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.
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This material is based upon work supported by the National Science Foundation under Grant No. 1625677.
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Kambar, M.E.Z.N., Nahed, P., Cacho, J.R.F., Lee, G., Cummings, J., Taghva, K. (2022). Clinical Text Classification of Alzheimer’s Drugs’ Mechanism of Action. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 235. Springer, Singapore. https://doi.org/10.1007/978-981-16-2377-6_48
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