[HTML][HTML] Multimodal biomedical AI
The increasing availability of biomedical data from large biobanks, electronic health records,
medical imaging, wearable and ambient biosensors, and the lower cost of genome and …
medical imaging, wearable and ambient biosensors, and the lower cost of genome and …
Multimodal data fusion for cancer biomarker discovery with deep learning
Technological advances have made it possible to study a patient from multiple angles with
high-dimensional, high-throughput multiscale biomedical data. In oncology, massive …
high-dimensional, high-throughput multiscale biomedical data. In oncology, massive …
[HTML][HTML] Designing a feature selection method based on explainable artificial intelligence
J Zacharias, M von Zahn, J Chen, O Hinz - Electronic Markets, 2022 - Springer
Nowadays, artificial intelligence (AI) systems make predictions in numerous high stakes
domains, including credit-risk assessment and medical diagnostics. Consequently, AI …
domains, including credit-risk assessment and medical diagnostics. Consequently, AI …
[HTML][HTML] An open-source framework for end-to-end analysis of electronic health record data
With progressive digitalization of healthcare systems worldwide, large-scale collection of
electronic health records (EHRs) has become commonplace. However, an extensible …
electronic health records (EHRs) has become commonplace. However, an extensible …
Opportunities and challenges for biomarker discovery using electronic health record data
Electronic health records (EHRs) have become increasingly relied upon as a source for
biomedical research. One important research application of EHRs is the identification of …
biomedical research. One important research application of EHRs is the identification of …
[HTML][HTML] Methodological issues of the electronic health records' use in the context of epidemiological investigations, in light of missing data: a review of the recent …
T Tsiampalis, D Panagiotakos - BMC medical research methodology, 2023 - Springer
Abstract Background Electronic health records (EHRs) are widely accepted to enhance the
health care quality, patient monitoring, and early prevention of various diseases, even when …
health care quality, patient monitoring, and early prevention of various diseases, even when …
Missing values and imputation in healthcare data: Can interpretable machine learning help?
Missing values are a fundamental problem in data science. Many datasets have missing
values that must be properly handled because the way missing values are treated can have …
values that must be properly handled because the way missing values are treated can have …
[HTML][HTML] Lifting hospital electronic health record data treasures: challenges and opportunities
Electronic health records (EHRs) have been successfully used in data science and machine
learning projects. However, most of these data are collected for clinical use rather than for …
learning projects. However, most of these data are collected for clinical use rather than for …
Prediction of Venous Thromboembolism in Diverse Populations Using Machine Learning and Structured Electronic Health Records
R Chen, BO Petrazzini, WA Malick… - … and Vascular Biology, 2024 - Am Heart Assoc
BACKGROUND: Venous thromboembolism (VTE) is a major cause of morbidity and
mortality worldwide. Current risk assessment tools, such as the Caprini and Padua scores …
mortality worldwide. Current risk assessment tools, such as the Caprini and Padua scores …
[HTML][HTML] Studying missingness in spinal cord injury data: challenges and impact of data imputation
Background In the last decades, medical research fields studying rare conditions such as
spinal cord injury (SCI) have made extensive efforts to collect large-scale data. However …
spinal cord injury (SCI) have made extensive efforts to collect large-scale data. However …