TY  - JOUR
T1  - Adaptive Model for Disability Determination Decision Process Based on
Natural Language Processing
AU - Amer, Eslam AU - Elfatah, Mohammed Abel 
JO  - Research Journal of Applied Sciences
VL  - 12
IS  - 7
SP  - 384
EP  - 391
PY  - 2017
DA  - 2001/08/19
SN  - 1815-932x
DO  - rjasci.2017.384.391
UR  - https://makhillpublications.co/view-article.php?doi=rjasci.2017.384.391
KW  - disability determination
KW  -biomedical text mining
KW  -Natural language processing
KW  -electronic health records
KW  -reducing
KW  -quality
AB  - Due to the high growth rate in claiming disability benefits, Social Security Administration (SSA) faces
a real overload challenge. Disability determination process has turned out to be time-consuming, complicated
and expensive. By unlocking patient’s details, we can gain valuable information that could lead to improvement
in the quality of healthcare, reducing time and healthcare cost. This study presents an approach to ease the
process of disability determination. Our approach uses natural language processing and biomedical text mining
to deal with data stored in patient’s Electronic Healthcare Records (EHRs). Such data may encode significant
information about the patient’s case. The developed system extracts relevant medical entities and builds
relations between symptoms and other clinical signature modifiers. The proposed system uses extracted
information as evaluation features. Such features decide whether an applicant should gain disability benefits.
Evaluations show that the proposed system accurately extracts symptoms and other laboratory marks with high
F-measures (93.5-95.6%). The proposed automated system deduces right assessments to approve or reject the
applicants for disability benefits.
ER  -