Search Results (160)

Early detection of clinical deterioration in emergency department (ED) patients is critical for timely interventions. This study evaluated the predictive performance of the National Early Warning Score (NEWS) parameters using machine learning. We conducted a single-center retrospective observational study including 27,238 adult ED patients admitted to Merano Hospital (Italy) between June 2022 and June 2023. NEWS vital signs were collected at triage, and mortality at 48 h, 7 days, and 30 days was obtained from ED database. Decision tree analysis (CHAID algorithm) was used to identify predictors of mortality; 10-fold cross-validation was applied to avoid overfitting. Mortality was 0.4% at 48 h, 1% at 7 days, and 2.45% at 30 days. For 48-h mortality, oxygen supplementation (FiO₂ >21%) and AVPU = “U” were the strongest predictors, with a maximum risk of 31.6%. For 7-day mortality, SpO₂ was the key predictor, with mortality up to 48.1%. At 30 days, patients with AVPU ≠ A, FiO₂ > 21%, and SpO₂ ≤ 94% had a mortality risk of 66.7%. Decision trees revealed different cut-offs compared to the standard NEWS. This study demonstrated that for ED patients, the NEWS may require some adjustments in both the cut-offs for vital parameters and the methods of collecting these parameters. Full article

Background/Objective: In-hospital cardiac arrest has high incidence and poor survival rates, posing a significant healthcare challenge. It is important to intervene in the hours before the cardiac arrest to prevent poor outcomes. The modified early warning score (MEWS) is a validated tool for identifying a deteriorating patient. It is an aggregate of vital signs and level of consciousness. We retrospectively evaluated MEWS for trends that might predict patient outcomes. Methods: We performed a single-center, one-year, retrospective study. A comprehensive review was conducted for patients aged 18 years and above who experienced a cardiac arrest. Cases that occurred within an intensive care unit, emergency department, during a procedure, or outside the hospital were excluded. A total of 87 cases met our predefined inclusion criteria. We collected data at 12 h, 6 h and 1 h time periods prior to the cardiac arrest. A trend analysis using a linear model with analysis of variance with Bonferroni correction was performed. Results: Out of 87 patients included in the study, 59 (67.8%) had an immediate return of spontaneous circulation (ROSC). Among those who achieved ROSC, 41 (69.5%) died during the admission. Only 20.7% of the patients that sustained a cardiac arrest survived to discharge. A significant increase in the average MEWS was noted from the 12 h period (MEWS = 3.95 ± 2.4) to the 1 h period (MEWS = 5.98 ± 3.5) (p ≤ 0.001) and the 6 h period (4.65 ± 2.6) to the 1 h period (5.98 ± 3.5) (p = 0.023) prior to cardiac arrest. Conclusions: An increase in the MEWS may be a valuable tool in identifying at-risk patients and provides an opportunity to intervene at least 6 h before a cardiac arrest event. Further research is needed to validate the results of our study. Full article

Accurate dengue forecasting is essential for sustainable public health planning, especially in tropical regions where the disease remains a persistent threat. This study evaluates the predictive performance of seven modeling approaches—Seasonal Autoregressive Integrated Moving Average (SARIMA), Extreme Gradient Boosting (XGBoost), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Convolutional LSTM (CNN–LSTM), and a Bayesian spatiotemporal model—using monthly dengue incidence data from 2009 to 2023 in Bandung City, Indonesia. Model performance was assessed using MAE, sMAPE, RMSE, and Pearson’s correlation (R). Among all models, the Bayesian spatiotemporal model achieved the best performance, with the lowest MAE (5.543), sMAPE (62.137), and RMSE (7.482), and the highest R (0.723). While SARIMA and XGBoost showed signs of overfitting, the Bayesian model not only delivered more accurate forecasts but also produced spatial risk estimates and identified high-risk hotspots via exceedance probabilities. These features make it particularly valuable for developing early warning systems and guiding targeted public health interventions, supporting the broader goals of sustainable disease management. Full article

Airborne respiratory viruses (e.g., influenza, respiratory syncytial virus (RSV), and SARS-CoV-2) continue to pose a serious threat to global public health due to their ability to spread through multiple transmission pathways. Among these, aerosol transmission stands out as a key route, particularly in enclosed environments. However, current monitoring systems have major limitations in sensitivity, standardization, and high time resolution. This study provides a summary of the latest information on the monitoring technologies for respiratory virus aerosols. It discusses the technical and ethical challenges in real-world applications. In addition, this study proposes practical solutions and future development pathways. The aim of this study is to provide theoretical support for building a dynamic, precise, and effective early warning system for monitoring variants of airborne respiratory viruses Full article

Termites pose significant threats to the structural integrity of embankments due to their nesting and tunneling behavior, which leads to internal voids, water leakage, or even dam failure. This review systematically classifies and evaluates current termite detection techniques in the context of embankment maintenance, focusing on physical sensing technologies and biological characteristic-based methods. Physical sensing methods enable non-invasive localization of subsurface anomalies, including ground-penetrating radar, acoustic detection, and electrical resistivity imaging. Biological characteristic-based methods, such as electronic noses, sniffer dogs, visual inspection, intelligent monitoring, and UAV-based image analysis, are capable of detecting volatile compounds and surface activity signs associated with termites. The review summarizes key principles, application scenarios, advantages, and limitations of each technique. It also highlights integrated multi-sensor frameworks and artificial intelligence algorithms as emerging solutions to enhance detection accuracy, adaptability, and automation. The findings suggest that future termite detection in embankments will rely on interdisciplinary integration and intelligent monitoring systems to support early warning, rapid response, and long-term structural resilience. This work provides a scientific foundation and practical reference for advancing termite management and embankment safety strategies. Full article

Due to the increasing number of vehicles and the aging population, the vulnerability to sudden medical emergencies among drivers is a growing problem. Events such as heart attack, stroke, and loss of consciousness can occur without warning and endanger everyone on the road. Modern vehicles, equipped with electronic systems, can support real-time driver’s health monitoring through early detection technologies. The existing Driver Monitoring Systems (DMS) in our cars assess behavioral states such as drowsiness and distraction. In the future, DMS will include biometric sensors to monitor vital signs such as heart rate and respiration. By finding predictors of sudden illnesses (SI), such a system will provide valuable time for the driver to react before the strike of a medical event. In this paper, we present our vision for DMS operation with physiological monitoring capabilities. A brief overview of sensor’s types and their locations in the vehicle interior used in the research studies for monitoring the corresponding physiological parameters is presented. A comparative analysis of the advantages and disadvantages of the sensing methods used for physiological monitoring of the driver in real driving scenarios is made. Full article

This study applied supervised machine learning algorithms to macro-fiscal panel data from 20 EU member states (2000–2024) to model and predict fiscal stress episodes in the Eurozone. Conventional frameworks for assessing public debt sustainability often rely on static thresholds and linear dynamics, limiting their ability to capture the complex, non-linear interactions in fiscal data. To address this, we implemented logistic regression, support vector machines, and XGBoost classifiers using core fiscal indicators such as debt-to-GDP ratio, primary balance, GDP growth, interest rates, and inflation. The models were evaluated using time-aware cross-validation, with XGBoost delivering the highest predictive accuracy but showing some signs of overfitting. We highlighted the interpretability of logistic regression and applied SHAP values to enhance transparency in the tree-based models. While limited by using annual data, we discuss the potential value of incorporating real-time or high-frequency fiscal indicators. Our results underscore the practical relevance of AI-enhanced early warning systems for fiscal surveillance and support their integration into institutional monitoring frameworks. Full article

Background/Objectives: In hospital settings, the wide variability of acute and complex chronic conditions—among both adult and pediatric patients—requires advanced approaches to detect early signs of clinical deterioration and the risk of transfer to the intensive care unit (ICU). Nursing diagnoses (NDs), standardized representations of patient responses to actual or potential health problems, reflect nursing complexity. However, most studies have focused on the total number of NDs rather than the individual role each diagnosis may play in relation to outcomes such as ICU transfer. This study aimed to identify and rank the specific NDs most strongly associated with ICU transfers in hospitalized adult and pediatric patients. Methods: A retrospective, monocentric observational study was conducted using electronic health records from an Italian tertiary hospital. The dataset included 42,735 patients (40,649 adults and 2086 pediatric), and sociodemographic, clinical, and nursing data were collected. A random forest model was applied to assess the predictive relevance (i.e., variable importance) of individual NDs in relation to ICU transfers. Results: Among adult patients, the NDs most strongly associated with ICU transfer were Physical mobility impairment, Injury risk, Skin integrity impairment risk, Acute pain, and Fall risk. In the pediatric population, Acute pain, Injury risk, Sleep pattern disturbance, Skin integrity impairment risk, and Airway clearance impairment emerged as the NDs most frequently linked to ICU transfer. The models showed good performance and generalizability, with stable out-of-bag and validation errors across iterations. Conclusions: A prioritized ranking of NDs appears to be associated with ICU transfers, suggesting their potential utility as early warning indicators of clinical deterioration. Patients presenting with high-risk diagnostic profiles should be prioritized for enhanced clinical surveillance and proactive intervention, as they may represent vulnerable populations. Full article

The ST segment of an ECG signal is a feature that changes in the event of cardiac ischemia, a condition that is an early warning sign of myocardial infarction. Being able to monitor this feature in real time would be highly beneficial for preventing recurrent heart attacks. However, to be worn daily, such a monitoring device must be extremely miniaturized, down to the scale of a single integrated circuit. Currently, it is possible to integrate a heart rate detector, but, to our knowledge, no existing work presents a chip capable of detecting ST segment deviation. This is mainly because accurate ST segment measurement requires low-distortion signal processing, as specified in the International Electrotechnical Commission (IEC) standard. At the same time, the system is required to filter out baseline wander, whose frequency components may partially overlap with those of the ST segment. In this study, we relied on wavelet-based analysis and reconstruction to compare several wavelet types. We optimized their hyperparameters to minimize implementation complexity while satisfying the low-distortion constraints. We also propose an ASIC-oriented architecture and evaluate its post-layout performance in terms of area and power consumption. The post-layout results indicate that the Daubechies wavelet db3 offers the best trade-off among the evaluated configurations. It exhibits an area utilization of $1.18$ mm² and a post-layout power consumption of $4.89$ $\mathsf{\mu }$W, while preserving the ST segment in compliance with the IEC standard, thanks in particular to its effective baseline wandering filtering of $6.9$ dB. These results demonstrate the feasibility of embedding automatic ST segment extraction on-chip. Full article

Satellite imagery provides a rich source of information that serves as a comprehensive and synoptic tool for the continuous monitoring of active volcanoes, including those in remote and inaccessible areas. The huge influx of such data requires the development of automated systems for efficient processing and interpretation. Early warning systems, designed to process satellite imagery to identify signs of impending eruptions and monitor eruptive activity in near real-time, are essential for hazard assessment and risk mitigation. Here, we propose a machine learning approach for the automatic classification of pixels in SEVIRI images to detect and characterize the eruptive activity of a volcano. In particular, we exploit a semi-supervised GAN (SGAN) model that retrieves the presence of thermal anomalies, volcanic ash plumes, and meteorological clouds in each SEVIRI pixel, allowing time series plots to be obtained showing the evolution of volcanic activity. The SGAN model was trained and tested using the huge amount of data available on Mount Etna (Italy). Then, it was applied to other volcanoes, specifically, Stromboli (Italy), Tajogaite (Spain), and Nyiragongo (Democratic Republic of the Congo), to assess the model’s ability to generalize. The validation of the model was performed through a visual comparison between the classification results and the corresponding SEVIRI images. Moreover, we evaluate the model performance by calculating three different metrics, namely the precision (correctness of positive predictions), the recall (ability to find all the positive instances), and the F1-score (general model’s accuracy), finding an average accuracy of 0.9. Our approach can be extended to other geostationary satellite data and applied worldwide to characterize volcanic activity, allowing the monitoring of even remote volcanoes that are difficult to reach from the ground. Full article

Dengue virus is the most prevalent arthropod-borne viral disease in humans. Severe dengue, defined by hemorrhagic fever and dengue shock syndrome, can develop quickly in people who have warning indications such as abdominal pain, mucosal bleeding, and a significant decrease in platelet count. Laboratory markers such as hematocrit, platelet count, liver enzymes, and coagulation tests are critical for early diagnosis and prognosis. This retrospective study was carried out from January 2023 to December 2024 at a super-specialty tertiary care hospital. There were 283 adult patients with dengue with warning signs, who were categorized into 102 with platelet transfusion and 181 with no platelet transfusion. Data on patient demographics, clinical history, laboratory values, and radiological findings were systematically obtained from hospital records at the time of admission. Laboratory parameters such as white blood cell (OR = 2.137), hemoglobin (OR = 2.15), aPTT (OR = 5.815), AST²/ALT (OR = 2.431), platelet count (OR = 26.261) and NS1 (OR = 4.279) were found to be significantly associated (p < 0.01) with platelet transfusion. Similarly, an increased prothrombin time (OR = 2.432) contributed to prolonged hospital stays and the presence of ascites (OR = 5.059), gallbladder wall thickening (OR = 4.212), and pleural effusion (OR = 2.917), contributing to the severity of the dengue infection. These significant laboratory markers help with identifying patients with dengue who may develop severe dengue, requiring platelet transfusion, thereby prioritizing patient care and enabling the implementation of targeted interventions. Full article

This research aims to develop mitigation and adaptation strategies for greenhouse gas emissions Thailand in accordance with Climate-Smart Agriculture policies. The research employs a mixed-methods approach, integrating both quantitative and qualitative research as a crucial framework for impact analysis and an early warning tool for the government in achieving sustainability. On the quantitative side, an advanced model called the Longitudinal Mediated Moderation Analysis Based on the Fuzzy Autoregressive Hierarchical Process (LMMA-FAHP) model has been developed. This model meets all validity criteria, shows no signs of spuriousness, and outperforms previous models in terms of performance. It is highly suitable for policy formulation and strategic planning to guide the country’s long-term governance toward achieving net-zero emissions by 2065. The findings indicate that the new scenario policy, with an appropriateness rating of over 80%, includes factors such as the clean technology rate, biogas energy, biofertilizers, organic fertilizers, anaerobic digestion rate, biomass energy, biofertilizer rate, renewable energy rate, green material rate, waste biomass, and organic waste treatments. All indicators demonstrate a high sensitivity level. When the new scenario policy is incorporated into future greenhouse gas emissions forecasts (2025–2065), the research reveals a declining growth rate of emissions, reaching 78.51 Mt CO₂ Eq., with a growth rate of 11.35%, which remains below the carrying capacity threshold (not exceeding 101.25 Mt CO₂ Eq.). Moreover, should the government adopt and integrate these indicators into national governance frameworks, it is projected that greenhouse gas emissions by 2065 could be reduced by as much as 36.65%, significantly exceeding the government’s current reduction target of 20%. This would enable the government to adjust its carbon sequestration strategies more efficiently. Additionally, qualitative research was conducted by engaging stakeholders from the public sector, private sector, and agricultural communities to develop adaptive strategies for future greenhouse gas emissions. If the country follows the research-driven approach outlined in this research, it will lead to effective long-term policy and governance planning, ensuring sustainability for Thailand. Full article

Background/Objectives: Data availability can affect the performance of AI-based early warning scores (EWSs). This study evaluated how the extent of missing data and imputation strategies influence the predictive performance of the VitalCare–Major Adverse Event Score (VC-MAES), an AI-based EWS that uses last observation carried forward and normal-value imputation for missing values, to forecast clinical deterioration events, including unplanned ICU transfers, cardiac arrests, or death, up to 6 h in advance. Methods: We analyzed real-world data from 6039 patient encounters at Keimyung University Dongsan Hospital, Republic of Korea. Performance was evaluated under three scenarios: (1) using only vital signs and age, treating all other variables as missing; (2) reintroducing a full set of real-world clinical variables; and (3) imputing missing values drawn from a distribution within one standard deviation of the observed mean or using Multiple Imputation by Chained Equations (MICE). Results: VC-MAES achieved the area under the receiver operating characteristic curve (AUROC) of 0.896 using only vital signs and age, outperforming traditional EWSs, including the National Early Warning Score (0.797) and the Modified Early Warning Score (0.722). Reintroducing full clinical variables improved the AUROC to 0.918, whereas mean-based imputation or MICE decreased the performance to 0.885 and 0.827, respectively. Conclusions: VC-MAES demonstrates robust predictive performance with limited inputs, outperforming traditional EWSs. Incorporating actual clinical data significantly improved accuracy. In contrast, mean-based or MICE imputation yielded poorer results than the default normal-value imputation, potentially due to disregarding the “informative presence” embedded in missing data patterns. These findings underscore the importance of understanding missingness patterns and employing imputation strategies that consider the decision-making context behind data availability to enhance model reliability. Full article

Skin paraneoplastic syndromes (SPNSs) are a group of disorders that arise as a consequence of cancer but are not directly related to the tumor mass itself. This review aims to provide a comprehensive overview of these syndromes, encompassing their pathophysiology, clinical features, diagnostic approaches, differential diagnosis, and management strategies. These syndromes, which include conditions such as Bazex syndrome, acanthosis nigricans, dermatomyositis, and necrolytic migratory erythema often manifest prior to or concurrently with a cancer diagnosis, serving as potential early warning signs of underlying malignancies. This review delves into the spectrum of SPNSs and their associations with specific cancer types. Special emphasis is placed on the critical role of dermatologists and oncologists in identifying these skin manifestations as potential markers of malignancy. By raising awareness of SPNSs, this paper highlights the pivotal importance of prompt recognition and intervention in reducing cancer-related mortality. Full article

Monitoring for early symptoms is a critical step in preventing pneumoconiosis. The early signs of pneumoconiosis can be characterized by dyspnea, tachypnea, and cough. While traditional sensor-based methods are promising, they necessitate the wearing of devices and confine human physical movements. On the other hand, camera-based methods have issues related to illumination, obstruction, and privacy. Recently, wireless sensing has attracted a significant amount of research attention. Among wireless signals, acoustic signals possess unique advantages for fine-grained sensing due to their low propagation speed in the air and low hardware requirement. In this paper, we propose a system called $P^{3}Warning$ to realize low-cost warnings for potential pneumoconiosis patients in a contactless manner. For the first time, the designed system utilizes the inaudible acoustic signal to monitor early symptoms of pneumoconiosis (i.e., abnormal respiration and cough), leveraging a pair of commercial speaker and microphone. We introduce and address unique technical challenges, such as formulating a delay elimination method to synchronize transceiver signals and providing a search-based strategy to amplify signal variation for accurate and long-distance vital sign sensing. Ultimately, we apply an innovative signal decomposition technique to reconstruct the respiration waveform and extract features for cough detection. Comprehensive experiments were conducted to evaluate $P^{3}Warning$. Experiment results show that it can achieve a robust performance with a median error of 0.39 bpm for abnormal respiration pattern monitoring and an accuracy of 95% for cough detection in total, and support the furthest sensing range of up to 4 m. Full article