@inproceedings{2a4f66a293694e428e3e0aeb1054ed4c,
title = "Deep neural models for chronic disease status detection in free text clinical records",
abstract = "Timely identification or prediction of a disease status can be a boon to patient's life. The elements that can detect the diseases status are mostly present in free text clinical records; these records contain private information about the patients. The time-consuming manual process to identify the disease status from these clinical records can be error-prone and comes with an expense. Hence the need to automate or semi automate this process is felt in the community. In this paper, we have used deep learning techniques on the publically available i2b2 clinical datasets to detect the chronic disease status, leading to promising results.",
keywords = "Clinical datasets, Deep Learning, Disease Status, Machine Learning, Multi Channel, NLP technique, Single channel",
author = "Kunal Rajput and Girija Chetty and Rachel Davey",
year = "2018",
month = nov,
day = "17",
doi = "10.1109/ICDMW.2018.00127",
language = "English",
isbn = "9781538692899",
series = "IEEE International Conference on Data Mining Workshops, ICDMW",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
pages = "864--869",
editor = "Jeffrey Yu and Zhenhui Li and Hanghang Tong and Feida Zhu",
booktitle = "Proceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018",
address = "United States",
note = "IEEE International Conference on Data Mining ; Conference date: 20-11-2018",
}