System dynamics simulation for evaluating implementation strategies of genomic sequencing: tutorial and conceptual model.

INTRODUCTION: Precision Medicine (PM), especially in oncology, involve diagnostic and complex treatment pathways that are based on genomic features. To conduct evaluation and decision analysis for PM, advanced modeling techniques are needed due to its complexity. Although System Dynamics (SD) has strong modeling power, it has not been widely used in PM and individualized treatment. AREAS COVERED: We explained SD tools using examples in cancer context and the rationale behind using SD for genomic testing and personalized oncology. We compared SD with other Dynamic Simulation Modelling (DSM) methods and listed SD's advantages. We developed a conceptual model using Causal Loop Diagram (CLD) for strategic decision-making in Whole Genome Sequencing (WGS) implementation. EXPERT OPINION: The paper demonstrates that SD is well-suited for health policy evaluation challenges and has useful tools for modeling precision oncology and genomic testing. SD's system-oriented modeling captures dynamic and complex interactions within systems using feedback loops. SD models are simple to implement, utilize less data and computational resources, and conduct both exploratory and explanatory analyses over time. If the targeted system has complex interactions and many components, deals with lack of data, and requires interpretability and clinicians' input, SD offers attractive advantages for modeling and evaluating scenarios.

Health Economic Evidence and Modeling Challenges for Liquid Biopsy Assays in Cancer Management: A Systematic Literature Review.

BACKGROUND: Cancer-derived material circulating in the bloodstream and other bodily fluids, referred to as liquid biopsies (LBs), has become an appealing adjunct or alternative to tissue biopsies, showing vital promise in several clinical applications. PURPOSE: A systematic literature review was conducted to (1) summarize the current health economic evidence for LB assays and (2) identify and analyze the studies addressed or reported on the challenges of health economic modeling in precision medicine. METHODS: Relevant studies were identified in the EMBASE, MEDLINE, Cochrane Library, EconLit, and the University of Melbourne Full Text Journal databases from 1 January 2013 to 16 September 2022. Included papers were selected if they were economic evaluations and/or budget impact analyses. RESULTS: A total of 24 studies were included and analyzed, with the majority being full economic evaluations (n = 19, 79.2%). Four studies (16.7%) were health and budget impact analyses, and one study (4.1%) incorporated both an economic evaluation and a budget impact analysis. Cohort-level modeling techniques were the most common approach (n = 16; 80%). LB technologies were cost-effective in 15 studies (75%) considering different biomarkers, cancer types and stages, and economic analyses. These studies evaluated LBs for screening and early detection (66.7%), treatment selection (26.7%), and monitoring treatment response (6.6%). Budget impact analysis results were varied among included studies, with the majority of studies (n = 4; 80%) reporting either cost savings, minimal, or modest budget impact, while one study (20%) reported LBs as an efficient strategy. The reviewed studies often inadequately reported or addressed modeling challenges, such as patient-level processes, the combination of tests and treatments, preferences, and uncertainty. CONCLUSION: LBs could provide a cost-effective approach for treatment selection in lung cancer and aid in the screening and early detection of other cancers, including colorectal, gastric, breast, and brain cancers. This is in comparison with various alternatives, such as the standard of care (SOC) and no screening scenario. However, it is important to mention that in some comparisons, LBs were used in combination with SOC instead of replacing it. Importantly, few studies have pointed toward LBs' cost-effectiveness for monitoring treatment response. Most health and budget impact analyses, especially those focused on lung cancer, suggest potential cost savings or a minimal-to-moderate budget impact. Nevertheless, additional research is needed to ascertain their effectiveness across various stages of lung and colorectal cancer, as well as to address potential modeling challenges. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42022307939.

Shape and size optimization of truss structures by Chaos game optimization considering frequency constraints.

INTRODUCTION: An engineering system consists of properly established activities and put together to achieve a predefined goal. These activities include analysis, design, construction, research, and development. Designing and constructing structural systems, including buildings, bridges, highways, and other complex systems, have been developed over the centuries. However, the evolution of these systems has been prolonged because the overall process is very costly and time-consuming, requiring primary human and material resources to be utilized. One of the options for overcoming these shortcomings is the utilization of metaheuristic algorithms as recently developed intelligent techniques. These algorithms can be utilized as upper-level search techniques for optimization procedures to achieve better results. OBJECTIVES: Shape and size optimization of truss structures are considered in this paper utilizing the Chaos Game Optimization (CGO) as one of the recently developed metaheuristic algorithms. The principles of chaos theory and fractal configuration are considered inspirational concepts. METHODS: For the numerical purpose, the 10-bar, 37-bar, 52-bar, 72-bar, and 120-bar truss structures as four of the benchmark problems in this field are considered as design examples in which the frequency constraints are considered as limits that have to be dealt with during the optimization procedure. Multiple optimization runs are also conducted for having a comprehensive statistical analysis, while a comparative investigation is also conducted with other algorithms in the literature. RESULTS: Based on the results of the CGO and other approaches from the literature, the CGO can provide better and competitive results in dealing with the considered truss design problems. CONCLUSION: In summary, the CGO can provide better solutions in dealing with the considered real-size structural design problems with higher levels of complexity.

Early Identification of Undesirable Outcomes for Transport Accident Injured Patients Using Semi-Supervised Clustering.

Identifying those patient groups, who have unwanted outcomes, in the early stages is crucial to providing the most appropriate level of care. In this study, we intend to find distinctive patterns in health service use (HSU) of transport accident injured patients within the first week post-injury. Aiming those patterns that are associated with the outcome of interest. To recognize these patterns, we propose a multi-objective optimization model that minimizes the k-medians cost function and regression error simultaneously. Thus, we use a semi-supervised clustering approach to identify patient groups based on HSU patterns and their association with total cost. To solve the optimization problem, we introduce an evolutionary algorithm using stochastic gradient descent and Pareto optimal solutions. As a result, we find the best optimal clusters by minimizing both objective functions. The results show that the proposed semi-supervised approach identifies distinct groups of HSUs and contributes to predict total cost. Also, the experiments prove the performance of the multi-objective approach in comparison with single- objective approaches.