scholarly journals Type 2 Vague Events and Their Applications

2021 ◽  
pp. 1-2
Author(s):  
Houju Hori Jr
Keyword(s):  
Differential Equation ◽  
Bayesian Inference ◽  
Markov Process ◽  
Decision Process ◽  
Mapping Function ◽  
Quadratic Mapping ◽  
Mapping Functions ◽  
Markov Decision

[1] discovered a mapping formula for Type 1 Vague events, and presented an alternative problem as an example of its application. Since it is well known that the alternative problem results in sequential Bayesian inference, the subsequent research flow is to make the mapping formula multidimensional, to derive the Markov (decision) process by introducing the concept of time, and so on. Furthermore, the stochastic differential equation from which it is derived was formulated. [2] This paper refers to Type 2 Vague events based on the secondary mapping formula. This quadratic mapping formula gives a certain rotation to a non-mapping function by transforming it with a relationship between the two mapping functions. Furthermore, here we refer to the derivation of the Type 2 Vague Markov process and the initial and stop conditions for its rotation.

scholarly journals Type 2 Vague Events and Their Applications

2021 ◽  
pp. 1-2
Author(s):  
Houju Hori Jr
Keyword(s):  
Differential Equation ◽  
Bayesian Inference ◽  
Markov Process ◽  
Decision Process ◽  
Mapping Function ◽  
Quadratic Mapping ◽  
Mapping Functions ◽  
Markov Decision

[1] discovered a mapping formula for Type 1 Vague events, and presented an alternative problem as an example of its application. Since it is well known that the alternative problem results in sequential Bayesian inference, the subsequent research flow is to make the mapping formula multidimensional, to derive the Markov (decision) process by introducing the concept of time, and so on. Furthermore, the stochastic differential equation from which it is derived was formulated. [2] This paper refers to Type 2 Vague events based on the secondary mapping formula. This quadratic mapping formula gives a certain rotation to a non-mapping function by transforming it with a relationship between the two mapping functions. Furthermore, here we refer to the derivation of the Type 2 Vague Markov process and the initial and stop conditions for its rotation.

A Markov decision process for modeling adverse drug reactions in medication treatment of type 2 diabetes

2019 ◽  
Vol 233 (8) ◽  
pp. 793-811 ◽  
Author(s):  
Maryam Eghbali-Zarch ◽  
Reza Tavakkoli-Moghaddam ◽  
Fatemeh Esfahanian ◽  
Amir Azaron ◽  
Mohammad Mehdi Sepehri
Keyword(s):  
Type 2 Diabetes ◽  
Adverse Drug Reaction ◽  
Drug Reaction ◽  
Markov Decision Process ◽  
Decision Process ◽  
Drug Reactions ◽  
Medication Treatment ◽  
Markov Decision

Type 2 diabetes has an increasing prevalence and high cost of treatment. The goal of type 2 diabetes treatment is to control patients’ blood glucose level by pharmacological interventions and to prevent adverse disease-related complications. Therefore, it is important to optimize the medication treatment plans for type 2 diabetes patients to enhance the quality of their lives and to decrease the economic burden of this chronic disease. Since the treatment of type 2 diabetes relies on medication, it is vital to consider adverse drug reactions. Adverse drug reaction is undesired harmful reactions that may result from some certain medications. Therefore, a Markov decision process is developed in this article to model the medication treatment of type 2 diabetes, considering the possibility of adverse drug reaction occurring adverse drug reaction. The optimal policy of the proposed Markov decision process model is compared with clinical guidelines and existing models in the literature. Moreover, a sensitivity analysis is conducted to address the manner in which model behavior depends on model parameterization and then therapeutic insights are obtained based on the results. The satisfying results show that the model has the capability to offer an optimal treatment policy with an acceptable expected quality of life by utilizing fewer medications and provide significant implications in endocrinology and metabolism applications.

scholarly journals A Markov decision process approach to optimizing cancer therapy using multiple modalities

2019 ◽  
Vol 37 (1) ◽  
pp. 22-39
Author(s):  
Kelsey Maass ◽  
Minsun Kim
Keyword(s):  
Tumor Progression ◽  
Markov Decision Process ◽  
Decision Process ◽  
Side Effect ◽  
Normal Tissue ◽  
Process Approach ◽  
Treatment Modalities ◽  
Cancer Management ◽  
Markov Decision

Abstract There are several different modalities, e.g. surgery, chemotherapy and radiotherapy, that are currently used to treat cancer. It is common practice to use a combination of these modalities to maximize clinical outcomes, which are often measured by a balance between maximizing tumor damage and minimizing normal tissue side effects due to treatment. However, multi-modality treatment policies are mostly empirical in current practice and are therefore subject to individual clinicians’ experiences and intuition. We present a novel formulation of optimal multi-modality cancer management using a finite-horizon Markov decision process approach. Specifically, at each decision epoch, the clinician chooses an optimal treatment modality based on the patient’s observed state, which we define as a combination of tumor progression and normal tissue side effect. Treatment modalities are categorized as (1) type 1, which has a high risk and high reward, but is restricted in the frequency of administration during a treatment course; (2) type 2, which has a lower risk and lower reward than type 1, but may be repeated without restriction; and (3) type 3, no treatment (surveillance), which has the possibility of reducing normal tissue side effect at the risk of worsening tumor progression. Numerical simulations using various intuitive, concave reward functions show the structural insights of optimal policies and demonstrate the potential applications of using a rigorous approach to optimizing multi-modality cancer management.

Type 2 Diabetes Overtakes Type 1 in Hispanic Girls

2008 ◽  
Vol 38 (15) ◽  
pp. 18
Author(s):  
SHERRY BOSCHERT
Keyword(s):  
Type 2 Diabetes
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