While large language models (LLMs) are vital for retrieving relevant information from extensive knowledge bases, they always face challenges, including high costs and issues of credibility. Here, we developed a question answering system focused on human health risk using Retrieval-Augmented Generation (RAG). We first proposed a framework to generate question–answer pairs, resulting in 300 high-quality pairs across six subfields. Subsequently, we created both a Naive RAG and an Advanced RAG-based Question-Answering (Q&A) system. Performance evaluation of the 300 question–answer pairs in individual research subfields demonstrated that the Advanced RAG outperformed traditional LLMs (including ChatGPT and ChatGLM) and Naive RAG. Finally, we integrated the developed module for a single subfield to launch a multi-knowledge base question answering system. Our study represents a novel application of RAG technology and LLMs to optimize knowledge retrieval methods in human health risk assessment.

Nowadays, air pollution is a severe environmental problem in China. To investigate the effects of ambient air pollution on health, a time series analysis of daily outpatient and inpatient visits in 2015 were conducted in Shenzhen (China). Generalized additive model was employed to analyze associations between six air pollutants (namely SO₂, CO, NO₂, O₃, PM₁₀, and PM_2.5) and daily outpatient and inpatient visits after adjusting confounding meteorological factors, time and day of the week effects. Significant associations between air pollutants and two types of hospital visits were observed. The estimated increase in overall outpatient visits associated with each 10 µg/m³ increase in air pollutant concentration ranged from 0.48% (O₃ at lag 2) to 11.48% (SO₂ with 2-day moving average); for overall inpatient visits ranged from 0.73% (O₃ at lag 7) to 17.13% (SO₂ with 8-day moving average). Our results also suggested a heterogeneity of the health effects across different outcomes and in different populations. The findings in present study indicate that even in Shenzhen, a less polluted area in China, significant associations exist between air pollution and daily number of overall outpatient and inpatient visits.

Foodborne disease is a growing public health problem worldwide and imposes a considerable economic burden on hospitals and other healthcare costs. Thus, accurately predicting the propagation of foodborne disease is crucial in preventing foodborne disease outbreaks. Few studies have investigated the dependencies between environmental variables and foodborne disease activity. This study develops a regularization-based eXtreme gradient boosting approach for foodborne disease trend forecasting considering environmental effects to capture dependencies hidden in foodborne disease time series. A real case in Shanghai, China was studied to validate our proposed model along with comparisons to traditional and benchmark algorithms for foodborne disease prediction. Results show that the foodborne disease prediction approach we propose achieves slightly superior performance in terms of one-day-ahead prediction of foodborne disease, and presents more robust prediction for 2-7 days ahead prediction.

The advent of the digital era has transformed E-commerce platforms into critical tools for industry, yet traditional recommendation systems often fall short in the specialized context of the electric power industry. These systems typically struggle with the industry's unique challenges, such as infrequent and high-stakes transactions, prolonged decision-making processes, and sparse data. This research has developed a novel recommendation engine tailored to these specific conditions, such as to handle the low frequency and long cycle nature of Business-to-Business (B2B) transactions. This approach includes algorithmic enhancements to better process and interpret the limited data available, and data pre-processing techniques designed to enrich the sparse datasets characteristic of this industry. This research also introduces a methodological innovation that integrates multi-dimensional data, combining user E-commerce activities, product specifics, and essential non-tendering information. The proposed engine employs advanced machine learning techniques to provide more accurate and relevant recommendations. The results demonstrate a marked improvement over traditional models, offering a more robust and effective tool for facilitating B2B transactions in the electric power industry. This research not only addresses the sector's unique challenges but also provides a blueprint for adapting recommendation systems to other industries with similar B2B characteristics.

Ambient particulate matter (PM) pollution is a leading environmental health threat worldwide. PM with an aerodynamic diameter ≤ 1.0 μm, also known as PM₁, has been implicated in the morbidity and mortality of several cardiorespiratory and cerebrovascular diseases. However, previous studies have mostly focused on analyzing fine PM (PM_2.5) associated with disease metrics, such as emergency department visits and mortality, rather than ultrafine PM, including PM₁. This study aimed to evaluate the association between short-term PM₁ exposure and hospital admissions (HAs) for all-cause diseases, chronic obstructive pulmonary disease (COPD), and respiratory infections (RIs), as well as the associated expenditures, using Beijing as a case study. Here, based on air pollution and hospital admission data in Beijing from 2015 to 2017, we performed a time-series analysis and meta-analysis. It was found that a 10 μg/m³ increase in the PM₁ concentration significantly increased all-cause disease HAs by 0.07% (95% Confidence Interval (CI): [0, 0.14%]) in Beijing between 2015 and 2017, while the COPD and RI-related HAs were not significantly associated with short-term PM₁ exposure. Meanwhile, we estimated the attributable number of HAs and hospital expenditures related to all-cause diseases. This study revealed that an average of 6644 (95% CI: [351, 12,917]) cases of HAs were attributable to ambient PM₁, which was estimated to be associated with a 106 million CNY increase in hospital expenditure annually (95% CI: [5.6, 207]), accounting for 0.32% (95% CI: [0.02, 0.62%]) of the annual total expenses. The findings reported here highlight the underlying impact of ambient PM pollution on health risks and economic burden to society and indicate the need for further policy actions on public health.