An Evidence Combination Rule Based on New Weight Assignment Scheme

Conflicting evidence and fuzzy evidence have a significant impact on the results of evidence combination in the application of evidence theory. However, the existing weight assignment methods can hardly reflect the significant influence of fuzzy evidence on the combination results. Therefore, a new method for assigning evidence weights and the corresponding combination rule are proposed. The proposed weight assignment method strengthens the consideration of fuzzy evidence and introduces the Wasserstein distance to compute the clarity degree of evidence which is an important reference index for weight assignment in the proposed combination rule and can weaken the effect of ambiguous evidence effectively. In the experiments, it's firstly verified that the impact of fuzzy evidence on the combination results is significant; therefore it should be fully considered in the weight assignment process. Then, the proposed combination rule with new weight assignment method is tested on a set of numerical arithmetic and Iris datasets. Compared with four existing methods, the results show that the proposed method has higher decision accuracy, F1 score, better computational convergence, and more reliable fusion results as well.

Product recommendation using online reviews with emotional preferences

PurposeThe purpose of this paper is to develop a novel recommendation method using online reviews with emotional preferences for facilitating online purchase decisions. This leads to better use of information-rich online reviews for providing users with personalized recommendations.Design/methodology/approachA novel method is developed for producing personalized recommendations in online purchase decision-making. Such a method fuses the belief structure and the Shapley function together to effectively deal with the emotional preferences in online reviews and adequately tackle the interaction existent between product criteria with the use of a modified combination rule for making better online recommendations for making online purchase decisions.FindingsAn example is presented for demonstrating the applicability of the method for facilitating online purchase. The results show that the recommendation using the proposed method can effectively improve customer satisfaction with better purchase decisions.Research limitations/implicationsThe proposed method can better utilize online reviews for satisfying personalized needs of consumers. The use of such a method can optimize interface design, refine customer needs, reduce recommendation errors and provide personalized recommendations.Originality/valueThe proposed method adequately considers the characteristics of online reviews and the personalized needs of customers for providing customers with appropriate recommendations. It can help businesses better manage online reviews for improving customer satisfaction and create greater value for both businesses and customers.

Conflicting evidence combination based on Belief Mover’s Distance

The Dempster-Shafer evidence theory has been extensively used in various applications of information fusion owing to its capability in dealing with uncertain modeling and reasoning. However, when meeting highly conflicting evidence, the classical Dempster’s combination rule may give counter-intuitive results. To address this issue, we propose a new method in this work to fuse conflicting evidence. Firstly, a new evidence distance metric, named Belief Mover’s Distance, which is inspired by the Earth Mover’s Distance, is defined to measure the difference between two pieces of evidence. Subsequently, the credibility weight and distance weight of each piece of evidence are computed according to the Belief Mover’s Distance. Then, the final weight of each piece of evidence is generated by unifying these two weights. Finally, the classical Dempster’s rule is employed to fuse the weighted average evidence. Several examples and applications are presented to analyze the performance of the proposed method. Experimental results manifest that the proposed method is remarkably effective in comparison with other methods.

A Novel Method for Conflict Data Fusion Using an Improved Belief Divergence Measure in Dempster–Shafer Evidence Theory

Dempster–Shafer (D-S) evidence theory plays an important role in multisource data fusion. Due to the nature of the Dempster combination rule, there can be counterintuitive results when fusing highly conflicting evidence data. To date, conflict management in D-S evidence theory is still an open issue. Inspired by evidence modification considering internal indeterminacy and external support, a novel method for conflict data fusion is proposed based on an improved belief divergence, evidence distance, and belief entropy. First, an improved belief divergence measure is defined to characterize the discrepancy and conflict between bodies of evidence (BOEs). Second, evidence credibility is generated to describe the external support based on the complementary advantages of the improved belief divergence and evidence distance. Third, belief entropy is utilized to quantify the internal indeterminacy and further determine evidence weight. Lastly, the classical Dempster combination rule is applied to fuse the BOEs modified by their credibility degrees and weights. As the results of numerical examples and an application show, the proposed divergence measure can overcome the invalidity of the existing measures in some special cases. Additionally, the proposed fusion method recognizes the correct target with the highest belief value of 98.96%, which outperforms other related methods in conflict management. The proposed fusion method also displays better convergence, validity, and robustness.

Algorithm to derive progression-based lines of therapy from a real-world non-small cell lung cancer (NSCLC) dataset.

e21172 Background: Analysis of Real World Data (RWD) from Electronic Health Records (EHR) for applications such as Health Economics and Outcomes Research (HEOR) or regulatory submissions requires identification of the lines of therapy (LoT) patients have received. LoTs are typically not captured in EHR and must be manually abstracted. As the use of RWD increases, there is a growing need to create algorithms that can work on RWD to extract LoT information in an automated manner with high accuracy. We present here the results of such an algorithm created on NSCLC RWD. Methods: 10950 advanced NSCLC patients from the ConcertAI Oncology RWD database who had received anti-neoplastic treatment after advanced diagnosis were used to build and validate this algorithm. These data were further enriched by expert nurse curators to fill in missing oral drug information and identify progression events. We developed a progression-based LoT (pLoT) model that identified LoT changes in sync with tumor progressions. If patients received multiple regimens before progression they were captured as nested regimens within the LoT. The algorithm uses complex rules to define combination of drugs as regimens (combination rule), identify resumption of regimens (gap rule) or dropping of drugs from regimens as new lines and to handle noisiness in RWD etc. Results: The LoT model accurately captures line changes triggered by progression events as well as any nested regimen changes due to adverse events etc. Patient level validation of LoT was carried out by clinical experts using an in-house tool and found to be consistent with literature & individual drug data. Cohort level analysis of top 3 combinations of therapies used in 1st & 2nd line treatment between 2015-2020 (8200 patients) are shown in Table. Sensitivity analysis on the combination rule showed that this parameter can be changed between 28-33 days without significantly impacting the LoT output (<1% impact). We use a 30 day combination rule as the default. Similarly, the gap rule parameter is quite robust and does not show significant variation between 45 – 90 days (<2% impact). We use 63 days. Conclusions: We have developed a robust algorithm to derive pLoT on RWD at scale assuming availability of curated progression data which can be used to support use cases such as HEOR, clinical development and regulatory submissions. pLoT is better suited for outcomes analysis compared to regimen based LoT since it distinguishes changes in treatment due to progression events from changes due to toxicity, drug availability, etc., and allows analysis on a more homogeneous patient population relating to their past clinical experience. [Table: see text]