[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 …
Graph Artificial Intelligence in Medicine
In clinical artificial intelligence (AI), graph representation learning, mainly through graph
neural networks and graph transformer architectures, stands out for its capability to capture …
neural networks and graph transformer architectures, stands out for its capability to capture …
[HTML][HTML] Knowledge Graph Embeddings for ICU readmission prediction
Abstract Background Intensive Care Unit (ICU) readmissions represent both a health risk for
patients, with increased mortality rates and overall health deterioration, and a financial …
patients, with increased mortality rates and overall health deterioration, and a financial …
Multi-modal contrastive learning for healthcare data analytics
Electronic Health Record (EHR) is a digital version of patient's medical charts. EHR consists
of longitudinal multi-modal data including demographics, diagnosis, clinical notes and …
of longitudinal multi-modal data including demographics, diagnosis, clinical notes and …
Textual data augmentation for patient outcomes prediction
Deep learning models have demonstrated superior performance in various healthcare
applications. However, the major limitation of these deep models is usually the lack of high …
applications. However, the major limitation of these deep models is usually the lack of high …
On procrustes analysis in hyperbolic space
P Tabaghi, I Dokmanić - IEEE Signal Processing Letters, 2021 - ieeexplore.ieee.org
Congruent Procrustes analysis aims to find the best matching between two point sets
through rotation, reflection and translation. We formulate the Procrustes problem for …
through rotation, reflection and translation. We formulate the Procrustes problem for …
[图书][B] What's Missing from Machine Learning for Medicine? New Methods for Causal Effect Estimation and Representation Learning from EHR Data
DR Bellamy - 2023 - search.proquest.com
This thesis explores the applications of deep learning in clinical and epidemiologic data
analysis, focusing on neural networks for causal effect estimation and clinical risk prediction …
analysis, focusing on neural networks for causal effect estimation and clinical risk prediction …
Ontology Embedding: A Survey of Methods, Applications and Resources
Ontologies are widely used for representing domain knowledge and meta data, playing an
increasingly important role in Information Systems, the Semantic Web, Bioinformatics and …
increasingly important role in Information Systems, the Semantic Web, Bioinformatics and …
[HTML][HTML] EffiCare: better prognostic models via resource-efficient health embeddings
N Rethmeier, NO Serbetci, S Möller… - AMIA Annual …, 2020 - ncbi.nlm.nih.gov
Recent medical prognostic models adapted from high data-resource fields like language
processing have quickly grown in complexity and size. However, since medical data …
processing have quickly grown in complexity and size. However, since medical data …
[HTML][HTML] 基于瓶颈神经网络的轨迹嵌入技术及其在飞行目标轨迹分类中的应用
雷磊 - Computer Science and Application, 2022 - hanspub.org
轨迹分类是利用目标运动轨迹识别出目标类型的技术. 如何从轨迹数据中提取出可分性好的轨迹
特征一直是轨迹分类的研究重点. 本论文提出了基于神经网络的轨迹嵌入方法 …
特征一直是轨迹分类的研究重点. 本论文提出了基于神经网络的轨迹嵌入方法 …