A regret theory approach to decision curve analysis: A novel method for eliciting decision makers' preferences and decision-making

<p>Abstract</p> <p>Background</p> <p>Decision curve analysis (DCA) has been proposed as an alternative method for evaluation of diagnostic tests, prediction models, and molecular markers. However, DCA is based on expected utility theory, which has been routinely violated by decision makers. Decision-making is governed by intuition (system 1), and analytical, deliberative process (system 2), thus, rational decision-making should reflect both formal principles of rationality and intuition about good decisions. We use the cognitive emotion of regret to serve as a link between systems 1 and 2 and to reformulate DCA.</p> <p>Methods</p> <p>First, we analysed a classic decision tree describing three decision alternatives: treat, do not treat, and treat or no treat based on a predictive model. We then computed the expected regret for each of these alternatives as the difference between the utility of the action taken and the utility of the action that, in retrospect, should have been taken. For any pair of strategies, we measure the difference in net expected regret. Finally, we employ the concept of acceptable regret to identify the circumstances under which a potentially wrong strategy is tolerable to a decision-maker.</p> <p>Results</p> <p>We developed a novel dual visual analog scale to describe the relationship between regret associated with "omissions" (e.g. failure to treat) vs. "commissions" (e.g. treating unnecessary) and decision maker's preferences as expressed in terms of threshold probability. We then proved that the Net Expected Regret Difference, first presented in this paper, is equivalent to net benefits as described in the original DCA. Based on the concept of acceptable regret we identified the circumstances under which a decision maker tolerates a potentially wrong decision and expressed it in terms of probability of disease.</p> <p>Conclusions</p> <p>We present a novel method for eliciting decision maker's preferences and an alternative derivation of DCA based on regret theory. Our approach may be intuitively more appealing to a decision-maker, particularly in those clinical situations when the best management option is the one associated with the least amount of regret (e.g. diagnosis and treatment of advanced cancer, etc).</p

Regret affects the choice between neoadjuvant therapy and upfront surgery for potentially resectable pancreatic cancer

Background: When treating potentially resectable pancreatic adenocarcinoma, therapeutic decisions are left to the sensibility of treating clinicians who, faced with a decision that post hoc can be proven wrong, may feel a sense of regret that they want to avoid. A regret-based decision model was applied to evaluate attitudes to-ward neoadjuvant therapy versus upfront surgery for potentially resectable pancreatic adenocarcinoma.Methods: Three clinical scenarios describing high-, intermediate-, and low-risk disease-specific mortality after upfront surgery were presented to 60 respondents (20 oncologists, 20 gastroenterologists, and 20 surgeons). Respondents were asked to report their regret of omission and commission regarding neo-adjuvant chemotherapy on a scale between 0 (no regret) and 100 (maximum regret). The threshold model and a multilevel mixed regression were applied to analyze respondents' attitudes toward neo-adjuvant therapy.Results: The lowest regret of omission was elicited in the low-risk scenario, and the highest regret in the high-risk scenario (P &lt; .001). The regret of the commission was diametrically opposite to the regret of omission (P &lt; .001). The disease-specific threshold mortality at which upfront surgery is favored over the neoadjuvant therapy progressively decreased from the low-risk to the high-risk scenarios (P &lt;=.001). The nonsurgeons working in or with lower surgical volume centers (P = .010) and surgeons (P = .018) accepted higher disease-specific mortality after upfront surgery, which resulted in the lower likelihood of adopting neoadjuvant therapy.Conclusion: Regret drives decision making in the management of pancreatic adenocarcinoma. Being a surgeon or a specialist working in surgical centers with lower patient volumes reduces the likelihood of recommending neoadjuvant therapy.(c) 2023 Elsevier Inc. All rights reserved

Patients' values and preferences of the expected efficacy of hip arthroscopy for osteoarthritis : a protocol for a multinational structured interview-based study combined with a randomised survey on the optimal amount of information to elicit preferences

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Predicting Academic Performance: A Systematic Literature Review

The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.Peer reviewe

Control of Autonomous Robot Teams in Industrial Applications

The use of teams of coordinated mobile robots in industrial settings such as underground mining, toxic waste cleanup and material storage and handling, is a viable and reliable approach to solving such problems that require or involve automation. In this thesis, abilities a team of mobile robots should demonstrate in order to successfully perform a mission in industrial settings are identified as a set of functional components. These components are related to navigation and obstacle avoidance, localization, task achieving behaviors and mission planning. The thesis focuses on designing and developing functional components applicable to diverse missions involving teams of mobile robots; in detail, the following are presented: 1. A navigation and obstacle avoidance technique to safely navigate the robot in an unknown environment. The technique relies on information retrieved by the robot\u27s vision system and sonar sensors to identify and avoid surrounding obstacles. 2. A localization method based on Kalman filtering and Fuzzy logic to estimate the robot\u27s position. The method uses information derived by multiple robot sensors such as vision system, odometer, laser range finder, GPS and IMU. 3. A target tracking and collision avoidance technique based on information derived by a vision system and a laser range finder. The technique is applicable in scenarios where an intruder is identified in the patrolling area. 4. A limited lookahead control methodology responsible for mission planning. The methodology is based on supervisory control theory and it is responsible for task allocation between the robots of the team. The control methodology considers situations where a robot may fail during operation. The performance of each functional component has been verified through extensive experimentation in indoor and outdoor environments. As a case study, a warehouse patrolling application is considered to demonstrate the effectiveness of the mission planning component

Application of Emerging Knowledge Discovery Methods in Engineering Education

The purpose of this study is to investigate the application of emerging knowledge discovery methodologies in analyzing student profiles to predict the performance of a student in a course. Knowledge discovery is the research area concerned with analyzing existing information and extracting implicit, previously unknown, hidden and potentially useful knowledge in an automated manner. The discovered knowledge is often represented by a set of rules or mathematical functions which has practical application. This type of knowledge can enable instructors to accommodate each student\u27s learning needs and abilities as well as aid the students in appropriate course selection. In this paper we present a pilot study which demonstrates the analysis of student profiles from 60 students. The methodology used for knowledge discovery is based on Rough Set Theory which combines theories such as fuzzy sets, evidence theory and statistics. The results of the pilot study show that the knowledge discovery methodologies are likely to discover knowledge which may be overlooked using traditional statistical approaches. Our preliminary results indicate that knowledge discovery methodologies can be successfully used in predicting student performance. Based on the experiences gained from this work, specific future research directions and tasks to ensure a successful comprehensive implementation are discussed

Dynamic Task Allocation in Cooperative Robot Teams

In this paper a dynamic task allocation and controller design methodology for cooperative robot teams is presented. Fuzzy logic based utility functions are derived to quantify each robot\'s ability to perform a task. These utility functions are used to allocate tasks in real-time through a limited lookahead control methodology partially based on the basic principles of discrete event supervisory control theory. The proposed controller design methodology accommodates flexibility in task assignments, robot coordination, and tolerance to robot failures and repairs. Implementation details of the proposed methodology are demonstrated through a warehouse patrolling case study