DeepPhe: A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records
| Autor: | Girish Chavan, Guergana Savova, Eugene Tseytlin, Chen Lin, Timothy A. Miller, Sean Finan, Olga Medvedeva, Melissa Castine, David J. Harris, Harry Hochheiser, Rebecca S. Jacobson |
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| Rok vydání: | 2017 |
| Předmět: |
Cancer Research
020205 medical informatics Computer science Genomic data Knowledge engineering 02 engineering and technology computer.software_genre Article 03 medical and health sciences 0302 clinical medicine Breast cancer Software Computer Systems Neoplasms 0202 electrical engineering electronic engineering information engineering medicine Data Mining Electronic Health Records Humans 030212 general & internal medicine Precision Medicine Natural Language Processing business.industry Cancer Reproducibility of Results medicine.disease Phenotype Cancer treatment ComputingMethodologies_PATTERNRECOGNITION Oncology Artificial intelligence business Clinical record computer Natural language processing Medical Informatics |
| Zdroj: | Cancer research. 77(21) |
| ISSN: | 1538-7445 |
| Popis: | Precise phenotype information is needed to understand the effects of genetic and epigenetic changes on tumor behavior and responsiveness. Extraction and representation of cancer phenotypes is currently mostly performed manually, making it difficult to correlate phenotypic data to genomic data. In addition, genomic data are being produced at an increasingly faster pace, exacerbating the problem. The DeepPhe software enables automated extraction of detailed phenotype information from electronic medical records of cancer patients. The system implements advanced Natural Language Processing and knowledge engineering methods within a flexible modular architecture, and was evaluated using a manually annotated dataset of the University of Pittsburgh Medical Center breast cancer patients. The resulting platform provides critical and missing computational methods for computational phenotyping. Working in tandem with advanced analysis of high-throughput sequencing, these approaches will further accelerate the transition to precision cancer treatment. Cancer Res; 77(21); e115–8. ©2017 AACR. |
| Databáze: | OpenAIRE |
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