A multi-objective optimization framework for determining optimal chemotherapy dosing and treatment duration
Traditional randomized clinical trials are regarded as the gold standard for assessing the efficacy of chemotherapy. However, this procedure has drawbacks such as high cost, time consumption, and limited patient exploration of treatment regimens. We develop a multi-objective optimization-based frame...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-06-01
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| Series: | Healthcare Analytics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772442524000376 |
| _version_ | 1827223294942642176 |
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| author | Ismail Abdulrashid Dursun Delen Basiru Usman Mark Izuchukwu Uzochukwu Idris Ahmed |
| author_facet | Ismail Abdulrashid Dursun Delen Basiru Usman Mark Izuchukwu Uzochukwu Idris Ahmed |
| author_sort | Ismail Abdulrashid |
| collection | DOAJ |
| description | Traditional randomized clinical trials are regarded as the gold standard for assessing the efficacy of chemotherapy. However, this procedure has drawbacks such as high cost, time consumption, and limited patient exploration of treatment regimens. We develop a multi-objective optimization-based framework to address these limitations and determine the best chemotherapy dosing and treatment duration. The proposed framework uses patient-specific biological parameters to create a mathematical model of cell population dynamics in the patient’s body. The framework employs evolutionary heuristic search methods (simulated annealing and genetic algorithms) and a prescriptive analytics approach to optimize therapy sessions that transition from treatment to relaxation. We carefully adjust the chemotherapy dose during treatment to reduce tumor cells while preserving host cells (such as effector-immune cells). We strategically time the relaxation sessions to aid recovery, considering the ability of tumors and healthy cells to regenerate. We use a combined optimization method to determine the length of the session and the amount of drug to be administered. We compare quadratic and linear optimal control solvers for drug administration while genetic algorithms and simulated annealing are used to optimize session length. This approach is especially important in limited healthcare resources, ensuring efficient allocation while accurately identifying high-risk patients to optimize resource allocation and utilization. |
| first_indexed | 2024-04-24T07:37:22Z |
| format | Article |
| id | doaj.art-6202d3f6e1b84f77951ef8c6e7ef2eb5 |
| institution | Directory Open Access Journal |
| issn | 2772-4425 |
| language | English |
| last_indexed | 2025-03-21T16:52:06Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Healthcare Analytics |
| spelling | doaj.art-6202d3f6e1b84f77951ef8c6e7ef2eb52024-06-15T06:14:59ZengElsevierHealthcare Analytics2772-44252024-06-015100335A multi-objective optimization framework for determining optimal chemotherapy dosing and treatment durationIsmail Abdulrashid0Dursun Delen1Basiru Usman2Mark Izuchukwu Uzochukwu3Idris Ahmed4School of Finance and Operations Management, Collins College of Business, The University of Tulsa, Tulsa, OK 74104, USA; Correspondence to: Collins College of Business, The University of Tulsa, 800 South Tucker Drive, Helmerich Hall 118A, Tulsa, 74104 OK, USA.Department of Management Science and Information Systems, Spears School of Business, Oklahoma State University, Stillwater, 74078, OK, USA; Department of Industrial Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, TurkeyDepartment of Business Management, Poole College of Management, North Carolina State University, Raleigh, NC 27695, USADepartment of Mathematics and Computer Science, Alabama State University, Montgomery, AL 36104, USADepartment of Mathematics, Faculty of Natural and Applied Sciences, Sule Lamido University, Kafin Hausa, Jigawa 700271, NigeriaTraditional randomized clinical trials are regarded as the gold standard for assessing the efficacy of chemotherapy. However, this procedure has drawbacks such as high cost, time consumption, and limited patient exploration of treatment regimens. We develop a multi-objective optimization-based framework to address these limitations and determine the best chemotherapy dosing and treatment duration. The proposed framework uses patient-specific biological parameters to create a mathematical model of cell population dynamics in the patient’s body. The framework employs evolutionary heuristic search methods (simulated annealing and genetic algorithms) and a prescriptive analytics approach to optimize therapy sessions that transition from treatment to relaxation. We carefully adjust the chemotherapy dose during treatment to reduce tumor cells while preserving host cells (such as effector-immune cells). We strategically time the relaxation sessions to aid recovery, considering the ability of tumors and healthy cells to regenerate. We use a combined optimization method to determine the length of the session and the amount of drug to be administered. We compare quadratic and linear optimal control solvers for drug administration while genetic algorithms and simulated annealing are used to optimize session length. This approach is especially important in limited healthcare resources, ensuring efficient allocation while accurately identifying high-risk patients to optimize resource allocation and utilization.http://www.sciencedirect.com/science/article/pii/S2772442524000376Prescriptive analyticsHealthcare analyticsDrug schedulingChemotherapyHeuristicsSimulation |
| spellingShingle | Ismail Abdulrashid Dursun Delen Basiru Usman Mark Izuchukwu Uzochukwu Idris Ahmed A multi-objective optimization framework for determining optimal chemotherapy dosing and treatment duration Healthcare Analytics Prescriptive analytics Healthcare analytics Drug scheduling Chemotherapy Heuristics Simulation |
| title | A multi-objective optimization framework for determining optimal chemotherapy dosing and treatment duration |
| title_full | A multi-objective optimization framework for determining optimal chemotherapy dosing and treatment duration |
| title_fullStr | A multi-objective optimization framework for determining optimal chemotherapy dosing and treatment duration |
| title_full_unstemmed | A multi-objective optimization framework for determining optimal chemotherapy dosing and treatment duration |
| title_short | A multi-objective optimization framework for determining optimal chemotherapy dosing and treatment duration |
| title_sort | multi objective optimization framework for determining optimal chemotherapy dosing and treatment duration |
| topic | Prescriptive analytics Healthcare analytics Drug scheduling Chemotherapy Heuristics Simulation |
| url | http://www.sciencedirect.com/science/article/pii/S2772442524000376 |
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