[HTML][HTML] Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond
Abstract Explainable Artificial Intelligence (XAI) is an emerging research topic of machine
learning aimed at unboxing how AI systems' black-box choices are made. This research field …
learning aimed at unboxing how AI systems' black-box choices are made. This research field …
Artificial intelligence in healthcare
Artificial intelligence (AI) is gradually changing medical practice. With recent progress in
digitized data acquisition, machine learning and computing infrastructure, AI applications …
digitized data acquisition, machine learning and computing infrastructure, AI applications …
[HTML][HTML] Long COVID risk and pre-COVID vaccination in an EHR-based cohort study from the RECOVER program
Long COVID, or complications arising from COVID-19 weeks after infection, has become a
central concern for public health experts. The United States National Institutes of Health …
central concern for public health experts. The United States National Institutes of Health …
[HTML][HTML] Mapping ICD-10 and ICD-10-CM codes to phecodes: workflow development and initial evaluation
Background: The phecode system was built upon the International Classification of
Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) for phenome-wide association …
Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) for phenome-wide association …
A roadmap for foundational research on artificial intelligence in medical imaging: from the 2018 NIH/RSNA/ACR/The Academy Workshop
Imaging research laboratories are rapidly creating machine learning systems that achieve
expert human performance using open-source methods and tools. These artificial …
expert human performance using open-source methods and tools. These artificial …
[HTML][HTML] Deep representation learning of electronic health records to unlock patient stratification at scale
Deriving disease subtypes from electronic health records (EHRs) can guide next-generation
personalized medicine. However, challenges in summarizing and representing patient data …
personalized medicine. However, challenges in summarizing and representing patient data …
[HTML][HTML] Increased monocyte count as a cellular biomarker for poor outcomes in fibrotic diseases: a retrospective, multicentre cohort study
Background There is an urgent need for biomarkers to better stratify patients with idiopathic
pulmonary fibrosis by risk for lung transplantation allocation who have the same clinical …
pulmonary fibrosis by risk for lung transplantation allocation who have the same clinical …
Mining electronic health records (EHRs) A survey
The continuously increasing cost of the US healthcare system has received significant
attention. Central to the ideas aimed at curbing this trend is the use of technology in the form …
attention. Central to the ideas aimed at curbing this trend is the use of technology in the form …
[HTML][HTML] What every reader should know about studies using electronic health record data but may be afraid to ask
Coincident with the tsunami of COVID-19–related publications, there has been a surge of
studies using real-world data, including those obtained from the electronic health record …
studies using real-world data, including those obtained from the electronic health record …
[HTML][HTML] Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives
In secondary analysis of electronic health records, a crucial task consists in correctly
identifying the patient cohort under investigation. In many cases, the most valuable and …
identifying the patient cohort under investigation. In many cases, the most valuable and …