Reinforcement learning-based control for combined infusion of sedatives and analgesics
| Autor: | Wassim M. Haddad, Nader Meskin, Regina Padmanabhan |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Drug
medicine.medical_specialty medicine.drug_class media_common.quotation_subject Analgesic law.invention Remifentanil 03 medical and health sciences 0302 clinical medicine 030202 anesthesiology law medicine Reinforcement learning 030212 general & internal medicine Intensive care medicine Propofol media_common Clinical pharmacology business.industry Therapeutic effect Clinical trial Interactive effects Sedative Anesthesia Level business Biomedical engineering |
| Zdroj: | CoDIT |
| DOI: | 10.1109/codit.2017.8102643 |
| Popis: | The focus of several clinical trials and research in the area of clinical pharmacology is to fine tune the drug dosing in the phase of additive, antagonistic, and synergistic drug interactive effects. It is important to consider the interactive effects of the drugs to restrict the drug usage to the optimal level required to achieve certain therapeutic effects. Such optimal drug dosing methods will minimize the adverse drug effects and cost associated with the treatment. In this paper, we discuss the use of a reinforcement learning (RL)-based controller to fine tune the drug titration while different drugs with interactive effects are administrated simultaneously. We demonstrate the efficacy of the method by using 25 simulated patients for the simultaneous infusion of a sedative and analgesic drug which has synergistic interactive effect. 1 2017 IEEE. This publication was made possible by the GSRA grant No. GSRA1-1-1128-13016 from the Qatar National Research Fund (a member of Qatar Foundation). The findings achieved herein are solely the responsibility of the authors. Scopus |
| Databáze: | OpenAIRE |
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