[HTML][HTML] Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review
Objectives Patient representation learning refers to learning a dense mathematical
representation of a patient that encodes meaningful information from Electronic Health …
representation of a patient that encodes meaningful information from Electronic Health …
Data-driven personal thermal comfort prediction: A literature review
Personal thermal comfort prediction modeling has become a trending topic in efforts to
improve individual indoor comfort, a notion that is closely related to the design and …
improve individual indoor comfort, a notion that is closely related to the design and …
The National COVID Cohort Collaborative (N3C): rationale, design, infrastructure, and deployment
Abstract Objective Coronavirus disease 2019 (COVID-19) poses societal challenges that
require expeditious data and knowledge sharing. Though organizational clinical data are …
require expeditious data and knowledge sharing. Though organizational clinical data are …
Bindaas: Blockchain-based deep-learning as-a-service in healthcare 4.0 applications
P Bhattacharya, S Tanwar, U Bodkhe… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
Electronic Health Records (EHRs) allows patients to control, share, and manage their health
records among family members, friends, and healthcare service providers using an open …
records among family members, friends, and healthcare service providers using an open …
Hitanet: Hierarchical time-aware attention networks for risk prediction on electronic health records
Deep learning methods especially recurrent neural network based models have
demonstrated early success in disease risk prediction on longitudinal patient data. Existing …
demonstrated early success in disease risk prediction on longitudinal patient data. Existing …
Kame: Knowledge-based attention model for diagnosis prediction in healthcare
The goal of diagnosis prediction task is to predict the future health information of patients
from their historical Electronic Healthcare Records (EHR). The most important and …
from their historical Electronic Healthcare Records (EHR). The most important and …
Discovering symptom patterns of COVID-19 patients using association rule mining
Background The COVID-19 pandemic is a significant public health crisis that is hitting hard
on people's health, well-being, and freedom of movement, and affecting the global economy …
on people's health, well-being, and freedom of movement, and affecting the global economy …
Deep representation learning of electronic health records to unlock patient stratification at scale
Deriving disease subtypes from electronic health records (EHRs) can guide next-generation
personalized medicine. However, challenges in summarizing and representing patient data …
personalized medicine. However, challenges in summarizing and representing patient data …
[Retracted] Blockchain‐Based Deep Learning to Process IoT Data Acquisition in Cognitive Data
S Hannah, AJ Deepa, VS Chooralil… - BioMed Research …, 2022 - Wiley Online Library
Remote health monitoring can help prevent disease at the earlier stages. The Internet of
Things (IoT) concepts have recently advanced, enabling omnipresent monitoring. Easily …
Things (IoT) concepts have recently advanced, enabling omnipresent monitoring. Easily …
[HTML][HTML] Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies
Objective Temporal electronic health records (EHRs) contain a wealth of information for
secondary uses, such as clinical events prediction and chronic disease management …
secondary uses, such as clinical events prediction and chronic disease management …