A5051309127- Topic Modeling
- Biomedical Text Mining and Ontologies
- Machine Learning in Healthcare
- Data Quality and Management
- Health Education and Validation
- Neural Networks and Applications
- Fault Detection and Control Systems
- Semantic Web and Ontologies
- Industrial Automation and Control Systems
- Real-time simulation and control systems
- Advanced Computational Techniques and Applications
- Probabilistic and Robust Engineering Design
- Advanced Control Systems Optimization
- Pharmacovigilance and Adverse Drug Reactions
- Computational Drug Discovery Methods
- Misinformation and Its Impacts
- Anomaly Detection Techniques and Applications
- Brain Tumor Detection and Classification
- Face and Expression Recognition
- Markov Chains and Monte Carlo Methods
- Nuclear reactor physics and engineering
Nara Institute of Science and Technology
2023-2024
Adverse Drug Events (ADE) are key information present in unstructured portions of Electronic Health Records. These pose a significant challenge healthcare, ranging from mild discomfort to severe complications, and can impact patient safety treatment outcomes.
Background Natural language processing (NLP) techniques can be used to analyze large amounts of electronic health record texts, which encompasses various types patient information such as quality life, effectiveness treatments, and adverse drug event (ADE) signals. As different aspects a patient’s status are stored in documents, we propose an NLP system capable 6 documents: physician progress notes, discharge summaries, radiology reports, radioisotope nursing records, pharmacist notes....
Entity Linking performance has a strong reliance on having large quantity of high-quality annotated training data available. Yet, manual annotation named entities, especially their boundaries, is ambiguous, error-prone, and raises many inconsistencies between annotators. While imprecise boundary can degrade model's performance, there are applications where accurate extraction entities' surface form not necessary. For those cases, lenient guideline could relieve the annotators' workload speed...
<sec> <title>BACKGROUND</title> Named entity recognition (NER) is a fundamental task in natural language processing. However, it typically preceded by named annotation, which poses several challenges, especially the clinical domain. For instance, determining boundaries one of most common sources disagreements between annotators due to questions such as whether modifiers or peripheral words should be annotated. If unresolved, these can induce inconsistency produced corpora, yet, on other...
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<sec> <title>BACKGROUND</title> Natural language processing (NLP) techniques can be used to process large amounts of electronic health record (EHR) texts containing various types patient information such as quality life (QoL), effectiveness treatments, and adverse drug event (ADE) signals. However, different aspects a status are contained in documents, we propose an NLP system capable six documents: physician progress notes, discharge summaries, radiology reports, radioisotope (RI) nursing...
Named entity recognition (NER) is a fundamental task in natural language processing. However, it typically preceded by named annotation, which poses several challenges, especially the clinical domain. For instance, determining boundaries one of most common sources disagreements between annotators due to questions such as whether modifiers or peripheral words should be annotated. If unresolved, these can induce inconsistency produced corpora, yet, on other hand, strict guidelines adjudication...
Background:Adverse Drug Events (ADE) are key information present in unstructured portions of Electronic Health Records. These pose a significant challenge healthcare, ranging from mild discomfort to severe complications, and can impact patient safety treatment outcomes.Methods:We explore the influence domain shift between set dummy clinical notes real-world hospital corpus consisting Japanese related breast cancer when extracting ADEs free text. We annotated subset dataset used it fine-tune...