NeuroWise: AI-Based NLP Model for Early Alzheimer’s Detection Using Clinical Text

Jibran Shar; Faisal Hussain; Imran Ali; Abdul Jabbar

Authors

Jibran Shar Department of Computer Science Quaid-e-Awam University of Engineering, Science and Technology Nawabshah, Pakistan
Faisal Hussain Department of Computer Science Quaid-e-Awam University of Engineering, Science and Technology Nawabshah, Pakistan
Imran Ali Department of Computer Science Quaid-e-Awam University of Engineering, Science and Technology Nawabshah, Pakistan
Abdul Jabbar Department of Computer Science Quaid-e-Awam University of Engineering, Science and Technology Nawabshah, Pakistan

Keywords:

Alzheimer’s Disease Detection, Machine Learning in Healthcare AI for Neurological Disorders, Deep Learning for Brain Imaging, Cognitive Decline Prediction, AI in Medical Diagnosis, Neuroimaging and AI

Abstract

Alzheimer's disease (AD) is a background neurodegenerative illness that affects millions of people worldwide. Early diagnosis and management are important for successful intervention and better patient outcomes. This study introduces a method of AD diagnosis using NLP from clinical notes and medical records. Machine learning algorithms are used for symptom classification and prediction from text data, yielding high accuracy and scalability. The suggested technique provides an affordable solution for early diagnosis, allowing increased access to cognitive healthcare.

References

World Health Organization, “Global action plan on the public health response to dementia,” WHO(World Heal. Organ., 2017, [Online]. Available: https://iris.who.int/bitstream/handle/10665/259615/9789241513487-eng.pdf?sequence=1

Y. Lu, D., Popuri, K., & Wang, “Multimodal Deep Learning for Alzheimer’s Disease Prediction Using Brain Imaging and Clinical Data,” Neuroimage, vol. 208, p. 116400, 2018.

D. Suk, H., & Shen, “Deep Learning- Based Feature Representation for AD/MCI Classification,” Neuroimage, vol. 101, pp. 506–517, 2015.

K. C. Fraser, J. A. Meltzer, and F. Rudzicz, “Linguistic features identify Alzheimer’s disease in narrative speech,” J. Alzheimer’s Dis., vol. 49, no. 2, pp. 407–422, Oct. 2015, doi: 10.3233/JAD-150520;WEBSITE:WEBSITE:SAGE;WGROUP:STRING:PUBLICATION.

M. Balagopalan, A., Oscherwitz, T., Sum, J., & Yancheva, “Analyzing speech patterns for early detection of Alzheimer’s disease,” Proc. IEEE EMBC, pp. 3156–3160, 2020.

Y. Li, Y., Yang, X., & Zhou, “A comparative study of machine learning models for Alzheimer’s disease prediction using clinical text data,” Healthc. Anal., vol. 2, no. 1, p. 100021, 2021.

J. Feng, L., Yap, P., & Li, “Machine Learning Models for Early Detection of Alzheimer’s Disease Using Neuroimaging Data,” J. Alzheimer’s Dis., vol. 75, no. 1, pp. 33–49, 2020.