Integrating multi-omics data with EHR for precision medicine using advanced artificial intelligence
With the recent advancement of novel biomedical technologies such as high-throughput
sequencing and wearable devices, multi-modal biomedical data ranging from multi-omics …
sequencing and wearable devices, multi-modal biomedical data ranging from multi-omics …
Missing data matter: an empirical evaluation of the impacts of missing EHR data in comparative effectiveness research
Objectives The impacts of missing data in comparative effectiveness research (CER) using
electronic health records (EHRs) may vary depending on the type and pattern of missing …
electronic health records (EHRs) may vary depending on the type and pattern of missing …
A novel method leveraging time series data to improve subphenotyping and application in critically ill patients with COVID-19
Computational subphenotyping, a data-driven approach to understanding disease subtypes,
is a prominent topic in medical research. Numerous ongoing studies are dedicated to …
is a prominent topic in medical research. Numerous ongoing studies are dedicated to …
[HTML][HTML] MixEHR-SurG: a joint proportional hazard and guided topic model for inferring mortality-associated topics from electronic health records
Survival models can help medical practitioners to evaluate the prognostic importance of
clinical variables to patient outcomes such as mortality or hospital readmission and …
clinical variables to patient outcomes such as mortality or hospital readmission and …
Using sequence clustering to identify clinically relevant subphenotypes in patients with COVID-19 admitted to the intensive care unit
Objective The novel coronavirus disease 2019 (COVID-19) has heterogenous clinical
courses, indicating that there might be distinct subphenotypes in critically ill patients …
courses, indicating that there might be distinct subphenotypes in critically ill patients …
Enhancing patient representation learning from electronic health records through predicted family relations
X Huang, J Arora, AM Erzurumluoglu, D Lam… - medRxiv, 2024 - medrxiv.org
Artificial intelligence and machine learning are powerful tools in analyzing electronic health
records (EHRs) for healthcare research. Despite the recognized importance of family health …
records (EHRs) for healthcare research. Despite the recognized importance of family health …
[HTML][HTML] DETECT: Feature extraction method for disease trajectory modeling in electronic health records
Modeling with longitudinal electronic health record (EHR) data proves challenging given the
high dimensionality, redundancy, and noise captured in EHR. In order to improve precision …
high dimensionality, redundancy, and noise captured in EHR. In order to improve precision …
DETECT: Feature extraction method for disease trajectory modeling
P Singhal, L Guare, C Morse, M Byrska-Bishop… - medRxiv, 2022 - medrxiv.org
Modeling with longitudinal electronic health record (EHR) data proves challenging given the
high dimensionality, redundancy, and noise captured in EHR. In order to improve precision …
high dimensionality, redundancy, and noise captured in EHR. In order to improve precision …