Cross-Modal self-supervised vision language pre-training with multiple objectives for medical visual question answering
Medical Visual Question Answering (VQA) is a task that aims to provide answers to questions about medical images, which utilizes both visual and textual information in the reasoning process. The absence of large-scale annotated medical VQA ...
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Sleep apnea test prediction based on Electronic Health Records
The identification of Obstructive Sleep Apnea (OSA) is done by a Polysomnography test which is often done in later ages. Being able to notify potential insured members at earlier ages is desirable. For that, we develop predictive models that rely ...
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MultiADE: A Multi-domain benchmark for Adverse Drug Event extraction
Active adverse event surveillance monitors Adverse Drug Events (ADE) from different data sources, such as electronic health records, medical literature, social media and search engine logs. Over the years, many datasets have been ...
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Importance of variables from different time frames for predicting self-harm using health system data
- Charles J. Wolock,
- Brian D. Williamson,
- Susan M. Shortreed,
- Gregory E. Simon,
- Karen J. Coleman,
- Rodney Yeargans,
- Brian K. Ahmedani,
- Yihe Daida,
- Frances L. Lynch,
- Rebecca C. Rossom,
- Rebecca A. Ziebell,
- Maricela Cruz,
- Robert D. Wellman,
- R. Yates Coley
Self-harm risk prediction models developed using health system data (electronic health records and insurance claims information) often use patient information from up to several years prior to the index visit when the prediction is ...
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Early multi-cancer detection through deep learning: An anomaly detection approach using Variational Autoencoder
- Innocent Tatchum Sado,
- Louis Fippo Fitime,
- Geraud Fokou Pelap,
- Claude Tinku,
- Gaelle Mireille Meudje,
- Thomas Bouetou Bouetou
Cancer is a disease that causes many deaths worldwide. The treatment of cancer is first and foremost a matter of detection, a treatment that is most effective when the disease is detected at an early stage. With the evolution of technology, ...
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Taxonomy-based prompt engineering to generate synthetic drug-related patient portal messages
- Natalie Wang,
- Sukrit Treewaree,
- Ayah Zirikly,
- Yuzhi L. Lu,
- Michelle H. Nguyen,
- Bhavik Agarwal,
- Jash Shah,
- James Michael Stevenson,
- Casey Overby Taylor
The objectives of this study were to: (1) create a corpus of synthetic drug-related patient portal messages to address the current lack of publicly available datasets for model development, (2) assess differences in language used and ...
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Structural analysis and intelligent classification of clinical trial eligibility criteria based on deep learning and medical text mining
To enhance the efficiency, quality, and innovation capability of clinical trials, this paper introduces a novel model called CTEC-AC (Clinical Trial Eligibility Criteria Automatic Classification), aimed at structuring clinical trial ...
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Highlights
- In this study, given the complexity of clinical trial eligibility criteria and the difficulty in obtaining structured representation features, we proposed a feature enhancement strategy. This strategy combines the semantic parsing ...
Biomedical document-level relation extraction with thematic capture and localized entity pooling
In contrast to sentence-level relational extraction, document-level relation extraction poses greater challenges as a document typically contains multiple entities, and one entity may be associated with multiple other entities. Existing methods ...
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How to identify patient perception of AI voice robots in the follow-up scenario? A multimodal identity perception method based on deep learning
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Abstract ObjectivesPost-discharge follow-up stands as a critical component of post-diagnosis management, and the constraints of healthcare resources impede comprehensive manual follow-up. However, patients are less cooperative with AI follow-up calls or ...
Machine learning approaches for the discovery of clinical pathways from patient data: A systematic review
Clinical pathways are sequences of events followed during the clinical care of a group of patients who meet pre-defined criteria. They have many applications ranging from healthcare evaluation and optimization to clinical decision ...
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