Mining for equitable health: Assessing the impact of missing data in electronic health records
Electronic health records (EHR) are collected as a routine part of healthcare delivery, and
have great potential to be utilized to improve patient health outcomes. They contain multiple …
have great potential to be utilized to improve patient health outcomes. They contain multiple …
Ehrmamba: Towards generalizable and scalable foundation models for electronic health records
Transformers have significantly advanced the modeling of Electronic Health Records (EHR),
yet their deployment in real-world healthcare is limited by several key challenges. Firstly, the …
yet their deployment in real-world healthcare is limited by several key challenges. Firstly, the …
Duka: A dual-keyless-attention model for multi-modality EHR data fusion and organ failure prediction
Objective: Organ failure is a leading cause of mortality in hospitals, particularly in intensive
care units. Predicting organ failure is crucial for clinical and social reasons. This study …
care units. Predicting organ failure is crucial for clinical and social reasons. This study …
[HTML][HTML] DOME: Directional medical embedding vectors from Electronic Health Records
Motivation: The increasing availability of Electronic Health Record (EHR) systems has
created enormous potential for translational research. Recent developments in …
created enormous potential for translational research. Recent developments in …
A comparison of representation learning methods for medical concepts in MIMIC-IV
Objective To compare and release the diagnosis (ICD-10-CM), procedure (ICD-10-PCS),
and medication (NDC) concept (code) embeddings trained by Latent Dirichlet Allocation …
and medication (NDC) concept (code) embeddings trained by Latent Dirichlet Allocation …
Deep Learning for Detecting Entailment Between Requirements Using Semantics from Use Case Diagrams as Training Data: A Comparative Study
DB Firmawan, D Siahaan… - 2024 International Seminar …, 2024 - ieeexplore.ieee.org
Textual entailment, also known as natural language inference, is a branch of natural
language processing (NLP) that examines the semantics and meaning of phrases and text …
language processing (NLP) that examines the semantics and meaning of phrases and text …
Utilizing Sequential Information of General Lab-test Results and Diagnoses History for Differential Diagnosis of Dementia
Y **ng, DP Pratama, Y Wang, Y Zhang… - arxiv preprint arxiv …, 2025 - arxiv.org
Early diagnosis of Alzheimer's Disease (AD) faces multiple data-related challenges,
including high variability in patient data, limited access to specialized diagnostic tests, and …
including high variability in patient data, limited access to specialized diagnostic tests, and …
Cellular Automaton-Based Sentiment Analysis Using Deep Learning Methods.
The swift expansion of internet-centric applications, including social media platforms, online
marketplaces and blogs, has given rise to comments, experiences, sentiments, evaluations …
marketplaces and blogs, has given rise to comments, experiences, sentiments, evaluations …
Mining for Health: Advancing trustworthy statistical and machine learning methods for complex electronic health records data
E Getzen - 2024 - search.proquest.com
Electronic health records (EHRs) consist of data that are collected each time a patient
interacts with the healthcare system. These data may consist of structured data such as labs …
interacts with the healthcare system. These data may consist of structured data such as labs …
Quaternion Generative Adversarial Networks for loT based Chronic Kidney Disease Detection using Greylag Goose Optimization Algorithm
N Shaik, A Chaudhary, S Abbineni… - 2024 4th …, 2024 - ieeexplore.ieee.org
Chronic disease detection using loT devices involves leveraging secure, decentralized
networks to collect, store, and share patient health data. Ensures data integrity, security, and …
networks to collect, store, and share patient health data. Ensures data integrity, security, and …