Clinical Text Classification of Alzheimer’s Drugs’ Mechanism of Action
Document Type
Conference Proceeding
Publication Date
1-1-2022
Publication Title
Lecture Notes in Networks and Systems
Publisher
Springer
Publisher Location
New York, NY
Volume
235
First page number:
513
Last page number:
521
Abstract
The Alzheimer’s disease Drug Development Pipeline [1, 2] delivers updates on potential AD-treatment, as well as drug development ongoing in clinical trials. To create these reports, researchers manually extract information from several resources like ClinicalTrials.gov and drug manufacturer websites; however, some of these items require expert review, such as when predicting a drug’s Mechanism of Action (MOA). In this paper, we aim to assist researchers by predicting and suggesting a drug’s MOA using Machine Learning. We test Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), XGBoost, and Decision Tree (DT) models. The latter showing the most promising results, with 95% accuracy, 100% recall, and a 0.92 F1-score.
Keywords
Alzheimer’s drug; Clinical text classification; Machine learning; Mechanism of action
Disciplines
Chemicals and Drugs | Nervous System Diseases
Repository Citation
Kambar, M.,
Nahed, P.,
Cacho, J. F.,
Lee, G.,
Cummings, J. L.,
Taghva, K.
(2022).
Clinical Text Classification of Alzheimer’s Drugs’ Mechanism of Action.
Lecture Notes in Networks and Systems, 235
513-521.
New York, NY: Springer.
http://dx.doi.org/10.1007/978-981-16-2377-6_48