Research Proposal: Preference Acquisition through Reconciliation of Inconsistencies

The quality of performance of a decision-support system (or an expert system) is determined to a large extent by its underlying preference model (or knowledge base). The difficulties in preference and knowledge acquisition make them a major focus of current research in decision-support and expert systems. Researchers have used various concepts to develop promising acquisition techniques. One of the concepts used is knowledge maintenence where the knowledge base is changed in response to incorrect or inadequate performance by the expert system. This dissertation investigates a preference acquisition technique based on the reconciliation of inconsistencies between the preference model and the decision maker by allowing the decision maker to modify the preference model interactively. The technique can be used in the class of decision-support systems which objectively evaluate competing plans and select the best plan. The technique will be implemented in the domain of evaluating three-dimensional (3-D) radiation treatment plans. Another major aim of the dissertation is to develop a clinically-relevant objective plan-evaluation model for 3-D radiation treatment plans, and to build a clinical decision-support system to assist in that task using the new preference acquisition method

Exact Dominance without Search in Decision Trees

In order to improve understanding of how planning and decision analysis relate, we propose a hybrid model containing concepts from both. This model is comparable to [Hartman90], with slightly more detail. Dominance is simple concept in decision theory. In a restricted version of our model, we give conditions under which dominance can be detected without search: that is, it can be used as a pruning strategy to avoid growing large trees. This investigation follows the lead of [Wellman87]. The conditions seem hard to meet, but may nevertheless be useful in forward-chaining situations without focus, such as [Breese87]. It may be possible to extend this work to produce better heuristic pruning based on inexact dominance and heuristic ability. Mainly, we contribute a detailed study of particular concept in a hybrid model that is the most detailed to date, further clarifying the relation between the two main paradigms for reasoning about preference among actions

759–5 Use of an Interactive Electronic Whiteboard to Teach Clinical Cardiology Decision Analysis to Medical Students

We used innovative state-of-the-art computer and collaboration technologies to teach first-year medical students an analytic methodology to solve difficult clinical cardiology problems to make informed medical decisions. Clinical examples included the decision to administer thrombolytic therapy considering the risk of hemorrhagic stroke, and activity recommendations for athletes at risk for sudden death. Students received instruction on the decision-analytic approach which integrates pathophysiology, treatment efficacy, diagnostic test interpretation, health outcomes, patient preferences, and cost-effectiveness into a decision-analytic model.The traditional environment of a small group and blackboard was significantly enhanced by using an electronic whiteboard, the Xerox LiveBoard™. The LiveBoard features an 80486-based personal computer, large (3’×4’) display, and wireless pens for input. It allowed the integration of decision-analytic software, statistical software, digital slides, and additional media. We developed TIDAL (Team Interactive Decision Analysis in the Large-screen environment), a software package to interactively construct decision trees, calculate expected utilities, and perform one- and two-way sensitivity analyses using pen and gesture inputs. The Live Board also allowed the novel incorporation of Gambler, a utility assessment program obtained from the New England Medical Center. Gambler was used to obtain utilities for outcomes such as non-disabling hemorrhagic stroke. The interactive nature of the LiveBoard allowed real-time decision model development by the class, followed by instantaneous calculation of expected utilities and sensitivity analyses. The multimedia aspect and interactivity were conducive to extensive class participation.Ten out of eleven students wanted decision-analytic software available for use during their clinical years and all students would recommend the course to next year's students. We plan to experiment with the electronic collaboration features of this technology and allow groups separated by time or space to collaborate on decisions and explore the models created

The impact of immediate breast reconstruction on the time to delivery of adjuvant therapy: the iBRA-2 study

Background: Immediate breast reconstruction (IBR) is routinely offered to improve quality-of-life for women requiring mastectomy, but there are concerns that more complex surgery may delay adjuvant oncological treatments and compromise long-term outcomes. High-quality evidence is lacking. The iBRA-2 study aimed to investigate the impact of IBR on time to adjuvant therapy. Methods: Consecutive women undergoing mastectomy ± IBR for breast cancer July–December, 2016 were included. Patient demographics, operative, oncological and complication data were collected. Time from last definitive cancer surgery to first adjuvant treatment for patients undergoing mastectomy ± IBR were compared and risk factors associated with delays explored. Results: A total of 2540 patients were recruited from 76 centres; 1008 (39.7%) underwent IBR (implant-only [n = 675, 26.6%]; pedicled flaps [n = 105,4.1%] and free-flaps [n = 228, 8.9%]). Complications requiring re-admission or re-operation were significantly more common in patients undergoing IBR than those receiving mastectomy. Adjuvant chemotherapy or radiotherapy was required by 1235 (48.6%) patients. No clinically significant differences were seen in time to adjuvant therapy between patient groups but major complications irrespective of surgery received were significantly associated with treatment delays. Conclusions: IBR does not result in clinically significant delays to adjuvant therapy, but post-operative complications are associated with treatment delays. Strategies to minimise complications, including careful patient selection, are required to improve outcomes for patients

Preference Acquisition through Reconciliation of Inconsistencies

The quality of performance of a decision-support system (or an expert system) is determined to a large extent by its underlying preference model (or knowledge base). The difficulties in preference and knowledge acquisition make them a major focus of current research in decision-support and expert systems. Researchers have used various concepts to develop promising acquisition techniques. One of the concepts used is knowledge maintenance where the knowledge base is changed in response to incorrect or inadequate performance by the expert system. This dissertation investigates a preference acquisition technique based on the reconciliation of inconsistencies between the preference model and the decision maker by allowing the decision maker to modify the preference model interactively. The technique can be used in the class of decision-support systems which objectively evaluate competing plans and select the best plan. The technique will be implemented in the domain of evaluating three-dimens..

Clinical Decision-Support Systems in Radiation Therapy

... This paper reviews eleven prototype systems developed for target volume delineation, treatment planning, treatment plan evaluation, and treatment machine diagnosis. The advent of three-dimensional (3D) conformal radiation therapy (CRT) provides radiation oncologists with the opportunity to consider innovative beam arrangements which were not possible in two-dimensional class solutions. The difficulty of manually generating the thousands of clinically plausible 3D treatment plans calls for the use of decision-support systems to generate them automatically. The large data sets generated in 3D CRT make manual treatment plan evaluation difficult, and call for the use of decision-support systems for objective radiation treatment plan evaluation. Computer-based optimization of 3D CRT can be then performed by combining the systems for automatic plan generation and objective plan evaluation