Neural natural language processing for unstructured data in electronic health records: a review
Electronic health records (EHRs), digital collections of patient healthcare events and
observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and …
observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and …
Advances in electronic phenotyping: from rule-based definitions to machine learning models
JM Banda, M Seneviratne… - Annual review of …, 2018 - annualreviews.org
With the widespread adoption of electronic health records (EHRs), large repositories of
structured and unstructured patient data are becoming available to conduct observational …
structured and unstructured patient data are becoming available to conduct observational …
How data science and AI-based technologies impact genomics
J Lin, KY Ngiam - Singapore medical journal, 2023 - journals.lww.com
Advancements in high-throughput sequencing have yielded vast amounts of genomic data,
which are studied using genome-wide association study (GWAS)/phenome-wide …
which are studied using genome-wide association study (GWAS)/phenome-wide …
[HTML][HTML] MixEHR-Guided: A guided multi-modal topic modeling approach for large-scale automatic phenotyping using the electronic health record
Abstract Electronic Health Records (EHRs) contain rich clinical data collected at the point of
the care, and their increasing adoption offers exciting opportunities for clinical informatics …
the care, and their increasing adoption offers exciting opportunities for clinical informatics …
Learning endometriosis phenotypes from patient-generated data
Endometriosis is a systemic and chronic condition in women of childbearing age, yet a
highly enigmatic disease with unresolved questions: there are no known biomarkers, nor …
highly enigmatic disease with unresolved questions: there are no known biomarkers, nor …
Phe2vec: Automated disease phenotyping based on unsupervised embeddings from electronic health records
Robust phenotyping of patients from electronic health records (EHRs) at scale is a challenge
in clinical informatics. Here, we introduce Phe2vec, an automated framework for disease …
in clinical informatics. Here, we introduce Phe2vec, an automated framework for disease …
Taste: temporal and static tensor factorization for phenotyping electronic health records
Phenotyping electronic health records (EHR) focuses on defining meaningful patient groups
(eg, heart failure group and diabetes group) and identifying the temporal evolution of …
(eg, heart failure group and diabetes group) and identifying the temporal evolution of …
Distributed tensor decomposition for large scale health analytics
In the past few decades, there has been rapid growth in quantity and variety of healthcare
data. These large sets of data are usually high dimensional (eg patients, their diagnoses …
data. These large sets of data are usually high dimensional (eg patients, their diagnoses …
A decision support system in precision medicine: contrastive multimodal learning for patient stratification
Precision medicine aims to provide personalized healthcare for patients by stratifying them
into subgroups based on their health conditions, enabling the development of tailored …
into subgroups based on their health conditions, enabling the development of tailored …
Learning phenotypes and dynamic patient representations via RNN regularized collective non-negative tensor factorization
Abstract Non-negative Tensor Factorization (NTF) has been shown effective to discover
clinically relevant and interpretable phenotypes from Electronic Health Records (EHR) …
clinically relevant and interpretable phenotypes from Electronic Health Records (EHR) …