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Machine learning approaches for electronic health records phenoty**: a methodical review
S Yang, P Varghese, E Stephenson… - Journal of the …, 2023 - academic.oup.com
Objective Accurate and rapid phenoty** is a prerequisite to leveraging electronic health
records for biomedical research. While early phenoty** relied on rule-based algorithms …
records for biomedical research. While early phenoty** relied on rule-based algorithms …
[HTML][HTML] Generate analysis-ready data for real-world evidence: tutorial for harnessing electronic health records with advanced informatic technologies
Although randomized controlled trials (RCTs) are the gold standard for establishing the
efficacy and safety of a medical treatment, real-world evidence (RWE) generated from real …
efficacy and safety of a medical treatment, real-world evidence (RWE) generated from real …
[HTML][HTML] MixEHR-Guided: A guided multi-modal topic modeling approach for large-scale automatic phenoty** 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 …
Large language models with retrieval-augmented generation for zero-shot disease phenoty**
WE Thompson, DM Vidmar, JK De Freitas… - ar** based on unsupervised embeddings from electronic health records
Robust phenoty** 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 …
Arch: Large-scale knowledge graph via aggregated narrative codified health records analysis
Objective: Electronic health record (EHR) systems contain a wealth of clinical data stored as
both codified data and free-text narrative notes (NLP). The complexity of EHR presents …
both codified data and free-text narrative notes (NLP). The complexity of EHR presents …
Modeling electronic health record data using an end-to-end knowledge-graph-informed topic model
The rapid growth of electronic health record (EHR) datasets opens up promising
opportunities to understand human diseases in a systematic way. However, effective …
opportunities to understand human diseases in a systematic way. However, effective …
Automatic phenoty** by a seed-guided topic model
Electronic health records (EHRs) provide rich clinical information and the opportunities to
extract epidemiological patterns to understand and predict patient disease risks with suitable …
extract epidemiological patterns to understand and predict patient disease risks with suitable …
GTM-decon: guided-topic modeling of single-cell transcriptomes enables sub-cell-type and disease-subtype deconvolution of bulk transcriptomes
LS Swapna, M Huang, Y Li - Genome Biology, 2023 - Springer
Cell-type composition is an important indicator of health. We present Guided Topic Model for
deconvolution (GTM-decon) to automatically infer cell-type-specific gene topic distributions …
deconvolution (GTM-decon) to automatically infer cell-type-specific gene topic distributions …
Classifying pseudogout using machine learning approaches with electronic health record data
Objective Identifying pseudogout in large data sets is difficult due to its episodic nature and a
lack of billing codes specific to this acute subtype of calcium pyrophosphate (CPP) …
lack of billing codes specific to this acute subtype of calcium pyrophosphate (CPP) …