A novel AI model for the extraction and prediction of Alzheimer disease from electronic health record
Big data; Dark data; Electronic health record; Machine learning; Natural language processing
Dark data is an emerging concept, with its existence, identification, and utilization being key areas of research. This study examines various aspects and impacts of dark data in the healthcare domain and designs a model to extract essential clinical parameters for Alzheimer's from electronic health records (EHR). The novelty of dark data lies in its significant impact across sectors. In healthcare, even the smallest data points are crucial for diagnosis, prediction, and treatment. Thus, identifying and extracting dark data from medical data corpora enhances decision-making. In this research, a natural language processing (NLP) model is employed to extract clinical information related to Alzheimer's disease, and a machine learning algorithm is used for prediction. Named entity recognition (NER) with SpaCy is utilized to extract clinical departments from doctors' descriptions stored in EHRs. This NER model is trained on custom data containing processed EHR text and associated entity annotations. The extracted clinical departments can then be used for future Alzheimer's diagnosis via support vector machine (SVM) algorithms. Results show improved accuracy with the use of extracted dark data, highlighting its importance in predicting Alzheimer's disease. This research also explores the presence of dark data in various domains and proposes a dark data extraction model for the clinical domain using NLP. 2025 Institute of Advanced Engineering and Science. All rights reserved.
Indonesian Journal of Electrical Engineering and Computer Science, Vol-37, No. 2, pp. 1023-1031.
Institute of Advanced Engineering and Science
https://doi.org/10.11591/ijeecs.v37.i2.pp1023-1031 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210744676&doi=10.11591%2fijeecs.v37.i2.pp1023-1031&partnerID=40&md5=a8cd65f57f1f383add56616138d82584
Maju S.V., Department of Computer Science and Engineering, CHRIST University, Bengaluru, India; Prakasi G., Department of Computer Science and Engineering, CHRIST University, Bengaluru, India; Pushpam O.S., Department of Computer Science and Engineering, CHRIST University, Bengaluru, India