Jack McKeown | Searcharxiv

arXiv2026

AGN-DB: A Unified Multi-Wavelength Database of Active Galactic Nuclei

We present the Active Galactic Nuclei Database (AGN-DB), a comprehensive, multi-wavelength catalog compiled from more than 100 publicly available AGN catalogs and samples released by the end of 2025, spanning radio to $γ$-ray wavelengths. The database contains approximately 8.1 million unique sources, approximately 7.8 million of which remain after flagging stellar contaminants, and approximately 6.8 million of these are classified as AGN. Source cross-matching across catalogs is performed using Lyra, a Bayesian likelihood-ratio framework that jointly considers positional uncertainties, source densities, and photometric information to compute posterior match probabilities. The resulting catalog provides astrometric coordinates, redshifts, photometry, and classifications for each unique source. All multi-catalog provenance is preserved. For every property, we store the full array of values and originating catalog identifiers, enabling multi-epoch and multi-survey analyses. In this paper, we describe the AGN-DB pipeline, including the cross-matching methodology, and present the statistical properties of the v1.0 catalog. AGN-DB is designed to enable population studies, spectral energy distribution modeling, AGN classification, and variability analyses at an unprecedented scale. Its pipeline is designed to facilitate the integration of new catalogs, allowing AGN-DB to be updated regularly, with releases planned at least annually.

astro-ph.GA↗

arXiv2025

SmartAlert: Implementing Machine Learning-Driven Clinical Decision Support for Inpatient Lab Utilization Reduction

Repetitive laboratory testing unlikely to yield clinically useful information is a common practice that burdens patients and increases healthcare costs. Education and feedback interventions have limited success, while general test ordering restrictions and electronic alerts impede appropriate clinical care. We introduce and evaluate SmartAlert, a machine learning (ML)-driven clinical decision support (CDS) system integrated into the electronic health record that predicts stable laboratory results to reduce unnecessary repeat testing. This case study describes the implementation process, challenges, and lessons learned from deploying SmartAlert targeting complete blood count (CBC) utilization in a randomized controlled pilot across 9270 admissions in eight acute care units across two hospitals between August 15, 2024, and March 15, 2025. Results show significant decrease in number of CBC results within 52 hours of SmartAlert display (1.54 vs 1.82, p <0.01) without adverse effect on secondary safety outcomes, representing a 15% relative reduction in repetitive testing. Implementation lessons learned include interpretation of probabilistic model predictions in clinical contexts, stakeholder engagement to define acceptable model behavior, governance processes for deploying a complex model in a clinical environment, user interface design considerations, alignment with clinical operational priorities, and the value of qualitative feedback from end users. In conclusion, a machine learning-driven CDS system backed by a deliberate implementation and governance process can provide precision guidance on inpatient laboratory testing to safely reduce unnecessary repetitive testing.

cs.LG↗