RESUMEN
OBJECTIVES: Sepsis is a leading global cause of mortality, and predicting its outcomes is vital for improving patient care. This study explored the capabilities of ChatGPT, a state-of-the-art natural language processing model, in predicting in-hospital mortality for sepsis patients. METHODS: This study utilized data from the Korean Sepsis Alliance (KSA) database, collected between 2019 and 2021, focusing on adult intensive care unit (ICU) patients and aiming to determine whether ChatGPT could predict all-cause mortality after ICU admission at 7 and 30 days. Structured prompts enabled ChatGPT to engage in in-context learning, with the number of patient examples varying from zero to six. The predictive capabilities of ChatGPT-3.5-turbo and ChatGPT-4 were then compared against a gradient boosting model (GBM) using various performance metrics. RESULTS: From the KSA database, 4,786 patients formed the 7-day mortality prediction dataset, of whom 718 died, and 4,025 patients formed the 30-day dataset, with 1,368 deaths. Age and clinical markers (e.g., Sequential Organ Failure Assessment score and lactic acid levels) showed significant differences between survivors and non-survivors in both datasets. For 7-day mortality predictions, the area under the receiver operating characteristic curve (AUROC) was 0.70-0.83 for GPT-4, 0.51-0.70 for GPT-3.5, and 0.79 for GBM. The AUROC for 30-day mortality was 0.51-0.59 for GPT-4, 0.47-0.57 for GPT-3.5, and 0.76 for GBM. Zero-shot predictions using GPT-4 for mortality from ICU admission to day 30 showed AUROCs from the mid-0.60s to 0.75 for GPT-4 and mainly from 0.47 to 0.63 for GPT-3.5. CONCLUSIONS: GPT-4 demonstrated potential in predicting short-term in-hospital mortality, although its performance varied across different evaluation metrics.
RESUMEN
Sodium-ion batteries (SIBs) is a promising technology for next-generation energy storage. However, their performance is limited at low temperatures due to the inferior bulk and interfacial resistance of current electrolytes. Here we present a systematic study to evaluate carboxylate ester-based electrolytes for SIB applications, due to their favorable properties (i.e., low melting point, low viscosity and high dielectric constant). The effects of salt, concentration and solvent molecular structure were systematically examined and compared with those of carbonate-based electrolytes. By combining electrochemical tests with spectroscopic characterization, the performance of selective carboxylate ester-based electrolytes in hard carbon/Na and Na3V2(PO4)3/Na half-cells was evaluated. We found carboxylates enable high electrolyte conductivities, especially at low temperatures. However, carboxylates alone are inadequate to form a stable interphase due to their high reactivity, which can be addressed by choosing a suitable anion and facilitating anion-rich Na+ solvation by increasing salt concentration. Fundamental knowledge on the chemistry-property-performance correlation of this new family of electrolytes was obtained, and their benefits and pitfalls were thoroughly discussed. These discoveries and knowledge will shed light on the potential of carboxylate ester-based electrolytes and provide the foundation for further electrolyte engineering.
RESUMEN
We compared the prediction performance of machine learning-based undiagnosed diabetes prediction models with that of traditional statistics-based prediction models. We used the 2014-2020 Korean National Health and Nutrition Examination Survey (KNHANES) (N = 32,827). The KNHANES 2014-2018 data were used as training and internal validation sets and the 2019-2020 data as external validation sets. The receiver operating characteristic curve area under the curve (AUC) was used to compare the prediction performance of the machine learning-based and the traditional statistics-based prediction models. Using sex, age, resting heart rate, and waist circumference as features, the machine learning-based model showed a higher AUC (0.788 vs. 0.740) than that of the traditional statistical-based prediction model. Using sex, age, waist circumference, family history of diabetes, hypertension, alcohol consumption, and smoking status as features, the machine learning-based prediction model showed a higher AUC (0.802 vs. 0.759) than the traditional statistical-based prediction model. The machine learning-based prediction model using features for maximum prediction performance showed a higher AUC (0.819 vs. 0.765) than the traditional statistical-based prediction model. Machine learning-based prediction models using anthropometric and lifestyle measurements may outperform the traditional statistics-based prediction models in predicting undiagnosed diabetes.