|
Descripción
|
|
|---|---|
| The automatic reconstruction of the patient?s treatment lines from their Electronic Health Records (EHRs) is a significant step towards improving the quality and the safety of the healthcare deliveries. With the recent rapid increase in the adaption of EHRs and the rapid development of computational science, we can discover new insights from the information stored in EHRs. However, this is still a challenging task, being unstructured data analysis one of them. In this paper, we focus on the most common challenges for reconstructing the patient?s treatment lines, which are the Named Entity Recognition (NER), temporal relation identification and the integration of structured results. We introduce our Natural Language Processing (NLP) framework, which deals with the aforementioned challenges. In addition, we focus on a real use case of patients, suffering from lung cancer to extract patterns associated with the treatment of the disease that can help clinicians to analyze toxicities and patterns depending on the lines of treatments given to the patient. | |
|
Internacional
|
Si |
|
Nombre congreso
|
SGAI: International Conference on Innovative Techniques and Applications of Artificial Intelligence |
|
Tipo de participación
|
960 |
|
Lugar del congreso
|
Cambridge, United Kingdom |
|
Revisores
|
Si |
|
ISBN o ISSN
|
0000-0000 |
|
DOI
|
10.1007/978-3-030-34885-4_33 |
|
Fecha inicio congreso
|
17/12/2019 |
|
Fecha fin congreso
|
19/12/2019 |
|
Desde la página
|
437 |
|
Hasta la página
|
442 |
|
Título de las actas
|
Artificial Intelligence XXXVI. 39th SGAI International Conference on Artificial Intelligence, AI 2019, Cambridge, UK, December 17?19, 2019, Proceedings |