang
- R. Vuduc
- Joyce Ho
- Jian Lou
- Jing Ma
- Evangelos E. Papalexakis
In Genetics
In Pharmacology
Citations in the corpus
- Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis (2019, background): “Phenotyping can be used to assist precision medicine, speedup biomedical discovery, and improve healthcare quality [25, 29].”
- SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping (2018, background): “Phenotyping is intended to distinguish the latent structure among features that can, in turn, be used to prevent disease subtypes and improve treatment development and management [43].”
- Artificial intelligence (AI) and big data in cancer and precision oncology (2020, background): “These ML algorithms are developed to identify useful insights to distinguish and classify genotypes based on patterns [22].”