Classification of genetic mutations using ontologies from clinical documents and deep learning
| Autor: | Neha Gupta, Veenu Bhasin, Priti Jagwani, Punam Bedi, Shivani |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Knowledge representation and reasoning
Computer science business.industry Deep learning Ontology (information science) computer.software_genre Automatic summarization Convolutional neural network Domain (software engineering) Identification (information) Artificial intelligence business Semantic Web computer Natural language processing |
| Popis: | Health care is an important aspect of human life in which medical data plays an important role. The medical data present in pathology reports, diagnosis, prescription, and clinical articles however is normally available in an unstructured textual format. Such data can be processed using Clinical Natural Language Processing (NLP) to unearth the important information. This information can be represented using ontology, a building block of the semantic web. Clinical NLP and ontology are used in the medical domain for text summarization and knowledge representation. This knowledge can be utilized for disease identification using machine learning and deep learning (DL) techniques. A framework for classifying cancerous genetic mutation reported in electronic health records has been proposed using ontologies and DL. In addition, a case study for the same is presented using clinical NLP, ontologies, and convolution neural network using catalog of somatic mutations in cancer mutation data and Kaggle’s cancer-diagnosis dataset. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|