Mitigating Voter Attribute Bias for Fair Opinion Aggregation

The aggregation of multiple opinions plays a crucial role in decision-making, such as in hiring and loan review, and in labeling data for supervised learning. Although majority voting and existing opinion aggregation models are effective for simple tasks, they are inappropriate for tasks without objectively true labels in which disagreements may occur. In particular, when voter attributes such as gender or race introduce bias into opinions, the aggregation results may vary depending on the composition of voter attributes. A balanced group of voters is desirable for fair aggregation results but may be difficult to prepare. In this study, we consider methods to achieve fair opinion aggregation based on voter attributes and evaluate the fairness of the aggregated results. To this end, we consider an approach that combines opinion aggregation models such as majority voting and the Dawid and Skene model (D&S model) with fairness options such as sample weighting. To evaluate the fairness of opinion aggregation, probabilistic soft labels are preferred over discrete class labels. First, we address the problem of soft label estimation without considering voter attributes and identify some issues with the D&S model. To address these limitations, we propose a new Soft D&S model with improved accuracy in estimating soft labels. Moreover, we evaluated the fairness of an opinion aggregation model, including Soft D&S, in combination with different fairness options using synthetic and semi-synthetic data. The experimental results suggest that the combination of Soft D&S and data splitting as a fairness option is effective for dense data, whereas weighted majority voting is effective for sparse data. These findings should prove particularly valuable in supporting decision-making by human and machine-learning models with balanced opinion aggregation

ガン ト エイヨウ フリョウ ガン アクエキシツ ショウカキガン オ チュウシン ニ

A majority of patients with cancer, in particularly cancer of digestive organs, shows malnutrition, leading to worse quality of life and occurrence of several complications. Mechanisms of being malnutrition include anorexia, abnormally affected metabolism, hypercytokinemia, chronic loss of blood, and consumption of nutrition by cancer growing. Nutritional states should be evaluated periodically by somatometry, blood chemistry, subjective nutritional assessment（SGA）, and so on. On the basis of the assessment of nutrition, appropriate supplementation of nutrition should be performed. The routs of nutritional supplementation include oral ingestion, tubural feeding, gastrostomy tube, enterostomy tube, peripheral parenteral nutrition and intravenous hyperalimenation. Thus, appropriate nutritional administration in patients with cancer leads to better outcome of cancer treatment

観察研究におけるバイアスの感度解析

要旨あり疫学研究のデザインとデータ解析：最近の理論的展開と実践研究詳

Estimating Treatment Effects Under Heterogeneous Interference

Treatment effect estimation can assist in effective decision-making in e-commerce, medicine, and education. One popular application of this estimation lies in the prediction of the impact of a treatment (e.g., a promotion) on an outcome (e.g., sales) of a particular unit (e.g., an item), known as the individual treatment effect (ITE). In many online applications, the outcome of a unit can be affected by the treatments of other units, as units are often associated, which is referred to as interference. For example, on an online shopping website, sales of an item will be influenced by an advertisement of its co-purchased item. Prior studies have attempted to model interference to estimate the ITE accurately, but they often assume a homogeneous interference, i.e., relationships between units only have a single view. However, in real-world applications, interference may be heterogeneous, with multi-view relationships. For instance, the sale of an item is usually affected by the treatment of its co-purchased and co-viewed items. We hypothesize that ITE estimation will be inaccurate if this heterogeneous interference is not properly modeled. Therefore, we propose a novel approach to model heterogeneous interference by developing a new architecture to aggregate information from diverse neighbors. Our proposed method contains graph neural networks that aggregate same-view information, a mechanism that aggregates information from different views, and attention mechanisms. In our experiments on multiple datasets with heterogeneous interference, the proposed method significantly outperforms existing methods for ITE estimation, confirming the importance of modeling heterogeneous interference

Effects of the anti-RANKL antibody denosumab on joint structural damage in patients with rheumatoid arthritis treated with conventional synthetic disease-modifying antirheumatic drugs (DESIRABLE study): a randomised, double-blind, placebo-controlled phase 3 trial.

ObjectiveTo evaluate the efficacy of denosumab in suppressing joint destruction when added to conventional synthetic disease-modifying antirheumatic drug (csDMARD) therapy in patients with rheumatoid arthritis (RA).MethodsThis was a multi-centre, randomised, double-blind, parallel-group, placebo-controlled phase 3 study in Japan. Patients with RA aged ≥20 years receiving csDMARDs were randomly assigned (1:1:1) to denosumab 60 mg every 3 months (Q3M), denosumab 60 mg every 6 months (Q6M) or placebo. The change in the modified total Sharp score (mTSS) and effect on bone mineral density (BMD) at 12 months was evaluated.ResultsIn total, 654 patients received the trial drugs. Denosumab groups showed significantly less progression of joint destruction. The mean changes in the mTSS at 12 months were 1.49 (95% CI 0.99 to 1.99) in the placebo group, 0.99 (95% CI 0.49 to 1.49) in the Q6M group (p=0.0235) and 0.72 (95% CI 0.41 to 1.03) in the Q3M group (p=0.0055). The mean changes in bone erosion score were 0.98 (95% CI 0.65 to 1.31) in the placebo group, 0.51 (95% CI 0.22 to 0.80) in the Q6M group (p=0.0104) and 0.22 (95% CI 0.09 to 0.34) in the Q3M group (p=0.0001). No significant between-group difference was observed in the joint space narrowing score. The per cent change in lumbar spine (L1-L4) BMD in the placebo, Q6M and Q3M groups were -1.03%, 3.99% (p<0.0001) and 4.88% (p<0.0001). No major differences were observed among safety profiles.ConclusionsDenosumab inhibits the progression of joint destruction, increases BMD and is well tolerated in patients with RA taking csDMARD