Neel Kanwal
Dilated Convolution Networks for Classification of ICD-9 based Clinical Summaries.
Rel. Maurizio Morisio, Giuseppe Rizzo. Politecnico di Torino, Master of science program in Communications And Computer Networks Engineering, 2020
|
Preview |
PDF (Tesi_di_laurea)
- Thesis
Licence: Creative Commons Attribution Non-commercial No Derivatives. Download (1MB) | Preview |
Abstract
Deployment of Artificial Intelligence for understanding clinical notes in the healthcare sector is a crucial step to extract meaningful phrases based on diseases. Electronic Health Records (EHR) are stored in the health care system in an unstructured and event associated way. Public clinical records can be used for billing, monitoring and insurance purpose. These clinical notes contain abbreviations, acronyms, and a non-uniform dictionary. Various Machine learning models are used with different approaches to understand these notes, these models are evaluated in various criteria based on datasets. These techniques differed mainly in pre-processing and code assignments as well as architecture for reading long medical documents.
In this thesis work, we propose a layered model of Convolution Neural Networks with pre-trained embeddings
Relators
Academic year
Publication type
Number of Pages
Course of studies
Classe di laurea
Aziende collaboratrici
URI
![]() |
Modify record (reserved for operators) |
