The Medical Authority of AI: A Study of AI-enabled Consumer-facing Health Technology

Recently, consumer-facing health technologies such as Artificial Intelligence (AI)-based symptom checkers (AISCs) have sprung up in everyday healthcare practice. AISCs solicit symptom information from users and provide medical suggestions and possible diagnoses, a responsibility that people usually entrust with real-person authorities such as physicians and expert patients. Thus, the advent of AISCs begs a question of whether and how they transform the notion of medical authority in everyday healthcare practice. To answer this question, we conducted an interview study with thirty AISC users. We found that users assess the medical authority of AISCs using various factors including automated decisions and interaction design patterns of AISC apps, associations with established medical authorities like hospitals, and comparisons with other health technologies. We reveal how AISCs are used in healthcare delivery, discuss how AI transforms conventional understandings of medical authority, and derive implications for designing AI-enabled health technology

Achievement goals affect metacognitive judgments

The present study examined the effect of achievement goals on metacognitive judgments, such as judgments of learning (JOLs) and metacomprehension judgments, and actual recall performance. We conducted five experiments manipulating the instruction of achievement goals. In each experiment, participants were instructed to adopt mastery-approach goals (i.e., develop their own mental ability through a memory task) or performance-approach goals (i.e., demonstrate their strong memory ability through getting a high score on a memory task). The results of Experiments 1 and 2 showed that JOLs of word pairs in the performance-approach goal condition tended to be higher than those in the mastery-approach goal condition. In contrast, cued recall performance did not differ between the two goal conditions. Experiment 3 also demonstrated that metacomprehension judgments of text passages were higher in the performance-approach goal condition than in the mastery-approach goals condition, whereas test performance did not differ between conditions. These findings suggest that achievement motivation affects metacognitive judgments during learning, even when achievement motivation does not influence actual performance

Contrastive Domain Adaptation for Early Misinformation Detection: A Case Study on COVID-19

Despite recent progress in improving the performance of misinformation detection systems, classifying misinformation in an unseen domain remains an elusive challenge. To address this issue, a common approach is to introduce a domain critic and encourage domain-invariant input features. However, early misinformation often demonstrates both conditional and label shifts against existing misinformation data (e.g., class imbalance in COVID-19 datasets), rendering such methods less effective for detecting early misinformation. In this paper, we propose contrastive adaptation network for early misinformation detection (CANMD). Specifically, we leverage pseudo labeling to generate high-confidence target examples for joint training with source data. We additionally design a label correction component to estimate and correct the label shifts (i.e., class priors) between the source and target domains. Moreover, a contrastive adaptation loss is integrated in the objective function to reduce the intra-class discrepancy and enlarge the inter-class discrepancy. As such, the adapted model learns corrected class priors and an invariant conditional distribution across both domains for improved estimation of the target data distribution. To demonstrate the effectiveness of the proposed CANMD, we study the case of COVID-19 early misinformation detection and perform extensive experiments using multiple real-world datasets. The results suggest that CANMD can effectively adapt misinformation detection systems to the unseen COVID-19 target domain with significant improvements compared to the state-of-the-art baselines.Comment: Accepted to CIKM 202

Defending Substitution-Based Profile Pollution Attacks on Sequential Recommenders

While sequential recommender systems achieve significant improvements on capturing user dynamics, we argue that sequential recommenders are vulnerable against substitution-based profile pollution attacks. To demonstrate our hypothesis, we propose a substitution-based adversarial attack algorithm, which modifies the input sequence by selecting certain vulnerable elements and substituting them with adversarial items. In both untargeted and targeted attack scenarios, we observe significant performance deterioration using the proposed profile pollution algorithm. Motivated by such observations, we design an efficient adversarial defense method called Dirichlet neighborhood sampling. Specifically, we sample item embeddings from a convex hull constructed by multi-hop neighbors to replace the original items in input sequences. During sampling, a Dirichlet distribution is used to approximate the probability distribution in the neighborhood such that the recommender learns to combat local perturbations. Additionally, we design an adversarial training method tailored for sequential recommender systems. In particular, we represent selected items with one-hot encodings and perform gradient ascent on the encodings to search for the worst case linear combination of item embeddings in training. As such, the embedding function learns robust item representations and the trained recommender is resistant to test-time adversarial examples. Extensive experiments show the effectiveness of both our attack and defense methods, which consistently outperform baselines by a significant margin across model architectures and datasets.Comment: Accepted to RecSys 202

Domain Adaptation for Question Answering via Question Classification

Question answering (QA) has demonstrated impressive progress in answering questions from customized domains. Nevertheless, domain adaptation remains one of the most elusive challenges for QA systems, especially when QA systems are trained in a source domain but deployed in a different target domain. In this work, we investigate the potential benefits of question classification for QA domain adaptation. We propose a novel framework: Question Classification for Question Answering (QC4QA). Specifically, a question classifier is adopted to assign question classes to both the source and target data. Then, we perform joint training in a self-supervised fashion via pseudo-labeling. For optimization, inter-domain discrepancy between the source and target domain is reduced via maximum mean discrepancy (MMD) distance. We additionally minimize intra-class discrepancy among QA samples of the same question class for fine-grained adaptation performance. To the best of our knowledge, this is the first work in QA domain adaptation to leverage question classification with self-supervised adaptation. We demonstrate the effectiveness of the proposed QC4QA with consistent improvements against the state-of-the-art baselines on multiple datasets.Comment: Accepted to COLING 202

Unsupervised Domain Adaptation for COVID-19 Information Service with Contrastive Adversarial Domain Mixup

In the real-world application of COVID-19 misinformation detection, a fundamental challenge is the lack of the labeled COVID data to enable supervised end-to-end training of the models, especially at the early stage of the pandemic. To address this challenge, we propose an unsupervised domain adaptation framework using contrastive learning and adversarial domain mixup to transfer the knowledge from an existing source data domain to the target COVID-19 data domain. In particular, to bridge the gap between the source domain and the target domain, our method reduces a radial basis function (RBF) based discrepancy between these two domains. Moreover, we leverage the power of domain adversarial examples to establish an intermediate domain mixup, where the latent representations of the input text from both domains could be mixed during the training process. Extensive experiments on multiple real-world datasets suggest that our method can effectively adapt misinformation detection systems to the unseen COVID-19 target domain with significant improvements compared to the state-of-the-art baselines

Exploring and Promoting Diagnostic Transparency and Explainability in Online Symptom Checkers

Online symptom checkers (OSC) are widely used intelligent systems in health contexts such as primary care, remote healthcare, and epidemic control. OSCs use algorithms such as machine learning to facilitate self-diagnosis and triage based on symptoms input by healthcare consumers. However, intelligent systems’ lack of transparency and comprehensibility could lead to unintended consequences such as misleading users, especially in high-stakes areas such as healthcare. In this paper, we attempt to enhance diagnostic transparency by augmenting OSCs with explanations. We first conducted an interview study (N=25) to specify user needs for explanations from users of existing OSCs. Then, we designed a COVID-19 OSC that was enhanced with three types of explanations. Our lab-controlled user study (N=20) found that explanations can significantly improve user experience in multiple aspects. We discuss how explanations are interwoven into conversation flow and present implications for future OSC designs

AACC: Asymmetric Actor-Critic in Contextual Reinforcement Learning

Reinforcement Learning (RL) techniques have drawn great attention in many challenging tasks, but their performance deteriorates dramatically when applied to real-world problems. Various methods, such as domain randomization, have been proposed to deal with such situations by training agents under different environmental setups, and therefore they can be generalized to different environments during deployment. However, they usually do not incorporate the underlying environmental factor information that the agents interact with properly and thus can be overly conservative when facing changes in the surroundings. In this paper, we first formalize the task of adapting to changing environmental dynamics in RL as a generalization problem using Contextual Markov Decision Processes (CMDPs). We then propose the Asymmetric Actor-Critic in Contextual RL (AACC) as an end-to-end actor-critic method to deal with such generalization tasks. We demonstrate the essential improvements in the performance of AACC over existing baselines experimentally in a range of simulated environments

Traditional Chinese medicine combined with hormone therapy to treat premature ovarian failure: a meta-analysis of randomized controlled trials

Background: This meta-analysis aimed to provide critically estimated evidence for the advantages and disadvantages of Chinese herbal medicines used for premature ovarian failure (POF), which could provide suggestions for rational treatments.Materials and Methods: The databases searched included MEDLINE, EMBASE, CNKI, VIP, China Dissertation Database, China Important Conference Papers Database, and online clinical trial registry websites. Published and unpublished randomized controlled trials of traditional Chinese medicine (TCM) combined with hormone therapy (HT) and HT alone for POF were assessed up to December 30, 2015. Two authors extracted data and assessed trial quality independently using Cochrane systematic review methods. Meta-analysis was used to quantitatively describe serum hormone levels and Kupperman scores associated with perimenopause symptoms.Results: Seventeen randomized controlled trials involving 1352 participants were selected. Compared with HT alone, although no significant effects were observed in the levels of luteinizing hormone, therapy with TCM combined with HT compared to HT alone effectively altered serum hormone levels of follicle stimulating hormone (P<0.01) and estradiol (P < 0.01), and improved Kupperman index scores (P< 0.01).Conclusions: The reported favorable effects of TCM combined with HT for treating POF patients are better than HT alone.However,the beneficial effects derived from this combination therapy cannot be viewed conclusive.In order to better support the clinical use, more rigorously designed trials are required to provide.Keywords: Traditional Chinese medicine, Hormone therapy, Premature ovarian failure, Meta-analysi

Size-dependent spin-reorientation transition in Nd2Fe14B nanoparticles

Nd2Fe14B magnetic nanoparticles have been successfully produced using a surfactant-assisted ball milling technique. The nanoparticles with different size about 6, 20 and 300 nm were obtained by a size-selection process. Spin-reorientation transition temperature of the NdFeB nanoparticles was then determined by measuring the temperature dependence of DC and AC magnetic susceptibility. It was found that the spin-reorientation transition temperature (Tsr) of the nanoparticles is strongly size dependent, i.e., Tsr of the 300 nm particles is lower than that of raw materials and a significant decrease was observed in the 20 nm particles