Annotating and Detecting Medical Events in Clinical Notes
Early detection and treatment of diseases that onset after a patient is admitted to a hospital, such as pneumonia, is critical to improving and reducing costs in healthcare. Previous studies (Tepper et al., 2013) showed that change-of-state events in clinical notes could be important cues for phenotype detection. In this paper, we extend the annotation schema proposed in (Klassen et al., 2014) to mark change-of-state events, diagnosis events, coordination, and negation. After we have completed the annotation, we build NLP systems to automatically identify named entities and medical events, which yield an f-score of 94.7{\%} and 91.8{\%}, respectively.
Code (0)
등록된 구현이 없습니다.
Tasks
NegationSimilar Papers 제목 키워드 기반
Listwise temporal ordering of events in clinical notes
We present metrics for listwise temporal ordering of events in clinical notes, as well as a baseline listwise temporal ranking model that generates a timeline of events that can be used in downstream medical natural lang…
Information RetrievalRelation ExtractionUse of 'off-the-shelf' information extraction algorithms in clinical informatics: a feasibility study of MetaMap annotation of Italian medical notes
Information extraction from narrative clinical notes is useful for patient care, as well as for secondary use of medical data, for research or clinical purposes. Many studies focused on information extraction from Englis…
Effective Medical Code Prediction via Label Internal Alignment
The clinical notes are usually typed into the system by physicians. They are typically required to be marked by standard medical codes, and each code represents a diagnosis or medical treatment procedure. Annotating thes…
Medical Code PredictionPredictionNeural Architecture for Temporal Relation Extraction: A Bi-LSTM Approach for Detecting Narrative Containers
We present a neural architecture for containment relation identification between medical events and/or temporal expressions. We experiment on a corpus of de-identified clinical notes in English from the Mayo Clinic, name…
RelationRelation ExtractionTemporal Information ExtractionTemporal Relation ExtractionSystematic Comparative Analysis of Large Pretrained Language Models on Contextualized Medication Event Extraction
Attention-based models have become the leading approach in modeling medical language for Natural Language Processing (NLP) in clinical notes. These models outperform traditional techniques by effectively capturing contex…
Information ExtractionEvent Extraction