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Extracting comprehensive clinical information for breast cancer using deep learning methods
استخراج اطلاعات جامع بالینی برای سرطان پستان با استفاده از روشهای یادگیری عمیق-2019 Objective Breast cancer is the most common malignant tumor among women. The diagnosis and
treatment information of breast cancer patients is abundant in multiple types of clinical fields, including
clinicopathological data, genotype and phenotype information, treatment information, and prognosis
information. However, current studies are mainly focused on extracting information from one specific
type of clinical field. This study defines a comprehensive information model to represent the
whole-course clinical information of patients. Furthermore, deep learning approaches are used to
extract the concepts and their attributes from clinical breast cancer documents by fine-tuning pretrained
Bidirectional Encoder Representations from Transformers (BERT) language models.
Materials and Methods The clinical corpus that was used in this study was from one 3A cancer
hospital in China, consisting of the encounter notes, operation records, pathology notes, radiology
notes, progress notes and discharge summaries of 100 breast cancer patients. Our system consists of
two components: a named entity recognition (NER) component and a relation recognition component.
For each component, we implemented deep learning-based approaches by fine-tuning BERT, which
outperformed other state-of-the-art methods on multiple natural language processing (NLP) tasks. A
clinical language model is first pretrained using BERT on a large-scale unlabeled corpus of Chinese
clinical text. For NER, the context embeddings that were pretrained using BERT were used as the input
features of the Bi-LSTM-CRF (Bidirectional long-short-memory-conditional random fields) model and
were fine-tuned using the annotated breast cancer notes. Furthermore, we proposed an approach to
fine-tune BERT for relation extraction. It was considered to be a classification problem in which the
two entities that were mentioned in the input sentence were replaced with their semantic types.
Results Our best-performing system achieved F1 scores of 93.53% for the NER and 96.73% for the
relation extraction. Additional evaluations showed that the deep learning-based approaches that
fine-tuned BERT did outperform the traditional Bi-LSTM-CRF and CRF machine learning algorithms
in NER and the attention-Bi-LSTM and SVM (support vector machines) algorithms in relation
recognition.
Conclusion In this study, we developed a deep learning approach that fine-tuned BERT to extract the
breast cancer concepts and their attributes. It demonstrated its superior performance compared to
traditional machine learning algorithms, thus supporting its uses in broader NER and relation extraction
tasks in the medical domain. Keywords: clinical information extraction | breast cancer | deep learning | fine-tuning BERT | information model |
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