- Investigating Cross-Institutional Recognition of Cancer Registration Items: A Case Study on Catastrophic Forgetting AbstractA cancer registry is a critical database for cancer research, which require diverse domain knowledge and manual extraction of vital information from patient records for surveillance. In order to building a real-time and high-quality cancer registry database, a named entity recognition (NER) model based on bidirectional long short-term memory (BiLSTM)-conditional random fields (CRFs) to automatically extract 14 cancer registry items from unstructured pathology reports was developed for five hospitals. Because not all hospitals have sufficient training data, so that we apply transfer learning to develop our models for different hospitals. However, catastrophic forgetting leads to poor performance of the transferred model on the source hospital. To address this issue, we study the effectiveness of applying the elastic weight consolidation (EWC) method for the extraction of cancer registry items from the unstructured pathology reports of colorectal cancer to mitigate the occurrence of catastrophic forgetting. In our results, we observe that effective parameter settings can reduce the impact of catastrophic forgetting.
- Protected Health Information Recognition of Unstructured Code-Mixed Electronic Health Records in Taiwan Abstract
- An Ensemble Neural Network Model for Benefiting Pregnancy Health Stats from Mining Social Media Abstract
- NTTMUNSW System for n2c2 Track1: Cohort Selection for Clinical Trails Abstract
- Ensemble of Different Sequential Labeling Algorithms for Medication and Adverse Drug Event Extraction Abstract
- Family History Information Extraction with Neural Sequence Labeling Model Abstract
- Identification of Adverse Drug Reactions and Medication Intakes on Twitter Using Various Combinations of Language Features Abstract
- NTTMU System in the 2nd Social Media Mining for Health Applications Shared Task AbstractIn this study, we describe our methods to automatically classify Twitter posts describing events of adverse drug reaction and medication intake. We developed classifiers using linear support vector machines (SVM) and Naïve Bayes Multinomial (NBM) models. We extracted features to develop our models and conducted experiments to examine their effectiveness as part of our participation in AMIA 2017 Social Media Mining for Health Applications shared task. For both tasks, the best-performed models on the test sets were trained by using NBM with n-gram, part-of-speech and lexicon features, which achieved F-scores of 0.295 and 0.615, respectively.
- Pregnant Women Recognition from Social Media for Health-related Information Exploration Abstract
- Principle base Approach for Classifying Tweets with Flu-related Information in NTCIR-13 MedWeb Task AbstractNot Available
- Using a Recurrent Neural Network Model for Classification of Tweets Conveyed Influenza-related Information Abstract
- SPRENO: A BioC Module for Recognizing and Normalizing Species and Their Model Organisms Abstract
- Identifying Mutation-induced Protein-Protein Interactions in Scientific Literature Abstract
- A Study on Identification of Organism and micro-RNA Mentions in Figure Captions Abstract
- NTTMU-SCHEMA BeCalm API in BioCreative V.5 Abstract
- An Ensemble Algorithm for Sequential Labelling: A Case Study in Chemical Named Entity Recognition AbstractNot Avaiable
- Performance and interoperability assessment of Disease Extract Annotation Server Abstract
