Predicting adverse drug reactions using machine learning: Is Malaysia ready?
Several healthcare-related studies conducted in Malaysia have demonstrated that the implementation of machine learning (ML) can lead to significant improvements across various aspects of the healthcare ecosystem. Abas et al. (2025) developed an ML-based model to predict complications among patients...
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| Main Authors: | , |
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| Format: | Article |
| Language: | en |
| Published: |
Faculty of Pharmacy
2025
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/130958/1/130958.pdf https://ir.uitm.edu.my/id/eprint/130958/ |
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| Summary: | Several healthcare-related studies conducted in Malaysia have demonstrated that the implementation of machine learning (ML) can lead to significant improvements across various aspects of the healthcare ecosystem. Abas et al. (2025) developed an ML-based model to predict complications among patients with type 2 diabetes, thereby enabling early intervention and improved health outcomes. Similarly, Kasim et al. (2025) developed a reliable model for predicting 10-year cardiovascular disease (CVD) risk. The model outperformed traditional risk scores like the Framingham Risk Score, thereby enhancing both risk stratification and clinical decision-making for improved disease management. Additional applications of ML in the Malaysian healthcare setting included predicting medication wastage based on patient beliefs and evaluating medication administration errors in neonatal intensive care units. (Josephine et al., 2024; Aziz et al., 2025). |
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