Explainable, domain-adaptive, and federated artificial intelligence in medicine
Artificial intelligence (AI) continues to transform data analysis in many domains. Progress in
each domain is driven by a growing body of annotated data, increased computational …
each domain is driven by a growing body of annotated data, increased computational …
[HTML][HTML] Autoscore: a machine learning–based automatic clinical score generator and its application to mortality prediction using electronic health records
Background: Risk scores can be useful in clinical risk stratification and accurate allocations
of medical resources, helping health providers improve patient care. Point-based scores are …
of medical resources, helping health providers improve patient care. Point-based scores are …
[HTML][HTML] Long-term mortality risk stratification of liver transplant recipients: real-time application of deep learning algorithms on longitudinal data
Background Survival of liver transplant recipients beyond 1 year since transplantation is
compromised by an increased risk of cancer, cardiovascular events, infection, and graft …
compromised by an increased risk of cancer, cardiovascular events, infection, and graft …
[HTML][HTML] Assessing the utility of deep neural networks in predicting postoperative surgical complications: a retrospective study
A Bonde, KM Varadarajan, N Bonde… - The Lancet Digital …, 2021 - thelancet.com
Background Early detection of postoperative complications, including organ failure, is pivotal
in the initiation of targeted treatment strategies aimed at attenuating organ damage. In an …
in the initiation of targeted treatment strategies aimed at attenuating organ damage. In an …
Ensembling classical machine learning and deep learning approaches for morbidity identification from clinical notes
The past decade has seen an explosion of the amount of digital information generated
within the healthcare domain. Digital data exist in the form of images, video, speech …
within the healthcare domain. Digital data exist in the form of images, video, speech …
[HTML][HTML] Computational models for clinical applications in personalized medicine—guidelines and recommendations for data integration and model validation
CB Collin, T Gebhardt, M Golebiewski… - Journal of personalized …, 2022 - mdpi.com
The future development of personalized medicine depends on a vast exchange of data from
different sources, as well as harmonized integrative analysis of large-scale clinical health …
different sources, as well as harmonized integrative analysis of large-scale clinical health …
Time series prediction using deep learning methods in healthcare
Traditional machine learning methods face unique challenges when applied to healthcare
predictive analytics. The high-dimensional nature of healthcare data necessitates labor …
predictive analytics. The high-dimensional nature of healthcare data necessitates labor …
[HTML][HTML] Machine and deep learning for longitudinal biomedical data: a review of methods and applications
A Cascarano, J Mur-Petit… - Artificial Intelligence …, 2023 - Springer
Exploiting existing longitudinal data cohorts can bring enormous benefits to the medical
field, as many diseases have a complex and multi-factorial time-course, and start to develop …
field, as many diseases have a complex and multi-factorial time-course, and start to develop …
[HTML][HTML] Performance of intensive care unit severity scoring systems across different ethnicities in the USA: a retrospective observational study
Background Despite wide use of severity scoring systems for case-mix determination and
benchmarking in the intensive care unit (ICU), the possibility of scoring bias across …
benchmarking in the intensive care unit (ICU), the possibility of scoring bias across …
[HTML][HTML] Comparative analysis of explainable machine learning prediction models for hospital mortality
Background Machine learning (ML) holds the promise of becoming an essential tool for
utilising the increasing amount of clinical data available for analysis and clinical decision …
utilising the increasing amount of clinical data available for analysis and clinical decision …