Optimal control of dosage decisions in controlled ovarian hyperstimulation

He, M.; Zhao, Lei; Powell, Warren B.

doi:10.1007/s10479-009-0563-y

Cited by 13 publications

(6 citation statements)

References 39 publications

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“…A sizeable body of literature has explored the use of MDP in medical decision-making. The most recent papers in this stream of literature include screening, prevention or treatment of chronic diseases such as liver disease (Alagoz et al 2004, 2007a,b, Sandıkc ßı et al 2008, kidney disease (Lee et al 2008, Skandari et al 2015, HIV (Shechter et al 2008), ovarian hyperstimulation (He et al 2010), breast cancer (Ayvaci et al 2012, Chhatwal et al 2010, prostate cancer (Zhang et al 2012), colorectal cancer (Erenay et al 2014(Erenay et al , G€ unes ß et al 2015, and diabetes (Denton et al 2009, Honeycutt et al 2003, Kurt et al 2011, Mason et al 2014. It is important to note that all these papers tackle the problem at the population level, thereby not offering personalized recommendations.…”

Section: Mdp Applications In Medical Decision-makingmentioning

confidence: 99%

Designing Risk‐Adjusted Therapy for Patients with Hypertension

Zargoush

Gümüş

Verter

et al. 2018

Production and Operations Management

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L imited guidance is available for providing patient-specific care to hypertensive patients, although this chronic condition is the leading risk factor for cardiovascular diseases. To address this issue, we develop an analytical model that takes into account the most relevant risk factors including age, sex, blood pressure, diabetes status, smoking habits, and blood cholesterol. Using the Markov Decision Process framework, we develop a model to maximize expected qualityadjusted life years, as well as characterize the optimal sequence and combination of antihypertensive medications. Assuming the physician uses the standard medication dose for each drug, and the patient fully adheres to the prescribed treatment regimen, we prove that optimal treatment policies exhibit a threshold structure. Our findings indicate that our recommended thresholds vary by age and other patient characteristics, for example (1) the optimal thresholds for all medication prescription are nonincreasing in age, and (2) the medications need to be prescribed at lower thresholds for males who smoke than for males who have diabetes. The improvements in quality-adjusted life years associated with our model compare favorably with those obtained by following the British Hypertension Society's guideline, and the gains increase with the severity of risk factors. For instance, in both genders (although at different rates), diabetic patients gain more than non-diabetic patients. Our sensitivity analysis results indicate that the optimal thresholds decrease if the medications have lower side-effects and vice versa.

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Section: Mdp Applications In Medical Decision-makingmentioning

confidence: 99%

Designing Risk‐Adjusted Therapy for Patients with Hypertension

Zargoush

Gümüş

Verter

et al. 2018

Production and Operations Management

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“…For instance, [ 35 ] described an optimal dosing regimen for the infusion of FSH to patients undergoing in vitro fertilization. While in [ 36 ], control theory is employed to investigate optimal dosage decisions in the administration of gonadotropin in controlled ovarian hyperstimulation treatment cycle. The current paper expands previously published papers by Selgrade et al on the hormonal regulation of the menstrual cycle [ 7 , 12 ] and the transition to contraception [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Toward an optimal contraception dosing strategy

et al. 2023

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Anovulation refers to a menstrual cycle characterized by the absence of ovulation. Exogenous hormones such as synthetic progesterone and estrogen have been used to attain this state to achieve contraception. However, large doses are associated with adverse effects such as increased risk for thrombosis and myocardial infarction. This study utilizes optimal control theory on a modified menstrual cycle model to determine the minimum total exogenous estrogen/progesterone dose, and timing of administration to induce anovulation. The mathematical model correctly predicts the mean daily levels of pituitary hormones LH and FSH, and ovarian hormones E2, P4, and Inh throughout a normal menstrual cycle and reflects the reduction in these hormone levels caused by exogenous estrogen and/or progesterone. Results show that it is possible to reduce the total dose by 92% in estrogen monotherapy, 43% in progesterone monotherapy, and that it is most effective to deliver the estrogen contraceptive in the mid follicular phase. Finally, we show that by combining estrogen and progesterone the dose can be lowered even more. These results may give clinicians insights into optimal formulations and schedule of therapy that can suppress ovulation.

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“…), ovarian hyperstimulation (He et al. ), the timing of biopsy decisions in mammography screening (Chhatwal et al. ), and the optimizing of diagnostic decisions after a mammography (Ayvaci et al.…”

Section: Introductionmentioning

confidence: 99%

“…In a setting such as ours, where medical decisions need to be made sequentially in highly stochastic environments, Markov decision processes (MDP) are ideally suited; see Schaefer et al (2004) for a comprehensive survey. More recent references include MDP applications to liver transplantation (Alagoz et al 2007), to HIV (Shechter et al 2008), ovarian hyperstimulation (He et al 2010), the timing of biopsy decisions in mammography screening (Chhatwal et al 2010), and the optimizing of diagnostic decisions after a mammography (Ayvaci et al 2012). POMDPs are an extension of MDPs which relax the assumption that the state of the system is known.…”

Section: Introductionmentioning

confidence: 99%

Designing Personalized Treatment: An Application to Anticoagulation Therapy

Ibrahim

Küçükyazicı

Verter

et al. 2016

Production and Operations Management

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I n this study, we develop an analytical framework for personalizing the anticoagulation therapy of patients who are taking warfarin. Consistent with medical practice, our treatment design consists of two stages: (i) the initiation stage, modeled using a partially-observable Markov decision process, during which the physician learns through systematic belief updates about the unobservable patient sensitivity to warfarin, and (ii) the maintenance stage, modeled using a Markov decision process, during which the physician relies on his formed belief about patient sensitivity to determine the stable, patient-specific, warfarin dose to prescribe. We develop an expression for belief updates in the POMDP, establish the optimality of the myopic policy for the MDP, and derive conditions for the existence and uniqueness of a myopically optimal dose. We validate our models using a real-life patient data set gathered at the Hematology Clinic of the Jewish General Hospital in Montreal. The proposed analytical framework and case study enable us to develop useful clinical insights, for example, concerning the length of the initiation period and the importance of correctly assessing patient sensitivity.

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Optimal control of dosage decisions in controlled ovarian hyperstimulation

Cited by 13 publications

References 39 publications

Designing Risk‐Adjusted Therapy for Patients with Hypertension

Designing Risk‐Adjusted Therapy for Patients with Hypertension

Toward an optimal contraception dosing strategy

Designing Personalized Treatment: An Application to Anticoagulation Therapy

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