Development and Validation of an Interpretable Machine Learning Model for Early Prognosis Prediction in ICU Patients with Malignant Tumors and Hyperkalemia.

This study aims to develop and validate a machine learning (ML) predictive model for assessing mortality in patients with malignant tumors and hyperkalemia (MTH). We extracted data on patients with MTH from the Medical Information Mart for Intensive Care-IV, version 2.2 (MIMIC-IV v2.2) database. The dataset was split into a training set (75%) and a validation set (25%). We used the Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify potential predictors, which included clinical laboratory indicators and vital signs. Pearson correlation analysis tested the correlation between predictors. In-hospital death was the prediction target. The Area Under the Curve (AUC) and accuracy of the training and validation sets of 7 ML algorithms were compared, and the optimal 1 was selected to develop the model. The calibration curve was used to evaluate the prediction accuracy of the model further. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) enhanced model interpretability. 496 patients with MTH in the Intensive Care Unit (ICU) were included. After screening, 17 clinical features were included in the construction of the ML model, and the Pearson correlation coefficient was <0.8, indicating that the correlation between the clinical features was small. eXtreme Gradient Boosting (XGBoost) outperformed other algorithms, achieving perfect scores in the training set (accuracy: 1.000, AUC: 1.000) and high scores in the validation set (accuracy: 0.734, AUC: 0.733). The calibration curves indicated good predictive calibration of the model. SHAP analysis identified the top 8 predictive factors: urine output, mean heart rate, maximum urea nitrogen, minimum oxygen saturation, minimum mean blood pressure, maximum total bilirubin, mean respiratory rate, and minimum pH. In addition, SHAP and LIME performed in-depth individual case analyses. This study demonstrates the effectiveness of ML methods in predicting mortality risk in ICU patients with MTH. It highlights the importance of predictors like urine output and mean heart rate. SHAP and LIME significantly enhanced the model's interpretability.

Antibiotic pollution promotes dominance by harmful cyanobacteria: A case study examining norfloxacin exposure in competition experiments.

Cyanobacterial harmful algal blooms (cyanoHABs) in freshwater lakes across the globe are often combined with other stressors. Pharmaceutical pollution, especially antibiotics in water bodies, poses a potential hazard in aquatic ecosystems. However, how antibiotics influence the risk of cyanoHABs remains unclear. Here, we investigated the effects of norfloxacin (NOR), one of the most widely used antibiotics globally, to a bloom-forming cyanobacterium (Microcystis aeruginosa) and a common green alga (Scenedesmus quadricauda), under both mono- and coculture conditions. Taxon-specific responses to NOR were evaluated in monoculture. In addition, the growth rate and change in ratio of cyanobacteria to green algae when cocultured with exposure to NOR were determined. In monocultures of Microcystis, exposure to low concentrations of NOR resulted in decreases in biomass, chlorophyll a and soluble protein content, while superoxide anion content and superoxide dismutase activity increased. However, NOR at high concentration only slightly affected Scenedesmus. During the co-culture trials of Microcystis and Scenedesmus, the 5 µg · L-1 NOR treatment increased the ratio of Microcystis to co-cultured Scenedesmus by 47.2%. Meanwhile, although Scenedesmus growth was enhanced by 4.2% under NOR treatment in monoculture, it was conversely inhibited by 63.4% and 38.2% when co-cultured with Microcystis with and without NOR, respectively. Our results indicate that antibiotic pollution has a potential risk to enhance the perniciousness of cyanoHABs by disturbing interspecific interaction between cyanobacteria and green algae. These results reinforce the need for scientists and managers to consider the influence of xenobiotics in shaping the outcome of interactions among multiple species in aquatic ecosystems.