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Semi-Supervised Natural Language Approach for Fine-Grained Classification of Medical Reports
Although machine learning has become a powerful tool to augment doctors in clinical analysis, the immense amount of labeled data that is necessary to train supervised learning approaches burdens each development task as time and resource intensive.
An Overview and Case Study of the Clinical AI Model Development Life Cycle for Healthcare Systems
Healthcare is one of the most promising areas for machine learning models to make a positive impact.
"Name that manufacturer". Relating image acquisition bias with task complexity when training deep learning models: experiments on head CT
Recognition of such bias is critical to develop robust, generalizable models that will be crucial for clinical applications in real-world data distributions.
Phenotype Detection in Real World Data via Online MixEHR Algorithm
Understanding patterns of diagnoses, medications, procedures, and laboratory tests from electronic health records (EHRs) and health insurer claims is important for understanding disease risk and for efficient clinical development, which often require rules-based curation in collaboration with clinicians.
Semantic Decomposition Improves Learning of Large Language Models on EHR Data
Electronic health records (EHR) are widely believed to hold a profusion of actionable insights, encrypted in an irregular, semi-structured format, amidst a loud noise background.
