Clinical machine-learning models are often evaluated within a single dataset, although deployment occurs across heterogeneous institutions and measurement processes. We evaluated Logistic Regression, Random Forest, and Gradient Boosting for ICU hospital-mortality prediction across MIMIC-III Demo and eICU using a 24-h l...
Jorge Robalino-Díaz, A. Cabrera-Andrade, A. K. Zambrano et al.· Frontiers in Digital Health· 0 citations
Blockchain, decentralized finance (DeFi), and artificial intelligence are converging into an economic ecosystem whose concentration, risk, and mechanism design have rarely been examined within a unified empirical framework. This study integrates five publicly available data sources: Uniswap decentralization indices,...
W. Villegas-Ch., Aracely Mera Navarrete, Rommel Gutierrez et al.· Frontiers in Blockchain· 0 citations
Biomedical graph-learning results are difficult to compare when source versions, identifier rules, data splits, structural features, and sampled-nonedge contracts are incompletely documented.
We developed BioGraphBench-PPI as an audit-first benchmark for protein-protein interaction (PPI) link prediction usin...
A. Cabrera-Andrade, Jorge Robalino-Díaz, I. Dueñas-Espín et al.· Frontiers in Bioinformatics· 0 citations
This study proposes a transparent rule-based framework for estimating persistence, delegation-related lexical patterns, and refinement-related lexical markers without inferring psychological dependence in conversational generative models.
Aracely Mera-Navarrete, Solange Revelo, Jefferson Beltrán-Morales et al.· Frontiers in Artificial Inte...· 0 citations
Findings demonstrate that social presence, affective bonding, and conversational continuity are interdependent and context-sensitive, supporting a multidimensional framework for understanding and designing human–AI communication processes.
W. Villegas-Ch., Iván Ortiz-Garcés, Fernando Zúñiga-Tello et al.· Frontiers of Computer Scienc...· 0 citations
E EduFairBench provides a reproducible methodology for jointly analyzing predictive performance, robustness, uncertainty, and feedback quality, providing a comprehensive methodological framework for the rigorous evaluation of LLM-based educational assessment systems.
W. Villegas-Ch., Aracely Mera-Navarrete, Fernando Zúñiga-Tello et al.· Frontiers in Artificial Inte...· 0 citations
This work reframes cross-model phishing detection from a problem of model incompatibility to one of practical calibration, and provides two deployable solutions, threshold recalibration on a small target slice and aggregated-pool training, along with a publicly released multi-LLM corpus.
Rommel Gutierrez, W. Villegas-Ch., Jaime Govea· Frontiers in Big Data· 0 citations
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