A precision medicine approach to personalized prescribing using genetic and nongenetic factors for clinical decision-making
Issued Date
2023-10-01
Resource Type
ISSN
00104825
eISSN
18790534
Scopus ID
2-s2.0-85168411135
Journal Title
Computers in Biology and Medicine
Volume
165
Rights Holder(s)
SCOPUS
Bibliographic Citation
Computers in Biology and Medicine Vol.165 (2023)
Suggested Citation
Jamrat S., Sukasem C., Sratthaphut L., Hongkaew Y., Samanchuen T. A precision medicine approach to personalized prescribing using genetic and nongenetic factors for clinical decision-making. Computers in Biology and Medicine Vol.165 (2023). doi:10.1016/j.compbiomed.2023.107329 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/88945
Title
A precision medicine approach to personalized prescribing using genetic and nongenetic factors for clinical decision-making
Other Contributor(s)
Abstract
Screening potential drug–drug interactions, drug–gene interactions, contraindications, and other factors is crucial in clinical practice. However, implementing these screening concepts in real-world settings poses challenges. This work proposes an approach towards precision medicine that combines genetic and nongenetic factors to facilitate clinical decision-making. The approach focuses on raising the performance of four potential interaction screenings in the prescribing process, including drug–drug interactions, drug–gene interactions, drug–herb interactions, drug–social lifestyle interactions, and two potential considerations for patients with liver or renal impairment. The work describes the design of a curated knowledge-based model called the knowledge model for potential interaction and consideration screening, the screening logic for both the detection module and inference module, and the personalized prescribing report. Three case studies have demonstrated the proof-of-concept and effectiveness of this approach. The proposed approach aims to reduce decision-making processes for healthcare professionals, reduce medication-related harm, and enhance treatment effectiveness. Additionally, the recommendation with a semantic network is suggested to assist in risk–benefit analysis when health professionals plan therapeutic interventions with new medicines that have insufficient evidence to establish explicit recommendations. This approach offers a promising solution to implementing precision medicine in clinical practice.