Deep Learning-based Clinical Temporal Relation Extraction
The extraction of temporal relations in medical text has been drawing growing attention because of its potential to dramatically increase the understanding of many medical phenomena such as disease progression, longitudinal effects of medications, a patient's clinical course, and its many clinical applications such as question answering, clinical outcomes prediction, and the recognition of temporal patterns and timelines. We develop deep neural architectures (e.g. Convolutional or Recurrent Neural Networks) for clinical temporal relation extraction. Comparing with conventional models, which make use of heavily engineered features, neural models employ simple token features or slightly enhanced features, establishing state-of-the-art results.
Chen Lin is an Informatician in Boston Children’s Hospital Informatics Program-Natural Language Processing (CHIP-NLP) group, an Apache cTAKES developer. Chen’s work involves using machine learning in clinical NLP tasks. Topics include clinical temporal relation extraction, disease activity classification based on clinical narratives, drug-induced adverse event prediction, deep phenotyping. His main interests in computational methods include convolutional neural networks, recurrent neural networks, auto-encoder/decoder, word and syntactic embeddings, semi-supervised learning, feature selection, and kernel methods. He holds Master degree in Computer Science from Brandeis University.