The problem of interpreting or aggregating multiple rankings is common to many real-world applications. Perhaps the simplest and most common approach is a weighted rank aggregation, wherein a (convex) weight is applied to each input ranking and then ordered. This paper describes a new tool for visualizing and displaying ranking information for the weighted rank aggregation method. Traditionally, the aim of rank aggregation is to summarize the information from the input rankings and provide one final ranking that hopefully represents a more accurate or truthful result than any one input ranking. While such an aggregated ranking is, and clearly has been, useful to many applications, it also obscures information. In this paper, we show the wealth of information that is available for the weighted rank aggregation problem due to its structure. We apply weight set decomposition to the set of convex multipliers, study the properties useful for understanding this decomposition, and visualize the indifference regions. This methodology reveals information-that is otherwise collapsed by the aggregated ranking-into a useful, interpretable, and intuitive decision support tool. Included are multiple illustrative examples, along with heuristic and exact algorithms for computing the weight set decomposition.
Purpose: xDECIDE is a clinical decision support system, accessed through a web portal and powered by a "Human-AI Team", that offers oncology healthcare providers a set of treatment options personalized for their cancer patients, and provides outcomes tracking through an observational research protocol. This article describes the xDECIDE process and the AI-assisted technologies that ingest semi-structured electronic medical records to identify and then standardize clinico-genomic features, generate a structured personal health record (PHR), and produce ranked treatment options based on clinical evidence, expert insights, and the real world evidence generated within the system itself. Method: Patients may directly enroll in the IRB-approved pan-cancer XCELSIOR registry (NCT03793088). Patient consent permits data aggregation, continuous learning from clinical outcomes, and sharing of limited datasets within the research team. Assisted by numerous AI-based technologies, the xDECIDE team aggregates and processes patients' electronic medical records, and applies multiple levels of natural language processing (NLP) and machine learning to generate a structured case summary and a standardized list of patient features. Next a ranked list of treatment options is created by an ensemble of AI-based models, called xCORE. The output of xCORE is reviewed by molecular pharmacologists and expert oncologists in a virtual tumor board (VTB). Finally a report is produced that includes a ranked list of treatment options and supporting scientific and medical rationales. Treating physicians can use an interactive portal to view all aspects of these data and associated reports, and to continuously monitor their patients' information. The xDECIDE system, including xCORE, is self-improving; feedback improves aspects of the process through machine learning, knowledge ingestion, and outcomes-directed process improvement. Results: At the time of writing, over 2,000 patients have enrolled in XCELSIOR, including over 650 with CNS cancers, over 300 with pancreatic cancer, and over 100 each with ovarian, colorectal, and breast cancers. Over 150 VTBs of CNS cancer patients and ~100 VTBs of pancreatic cancer patients have been performed. In the course of these discussions, ~450 therapeutic options have been discussed and over 2,000 consensus rationales have been delivered. Further, over 500 treatment rationale statements ("rules") have been encoded to improve algorithm decision making between similar therapeutics or regimens in the context of individual patient features. We have recently deployed the xCORE AI-based treatment ranking algorithm for validation in real-world patient populations. At present approximately 15 patients are processed each week via the full xDECIDE process, including xCORE, under the continuing oversight of experts and VTBs. Conclusion: Clinical decision support through xDECIDE is available for oncologists to utilize in their standard practice of medicine by enrolling a patient in the XCELSIOR trial and accessing xDECIDE through its web portal. This system can help to identify potentially effective treatment options individualized for each patient, based on sophisticated integration of real world evidence, human expert knowledge and opinion, and scientific and clinical publications and databases.
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