Suchergebnisse

Climate change has been a hotspot of scientific research in the world for decades, which caused serious effects of agriculture, water resources, ecosystem, environment, human health and so on. In China, drought accounts for almost 50 % of the total loss among all the meteorological disasters. In this article the interpolated and corrected precipitation of one GCM (HadGEM2-ES) output under four emission scenarios (RCP2.6, 4.5, 6.0, 8.5) were used to analyze the drought. The standardized precipitation index (SPI) calculated with these data was used to assess the climate change impact on droughts from meteorological perspectives. Based on five levels of SPI, an integrated index of drought hazard (IIDH) was established, which could explain the frequency and intensity of meteorological drought in different regions. According to yearbooks of different provinces, 15 factors have been chosen which could represent the impact of drought on human being, crops, water resources and economy. Exposure index, sensitivity index and adaptation index have been calculated in almost 2400 counties and vulnerability of drought has been evaluated. Based on hazard and vulnerability evaluation of drought, risk assessment of drought in China under the RCP2.6, 4.5, 6.0, 8.5 emission scenarios from 2016 to 2050 has been done. Results from such a comprehensive study over the whole country could be used not only to inform on potential impacts for specific sectors but also can be used to coordinate adaptation/mitigation strategies among different sectors/regions by the central government.

Given an existing stock allocation in an inventory system, it is often necessary to perform reallocation over multiple time points to address inventory imbalance and maximize availability. In this paper, we focus on the situation where there are two opportunities to perform reallocation within a replenishment cycle. We derive a mathematical model to determine when and how to perform reallocation. Furthermore, we consider the extension of this model to the situation allowing an arbitrary number of reallocations. Experimental results show that the two-reallocation approach achieves better performance compared with the single-reallocation approach found in the literature. We also illustrate how to apply the proposed model to design cost-optimal periodic resupply policies.

ObjectiveElectronic medical records (EMR) are widely available to complement administrative data-based disease surveillance and healthcare performance evaluation. Defining conditions from EMR is labour-intensive, requiring advanced medical informatics knowledge, and is challenging without effective data extraction tools. This study developed a high-throughput pipeline to detect diseases in EMRs.
MethodsWe developed a pipeline that leverages a generative large language model (LLM) to analyze, understand, and interpret EMR notes by following clinical experts' designed prompts. The pipeline was applied to detect diabetes, hypertension, and acute myocardial infarction (AMI) from the EMRs for a cardiac patient cohort in Calgary, Canada. The performance was compared against clinician-validated diagnoses as the reference standard.
ResultsThe cohort consisted of 3,413 patients with 551,095 clinical notes. The prevalence was 27.8%, 66.3%, and 54.3% for diabetes, hypertension, and AMI, respectively. The performance for detecting conditions varied: diabetes had 90.5% sensitivity, 83% specificity, and 67% positive predictive value (PPV); hypertension had 94.2% sensitivity, 30.2% specificity, and 73.8% PPV; and AMI had 86.4% sensitivity, 61% specificity, and 75.3% PPV. The monthly prevalence trends between the detected cases and reference standard showed similar patterns.
ConclusionThe proposed pipeline demonstrated reasonable accuracy and high efficiency in disease detection without manually curated labels, indicating the potential for automated real-time disease surveillance using EMRs.
ImplicationVariations of documentation practices in clinical note can impact the detection performance of different diseases. Hence, an automated pipeline integrating LLMs with expert knowledge may improve detection accuracy with reduced labour costs while indicating documentation quality.

ObjectiveCardiovascular diseases (CVD) are the leading cause of mortality and morbidity worldwide. Traditionally, disease surveillance relies on data from surveys, registries, and administrative databases. As medical records undergo global digitization, electronic medical records (EMRs) are emerging as a crucial reservoir of real-world data. However, the extent EMRs are used in CVD surveillance is unknown. We are conducting a scoping review to assess the current state and effectiveness of EMR-based CVD surveillance worldwide.
ApproachFollowing the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for Scoping Reviews, we searched MEDLINE and EMBASE bibliographic databases to capture studies on prevalence, incidence, and trend measurements of CVDs using EMRs. Assessed factors include CVD types, modelling methodologies, data linkages, advantages, disadvantages, challenges, and solutions. Due to the qualitative nature of the review, collected data will be narratively synthesized to present overall perspectives.
ResultsOur search algorithm yielded 11,979 citations, of which 5,886 abstracts were selected for screening, in progress at the time of this submission. The interim results indicate that most eligible reports came from the USA (49%), followed by the UK (13%), China (6%), Spain (4%) and Canada (3%). The most common diseases were coronary artery diseases (29%), followed by hypertension (26%), stroke (21%), and heart failure (15%).
ConclusionsSurveillance of CVD is crucial for prevention and health policy development. While EMRs can be a data source for surveillance, such potential has yet to be fully realized.
ImplicationsThis study will inform existing research challenges and future opportunities of EMR-based CVD surveillance.