Navigating health questions can be daunting in the modern information landscape. Large language models (LLMs) may provide tailored, accessible information, but also risk being inaccurate, biased or misleading. We present insights from 5 mixed-methods studies (total N=261), examining how people interact with LLMs for their own health questions. Qualitative studies revealed the importance of context-seeking in conversational AIs to elicit specific details a person may not volunteer or know to share. Context-seeking by LLMs was valued by participants, even if it meant deferring an answer for several turns. Incorporating these insights, we developed a “Wayfinding AI” to proactively solicit context. In two randomized, blinded studies, participants rated the Wayfinding AI as more helpful, relevant, and tailored to their concerns compared to a baseline AI. These results demonstrate the strong impact of proactive context-seeking on conversational dynamics, and suggest design patterns for conversational AI to help navigate health topics.

Distributed Multi-Agent Path Finding (MAPF) integrated with Multi-Agent Reinforcement Learning (MARL) has emerged as a prominent research focus, enabling real-time cooperative decision-making in partially observable environments through inter-agent communication. However, due to insufficient collaborative and perceptual capabilities, existing methods are inadequate for scaling across diverse environmental conditions. To address these challenges, we propose PC2P, a novel distributed MAPF method derived from a Q-learning-based MARL framework. Initially, we introduce a personalized-enhanced communication mechanism based on dynamic graph topology, which ascertains the core aspects of "who" and "what" in interactive process through three-stage operations: selection, generation, and aggregation. Concurrently, we incorporate local crowd perception to enrich agents’ heuristic observation, thereby strengthening the model’s guidance for effective actions via the integration of static spatial constraints and dynamic occupancy changes. To resolve extreme deadlock issues, we propose a region-based deadlock-breaking strategy that leverages expert guidance to implement efficient coordination within confined areas. Experimental results demonstrate that PC2P achieves superior performance compared to state-of-the-art distributed MAPF methods in varied environments. Ablation studies further confirm the effectiveness of each module for overall performance.

Dairy owners invest heavily to keep their animals healthy. There is good reason to hope that technologies such as computer vision and artificial intelligence (AI) could reduce costs, yet obstacles arise when adapting these advanced tools to farming environments. In this work, we applied AI tools to dairy cow teat localization and teat shape classification, obtaining a model that achieves a mean average precision of 0.783. This digital twin-driven approach is intended as a first step towards automating and accelerating the detection and treatment of hyperkeratosis, mastitis, and other medical conditions that significantly burden the dairy industry.

Shared decision making (SDM) plays a vital role in clinical practice guidelines, fostering enduring therapeutic communication and patient-clinician relationships. Previous research indicates that active patient participation in decision-making improves satisfaction and treatment outcomes. However, medical decision-making can be intricate and multifaceted. To help make SDM more accessible, we designed a patient-centered Artificial Intelligence (AI) SDM system for older adult cancer patients who lack high health literacy to become more involved in the clinical decision-making process and to improve comprehension toward treatment outcomes. We conducted a pilot feasibility study through 12 preliminary interviews followed by 25 usability testing interviews after the system development, with older adult cancer survivors and clinicians. Results indicated promise in the AI system’s ability to enhance SDM, providing personalized healthcare experiences and education for cancer patients. Clinician responses also provided useful suggestions for SDM’s new design and research opportunities in mitigating medical errors and improving clinical efficiency.

Sequential recommendation aims to choose the most suitable items for a user at a specific timestamp given historical behaviors. Existing methods usually model the user behavior sequence based on transition-based methods such as Markov chain. However, these methods also implicitly assume that the users are independent of each other without considering the influence between users. In fact, this influence plays an important role in sequence recommendation since the behavior of a user is easily affected by others. Therefore, it is desirable to aggregate both user behaviors and the influence between users, which are evolved temporally and involved in the heterogeneous graph of users and items. In this article, we incorporate dynamic user-item heterogeneous graphs to propose a novel sequential recommendation framework. As a result, the historical behaviors as well as the influence between users can be taken into consideration. To achieve this, we first formalize sequential recommendation as a problem to estimate conditional probability given temporal dynamic heterogeneous graphs and user behavior sequences. After that, we exploit the conditional random field to aggregate the heterogeneous graphs and user behaviors for probability estimation and employ the pseudo-likelihood approach to derive a tractable objective function. Finally, we provide scalable and flexible implementations of the proposed framework. Experimental results on three real-world datasets not only demonstrate the effectiveness of our proposed method but also provide some insightful discoveries on the sequential recommendation.

Clinical decision support tools (DSTs), powered by Artificial Intelligence (AI), promise to improve clinicians’ diagnostic and treatment decision-making. However, no AI model is always correct. DSTs must enable clinicians to validate each AI suggestion, convincing them to take the correct suggestions while rejecting its errors. While prior work often tried to do so by explaining AI’s inner workings or performance, we chose a different approach: We investigated how clinicians validated each other’s suggestions in practice (often by referencing scientific literature) and designed a new DST that embraces these naturalistic interactions. This design uses GPT-3 to draw literature evidence that shows the AI suggestions’ robustness and applicability (or the lack thereof). A prototyping study with clinicians from three disease areas proved this approach promising. Clinicians’ interactions with the prototype also revealed new design and research opportunities around (1) harnessing the complementary strengths of literature-based and predictive decision supports; (2) mitigating risks of de-skilling clinicians; and (3) offering low-data decision support with literature.

Late fetal growth restriction (FGR) is a common complication of pregnancy characterized by chronic hypoxia. However, late FGR is in a dilemma of the high incidence but low detection rate. Depending on the non-invasiveness and convenient operation, the routine cardiotocography (CTG) allows continuous monitoring fetal heart rate (FHR) to assess fetal intrauterine stockpiling ability. In this paper, we aimed to explore the FHR pattern of late FGR in routine CTG. For analysis, the FHR features were acquired using routine CTG in a population of 160 healthy and 102 late FGR fetuses published in IEEE Dataport. First, we explored the relationships among FHR features and their importance on late FGR assessment by utilizing hypothesis testing, principal component analysis (PCA) and Spearman correlation analysis. Second, we presented a regression coefficient-based backward-stepwise-selection of association rules analysis (ARA) called backward-stepwise Max-R2Apriori ARA, to find the optimum itemset that helps diagnose late FGRs from healthy fetuses. The hypothesis testing, PCA and Spearman correlation analysis found eight FHR features were highly relevant to the late FGR. Moreover, the backward-stepwise Max-R2 Apriori ARA validated the correlation and interpretation about FHR features of late FGR. In conclusion, the analysis results are consistent with clinical knowledge on late FGR and help screen late FGR in antepartum fetal monitoring.

Existing leading code comment generation approaches with the structure-to-sequence framework ignores the type information of the interpretation of the code, e.g., operator, string, etc.However, introducing the type information into the existing framework is non-trivial due to the hierarchical dependence among the type information.In order to address the issues above, we propose a Type Auxiliary Guiding encoder-decoder framework for the code comment generation task which considers the source code as an N-ary tree with type information associated with each node.Specifically, our framework is featured with a Typeassociated Encoder and a Type-restricted Decoder which enables adaptive summarization of the source code.We further propose a hierarchical reinforcement learning method to resolve the training difficulties of our proposed framework.Extensive evaluations demonstrate the state-of-the-art performance of our framework with both the auto-evaluated metrics and case studies.

MOTIVATION: Synthetic lethality (SL) is a promising form of gene interaction for cancer therapy, as it is able to identify specific genes to target at cancer cells without disrupting normal cells. As high-throughput wet-lab settings are often costly and face various challenges, computational approaches have become a practical complement. In particular, predicting SLs can be formulated as a link prediction task on a graph of interacting genes. Although matrix factorization techniques have been widely adopted in link prediction, they focus on mapping genes to latent representations in isolation, without aggregating information from neighboring genes. Graph convolutional networks (GCN) can capture such neighborhood dependency in a graph. However, it is still challenging to apply GCN for SL prediction as SL interactions are extremely sparse, which is more likely to cause overfitting. RESULTS: In this article, we propose a novel dual-dropout GCN (DDGCN) for learning more robust gene representations for SL prediction. We employ both coarse-grained node dropout and fine-grained edge dropout to address the issue that standard dropout in vanilla GCN is often inadequate in reducing overfitting on sparse graphs. In particular, coarse-grained node dropout can efficiently and systematically enforce dropout at the node (gene) level, while fine-grained edge dropout can further fine-tune the dropout at the interaction (edge) level. We further present a theoretical framework to justify our model architecture. Finally, we conduct extensive experiments on human SL datasets and the results demonstrate the superior performance of our model in comparison with state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION: DDGCN is implemented in Python 3.7, open-source and freely available at https://github.com/CXX1113/Dual-DropoutGCN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this study, the authors propose a novel network architecture to address the problem of removing rain streaks from single images. To strengthen the representational power of the network, they adopt the squeeze‐and‐excitation block in the network. Furthermore, they propose a new network connection called reusing original input (ROI). The ROI connection reuses the original input of the network and can provide more texture details of the background. These details can be useful for the restoration of the image after removing the rain streaks. Batch normalisation is applied to further improve the rain removal performance of the network. Despite the fact that the network is trained on synthetic data, experimental results show that the proposed network has a comparable performance on both synthetic images and real‐world images to the state‐of‐the‐art methods.

Joint extraction of entities and relations is an important task in information extraction.To tackle this problem, we firstly propose a novel tagging scheme that can convert the joint extraction task to a tagging problem.Then, based on our tagging scheme, we study different end-toend models to extract entities and their relations directly, without identifying entities and relations separately.We conduct experiments on a public dataset produced by distant supervision method and the experimental results show that the tagging based methods are better than most of the existing pipelined and joint learning methods.What's more, the end-to-end model proposed in this paper, achieves the best results on the public dataset.

Taking advantage of the large scale corpus on the web to effectively and efficiently mine the topics within texts is an essential problem in the era of big data. We focus on the problem of learning text topic embedding in an unsupervised manner, which enjoys the properties of efficiency and scalability. Text topic embedding represents words and documents in a semantic topic space, in which the words and documents with similar topic will be embedded close to each other. When compared with conventional topic models, which implicitly capture the document-level word co-occurrence patterns, text topic embedding alleviates the data sparsity problem and captures the semantic relevance between different words and documents. To model text topic embedding, we propose a Bidirectional Hierarchical Skip-Gram model (BHSG) based on skip-gram model. BHSG includes two components: semantic generation module to learn semantic relevance between texts and topic enhance module to produce the text topic embedding based on text embedding learned in the former module. We evaluated our method on two kinds of topic-related tasks: text classification and information retrieval. The experimental results on four public datasets and one dataset we provide all demonstrate that our proposed method can achieve a better performance.