Federated learning for healthcare: Systematic review and architecture proposal
The use of machine learning (ML) with electronic health records (EHR) is growing in
popularity as a means to extract knowledge that can improve the decision-making process in …
popularity as a means to extract knowledge that can improve the decision-making process in …
Machine learning for software engineering: A tertiary study
Machine learning (ML) techniques increase the effectiveness of software engineering (SE)
lifecycle activities. We systematically collected, quality-assessed, summarized, and …
lifecycle activities. We systematically collected, quality-assessed, summarized, and …
[HTML][HTML] Addressing the clinical unmet needs in primary Sjögren's Syndrome through the sharing, harmonization and federated analysis of 21 European cohorts
For many decades, the clinical unmet needs of primary Sjögren's Syndrome (pSS) have
been left unresolved due to the rareness of the disease and the complexity of the underlying …
been left unresolved due to the rareness of the disease and the complexity of the underlying …
[HTML][HTML] A computational pipeline for data augmentation towards the improvement of disease classification and risk stratification models: A case study in two clinical …
VC Pezoulas, GI Grigoriadis, G Gkois… - Computers in Biology …, 2021 - Elsevier
Virtual population generation is an emerging field in data science with numerous
applications in healthcare towards the augmentation of clinical research databases with …
applications in healthcare towards the augmentation of clinical research databases with …
Unsynchronized wearable sensor data analytics model for improving the performance of smart healthcare systems
O Alfarraj, A Tolba - Journal of Ambient Intelligence and Humanized …, 2021 - Springer
Background A wearable sensor (WS) is a prominent technology application that senses and
gathers information from a user for analyzing changes in physiological signs. Analyzing the …
gathers information from a user for analyzing changes in physiological signs. Analyzing the …
ICU admission and mortality classifiers for COVID-19 patients based on subgroups of dynamically associated profiles across multiple timepoints
Abstract The coronavirus disease 2019 (COVID-19) which is caused by severe acute
respiratory syndrome coronavirus type 2 (SARS-CoV-2) is consistently causing profound …
respiratory syndrome coronavirus type 2 (SARS-CoV-2) is consistently causing profound …
[PDF][PDF] The clinical and technical impact of the HarmonicSS project
Clinical and technical impact of the HarmonicSS project/AV Goules et al. of beyond the state-
of-the-art data curator mechanisms to enhance the quality of clinical data in terms of …
of-the-art data curator mechanisms to enhance the quality of clinical data in terms of …
Pretrained Language Models for Semantics-Aware Data Harmonisation of Observational Clinical Studies in the Era of Big Data
JJ Dylag, Z Zlatev, M Boniface - medRxiv, 2024 - medrxiv.org
In clinical research, there is a strong drive to leverage big data from population cohort
studies and routine electronic healthcare records to design new interventions, improve …
studies and routine electronic healthcare records to design new interventions, improve …
A federated AI strategy for the classification of patients with Mucosa Associated Lymphoma Tissue (MALT) lymphoma across multiple harmonized cohorts
Mucosa Associated Lymphoma Tissue (MALT) type is an extremely rare type of lymphoma
which occurs in less than 3% of patients with primary Sjögren's Syndrome (pSS). No …
which occurs in less than 3% of patients with primary Sjögren's Syndrome (pSS). No …
A hybrid data harmonization workflow using word embeddings for the interlinking of heterogeneous cross-domain clinical data structures
Retrospective data harmonization is an open issue in healthcare due to the emerging need
to interlink data from multiple clinical centers with the absence of standardized data …
to interlink data from multiple clinical centers with the absence of standardized data …