Background Monkeypox (Mpox) has emerged as a growing public health concern, requiring effective tools for early patient risk assessment and clinical decision-making. This study aimed to develop an artificial intelligence (AI)-based machine learning framework using a multilayer perceptron (MLP) neural network to analyze a Mpox dataset collected from 11 non-endemic countries, identify the most significant clinical symptoms, investigate the relationships among travel history, hospitalization, isolation, and infection status, and predict the hospitalization and isolation status of Mpox patients. Methods A total of 365 non-severe Mpox cases from 11 non-endemic countries were included in the analysis provided in publicly available World Health Organization (WHO) reports from May 6 to May 29, 2022. Patients were classified into two groups: Hospitalized and isolated patients. Clinical data were converted to binary dummy variables and demographic and travel-history data were added as features. The data were analyzed using cross-tabulation and descriptive visualization for symptom patterns and relationships between clinical and demographic variables. A supervised AI-based MLP neural network was then developed to predict the status of hospitalization and isolation, and the performance of the model was assessed by accuracy, recall, false omission rate (FOR) and F1 score. Results The symptoms most commonly reported in the analysis included genital ulcers, fever, and lesions. The eleven countries included in the study reported varying symptom patterns, with Argentina showing the highest symptom diversity, with up to four symptoms recorded per case.. The overall training accuracy of the proposed MLP neural network for the prediction of hospitalization was 94.7% and the overall testing accuracy was 90.7%. The model showed good predictive performance for each patient status classification task with an accuracy of 94.2% at training phase and 90.1% at test phase for isolation prediction. Conclusion The proposed AI-based multilayer perceptron (MLP) neural network provides an effective approach for predicting the hospitalization and isolation status of Mpox patients using clinical and demographic variables. Symptom distributions varied across countries, indicating regional heterogeneity in disease presentation.
Background Cardiac arrest (CA) remains a critical medical emergency characterized by high mortality rates despite the advancements in resuscitation and intensive care. The heterogeneous and evolving clinical course of patients with CA hinders early and accurate prognostication. We aimed to construct and externally validate a predictive model for 28-day intensive care unit (ICU) mortality in patients with CA by leveraging time-weighted clinical data and traditional and machine-learning approaches including extreme gradient boosting (XGBoost), gradient boosting with component-wise linear models (Glmboost), random forest (RF), generalized linear models with elastic net regularization (Glmnet), and multivariate Cox regression. Methods We retrospectively analyzed adult patients with CA from three large-scale ICU databases: eICU Collaborative Research Database (training cohort, n = 1,077), Medical Information Mart for Intensive Care IV (MIMIC-IV v3.1), and MIMIC-III Clinical Database CareVue subset v1.4 (combined validation cohort, n = 368). Time-weighted averages (TWA) of clinical parameters—including the Glasgow Coma Scale (GCS), vital signs, and arterial blood gas indices—were computed over the first three ICU days. Variable selection was performed using the least absolute shrinkage and selection operator regression, followed by multivariable Cox regression analysis. Five prognostic models—Glmboost, XGBoost, RF, Glmnet, and Cox regression—were developed and evaluated using C-index, time-dependent area under the curve (AUC), Graf score, and decision curve analysis. Model interpretability and time-varying effects were examined via SHapley Additive exPlanations (SHAP) and Piece-wise exponential additive mixed model (PAMM). Results Nine independent predictors of ICU mortality were identified, with TWA Glasgow Coma Scale (TWA-GCS) (hazard ratio: 0.75, 95% CI: 0.71–0.78, P < 0.01) and duration of mechanical ventilation (hazard ratio: 0.72, 95% CI: 0.69–0.75, P < 0.01) showing the strongest associations. In external validation, the Glmboost model showed the lowest Graf score. SHAP analyses underscored TWA-GCS and duration of mechanical ventilation as the most impactful variables. PAMM analysis revealed an increasingly protective temporal effect of higher TWA-GCS values. Subgroup analysis showed 28-day ICU mortality rates of 76%, 57%, and 32% in patients with low (< 4), moderate (4–10), and high (> 10) TWA-GCS scores, respectively (P < 0.01). Conclusion Incorporating time-weighted clinical data significantly improves ICU mortality prediction in patients with CA. Glmboost showed the best calibration/error-related performance based on Graf score while demonstrating comparable discrimination, with TWA-GCS identified as a key prognostic factor.
Background Pancreatic cancer continues to be one of the most challenging malignancies to diagnose and treat, and it is often detected at an advanced stage owing to asymptomatic early phases and limitations in current diagnostic tools, highlighting the need for improved diagnostic methodologies. Methods We aimed to evaluate the potential for integrating information from thermal liquid biopsy (TLB) thermograms with clinical biomarkers to improve the diagnosis and prognosis of pancreatic cancer. Serum samples from 381 Danish patients with pancreatic cancer and 325 patients referred with non-organ specific signs and symptoms of cancer, but without confirmed cancer after clinical and computed tomography evaluation, were analyzed. TLB thermograms provided information on the partial excess heat capacity of the serum as a function of temperature. Three classification models were constructed using machine learning algorithms based on variable selection with penalization, applying cross-validation and resampling techniques: iClin Model (age, Eastern Cooperative Oncology Group Performance Status, carbohydrate antigen 19.9, and C-reactive protein), iTLB Model (discordant pairs of temperatures from thermograms), and iTLB + iClin Model (discordant pairs of temperatures and clinical data). Results The iClin Model achieved high diagnostic performance, with a validation area under the curve (AUC) of 0.95 ± 0.01 for differentiating symptomatic controls from patients with pancreatic ductal adenocarcinoma (PDAC), but showed no significant association with overall survival (OS). The iTLB Model showed limited diagnostic performance, with a validation AUC of 0.59 ± 0.03, but was associated with OS in patients with PDAC. The combined iTLB + iClin Model preserved high diagnostic performance, with a validation AUC of 0.95 ± 0.02, sensitivity of 96.20%, specificity of 91.20%, positive predictive value of 90.48%, and negative predictive value of 96.59%. For early-stage PDAC, the iTLB + iClin Model achieved an AUC of 0.93 (95% CI: 0.89–0.98), compared with 0.96 (95% CI: 0.95–0.98) for stages III–IV. Conclusion This study demonstrates the potential of combining specific clinical biomarker information with advanced techniques such as TLB, to improve the accuracy of pancreatic cancer diagnosis and prognosis.
Critical care medicine is at a pivotal stage of transformation from symptom-based approaches toward systems science. Building on a first-principles understanding of the mechanisms underlying critical illness, we propose the entropic critical illness theory (ECIT). Grounded in the second law of thermodynamics, ECIT posits that critical illness arises when the body’s capacity to regulate entropy production is impaired, leading to systemic disorder. This process is manifested by the concurrent escalation of host response entropy and hemodynamic entropy, resulting in disruptions in blood flow and oxygen delivery, ischemia, and hypoxia at the level of the critical unit, which is defined as the terminal microcirculatory–mitochondrial functional unit, ultimately culminating in multi-organ dysfunction. This study delineates the theoretical foundations of ECIT and outlines an entropy-based critical care framework that may inform future multimodal entropy-informed monitoring, risk stratification, and AI-assisted precision intervention in critical care medicine.
