About

Julio C. Facelli, PhD, FACMI, is a Distinguished Professor in the Department of Biomedical Informatics at the University of Utah, where he also serves as Vice Chair for Faculty and Director of Graduate Students. He is the Associate Director for Biomedical Informatics at the Clinical and Translational Science Institute and holds adjunct positions in Nursing, Chemistry, and Physics. Born in Buenos Aires, Argentina, he earned his PhD in physics from the University of Buenos Aires in 1982 and completed post-doctoral research at the University of Arizona before joining the University of Utah in 1984. His extensive expertise encompasses computational sciences, parallel and distributed computing, and advanced network applications, with a research focus on applying advanced computing techniques to solve important biomedical problems. His research projects include environmental health informatics, big data applications in biomedical informatics, protein structure prediction, crystal structure prediction of pharmaceutical drugs, and distributed information systems for clinical and translational research. Dr. Facelli has co-authored over 250 peer-reviewed publications, served as Chair of the Coalition for Scientific Computing, and received awards such as the Reed Gardener Award for Faculty Excellence. He is a Fellow of the American College of Medical Informatics and the Academy of Science Health Educators, with research funded by NSF, NIH, DOD, and DOE. His work involves teaching across multiple disciplines and participating in numerous national committees and advisory panels related to information technology and computational sciences.

Research topics

• Medicine
• Computer Science
• Artificial Intelligence
• Internal medicine
• Knowledge management
• Virology
• Political Science
• Sociology
• Bioinformatics
• Demography
• Pathology
• Genetics
• Biology
• Endocrinology

Selected publications

• Evaluating the indirect interaction between glucagon-like peptide-1 receptor agonists and warfarin using real-world data

  Journal of Thrombosis and Thrombolysis · 2026-05-11

  article
  PublisherDOI

• Needs, Preferences, and Challenges of Environmental Health Researchers in Using Sensors: A Content Analysis (Preprint)

  2026-01-12

  articleOpen access

  <sec> <title>BACKGROUND</title> Environmental exposures can influence human health in complex ways. It remains difficult for researchers to integrate exposure data, partly due to an unmet need for informatics and metadata tools. </sec> <sec> <title>OBJECTIVE</title> The purpose of this study was to understand the needs, preferences, and pain points of environmental health researchers regarding the selection, deployment, and integration of sensors for their research studies, to inform user requirements for a sensor metadata repository. </sec> <sec> <title>METHODS</title> We purposively recruited six exposure health researchers with expertise entailing sensors, corresponding to one of eight role types, and conducted semi-structured interviews between February 7-26, 2025. Interviews centered on understanding the needs, preferences, and pain points of environmental health researchers seeking to use sensors for their research projects. We conducted deductive content analysis of interview transcripts, guided by the HITREF framework. </sec> <sec> <title>RESULTS</title> The participants held primary roles of primary investigator, study coordinator, sensor developer, biomedical informaticist, and study sponsor. Content analysis revealed that participants consider multiple characteristics of sensors when selecting sensors for studies, including cost, physical parameters and limitations of the sensors, reliability, suitable environments for deployment, and software and processes required for data acquisition, transfer, integration, and analysis. User training and interaction are important considerations, often conceptualized as burdens on study participants that research teams seek to minimize. Participants described a desire for adequate support from sensor developers and flexibility in data transfer and analysis. </sec> <sec> <title>CONCLUSIONS</title> Participants in varied roles described many similar themes regarding considerations for sensor selection, deployment, and integration as well as desired features for a sensor metadata repository. </sec>

  PublisherDOI

• Enhancing prediction of inpatient deterioration by combining clinical and nurse concern features, with or without temporal clustering

  JAMIA Open · 2026-05-02

  articleOpen access

  Background: Early warning systems (EWSs) help clinicians identify deteriorating patients using clinical data, such as vital signs. However, standard systems struggle to capture nuanced nursing concerns. The Healthcare Process Model-ExpertSignals (HPM-ExpertSignals) framework describes how nurses' concerns are reflected in their documentation patterns. While a recent trial showed positive outcomes, the predictive gain of combining both data types remains unquantified. Objectives: We evaluated improvements in F-measure by combining HPM-ExpertSignals with clinical data using the k-shape temporal clustering algorithm. Materials and Methods: Six models were compared based on their features and the inclusion of k-shape. Models were trained to predict patient deterioration (cardiac arrest and death) 12 h before the event using a large dataset. The primary outcome was the harmonic mean of precision and recall (F-measure). Results: The F-measure achieved by the model that uses both feature types was 0.25 (±0.01). The clinical features-only model was 0.16 (±0.01), and the HPM-ExpertSignals-only model was 0.19 (±0.02). F-measures for their corresponding k-Shape models were all at 0.06 (±0.0). Discussion: The combined model has the highest F-measure among the clinical-only and HPM-ExpertSignals-only models. The low performance of the k-Shape models suggests that k-Shape is not well suited to capturing the specific temporal patterns present in this problem set. Conclusion: Early warning systems leveraged both clinical data and HPM-ExpertSignals predictors, which may offer clinically significant improvements. Future research should explore alternative temporal pattern algorithms to further refine predictive accuracy.

  PublisherDOI

• Reliable Uncertainty Under Class Imbalance and Distribution Shift: Class-Conditional Conformal Prediction of Multiple Sclerosis

  medRxiv · 2026-05-15

  articleSenior author

  Abstract Objectives To evaluate whether class-conditional conformal prediction (CP) can provide reliable uncertainty quantification (UQ) under severe class imbalance and distribution shift, using multiple sclerosis (MS) diagnosis from magnetic resonance imaging (MRI) as a clinical exemplar. Methods We evaluated marginal and class-conditional CP using 720 T2-weighted MRI scans (142 MS, 578 controls). A convolutional neural network trained on 3 T data was evaluated under distribution shift (1.5 T acquisitions and synthetic image degradations). Through 100 Monte Carlo experiments, we assessed coverage guarantees, class-specific performance, and relationships between calibration set size, coverage variance, and uncertainty. Results Marginal CP severely under-covered the minority MS class (16.9% mean coverage at 1.5 T vs. 95.2% for controls) despite valid population-level guarantees. Class-conditional CP dramatically improved MS coverage to 77.5% at 1.5 T and 85.8% at 3 T, significantly reducing severe undercoverage (&lt;80%) frequency while maintaining &gt;89% control coverage. Minority class coverage variance increased due to limited calibration samples, matching theoretical Beta-binomial predictions. CP maintained validity under distribution shift; prediction set sizes scaled monotonically with shift severity, yielding clinically interpretable UQ. Conclusions Class-conditional CP successfully mitigates systematic undercoverage of minority disease classes while maintaining validity under distribution shift. The approach offers a practical, model-agnostic solution for uncertainty quantification applicable across clinical AI systems, though increased coverage variance for less represented conditions reflects fundamental statistical constraints. By characterizing these variance trade-offs, this framework enables more reliable deployment of diagnostic AI in heterogeneous clinical environments across diverse medical domains where minority disease class detection is critical.

  PublisherDOI

• Scaling Sensor Metadata Extraction for Exposure Health Using LLMs

  Zenodo (CERN European Organization for Nuclear Research) · 2025-08-22

  articleOpen access

  This repository contains resources supporting the manuscript “Scaling Sensor Metadata Extraction for Exposure Health Using Large Language Models.” It provides the workflow and supporting files for automating the extraction and harmonization of sensor metadata from exposure health literature. Contents: Paper List (Excel): list of 20 used research papers. Users should download the full-text PDFs of these papers. Extraction Code: Python scripts leveraging the OpenAI API to process downloaded PDFs, extract sensor metadata, and output results in an excel file. Postprocessing Code: Scripts that process the GPT-generated outputs, extract metadata fields for each attribute, and compile them into structured Excel files. Usage: Download the listed papers in PDF format. Run the instrument_entity.py code to generate raw metadata outputs. Apply the postprocessing scripts json_to_xlsx.py to organize extracted metadata into attribute-level Excel tables.

  PublisherDOI

• Conceptual framework for prediction models of patient deterioration based on nursing documentation patterns: reproducibility and generalizability with a large number of hospitals across the United States

  Journal of Biomedical Informatics · 2025-07-27 · 2 citations

  articleOpen access
  PublisherOA PDFDOI

• Structural Homology and Electrostatic Potential Comparisons of Epitope Pair Candidates for Molecular Mimicry Triggering of Type 1 Diabetes Mellitus

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-09-04

  preprintOpen accessSenior authorCorresponding

  Abstract Background Molecular mimicry, where foreign and self-peptides contain similar epitopes, can induce autoimmune responses. Identifying potential molecular mimics and studying their properties is key to understanding the onset of autoimmune diseases such as type 1 diabetes mellitus (T1DM). Previous work identified pairs of infectious epitopes (E INF ) and T1DM epitopes (E T1D ) that demonstrated sequence homology; however, structural homology was not considered. Correlating sequence homology with structural properties is important for translational investigation of potential molecular mimics. This work compares sequence homology with structural homology by calculating the structures and electrostatic potential surfaces of the epitope pairs identified in previous work from our laboratory. Results For each pair of E INF and E T1D , the root mean square deviations (RMSD) were calculated between their predicted structures and their electrostatic potentials. Structures were predicted using the AlphaFold software program. Of the 53 epitope pairs considered here, only 10 did not exhibit any matching (i.e. less than 3 residues overlap). When considering all residues the RMSD ranges from 0.33 Å to 11.66 Å with an average of 2.68 Å. Twenty-two pairs (42%) have RMSD of less than 1.5 Å and 30 (58%) less than 3 Å. Conclusions Most of the E INF /E T1D pairs selected by sequence homology show similar structural and electrostatic distributions, indicating that the E INF may also bind to the same protein targets, i.e. the major histocompatibility complex molecules, for T1DM, leading to molecular mimicry onset of the disease. These findings suggest that searching for epitope pairs using sequence homology, a much less computationally demanding approach, leads to strong candidates for molecular mimicry that should be considered for further study. But structure homology, electrostatic potential calculations and full docking calculations may be necessary to advance the in-silico molecular mimicry predictions, which may be useful to select the most promising candidates for experimental studies.

  PublisherOA PDFDOI

• Data science and artificial intelligence in biology, health, and healthcare

  Journal of Clinical and Translational Science · 2025-01-01

  editorialOpen access
  PublisherOA PDFDOI

• 363 The art and science of data navigation for translational research

  Journal of Clinical and Translational Science · 2025-03-25

  articleOpen accessSenior author

  Objectives/Goals: Translational researchers spend significant amounts of time finding available datasets and other research data resources for their purposes. Objectives of this program are develop and evaluate a multipronged approach to supporting researchers with existing data resources. Methods/Study Population: We established a dedicated service with expertise in data resources to increase awareness, understanding, and utilization of existing data resources. This program assists investigators and trainees discover appropriate data resources, formulate scientific problems in computable formats, advise on state-of-the-art data analytics, data management, build collaborations, mentor data users, and develop a service pipeline for streamlined data resource project management. This is accomplished through these essential functions: (1) Discover, catalog, document, and manage metadata resources, (2) train and present data resources to the research community, (3) provide individual consultations, and (4) explore and assess novel data resources. Results/Anticipated Results: In a phased approach, the data navigation program is performing outreach to the research community and integrating with existing data efforts on campus, presenting and demonstrating existing data resources, established a consultation service, and building core competencies into long-term usage and navigation of resources across campus. Evaluating the program monthly has shown an increase in various metrics for evaluating commitment and engagement including number of requests for access to data resource, consultations, publications and presentations, co-authorship, and proposals. Unawareness and inappropriate use of data resources leads to delays in performing research and potentially unnecessary duplications of efforts. Discussion/Significance of Impact: Our data navigation program has increased use of data resources in research. Next steps are to continue evaluation and further streamline informatics approaches to data discovery, abstraction, formulation, and analysis. Harmonized data resource programs are important translational science approach to foster the next generation of research.

  PublisherOA PDFDOI

• Introduction to the <i>JCTS</i> special issue on advancing understanding and use of impact measures in implementation science

  Journal of Clinical and Translational Science · 2025-01-01

  articleOpen accessSenior authorCorresponding
  PublisherOA PDFDOI

Recent grants

• NIH Grant S10RR017214

  NIH · $1.5M · 2004

• NIH Grant U54EB021973

  NIH · $7.1M · 2020

• NIH Grant RC2LM010798

  NIH · $2.8M · 2013

• NIH Grant R25GM083755

  NIH · $969k · 2016

Frequent coauthors

• Marta B. Ferraro

  119 shared

• Rubén H. Contreras

  University of Buenos Aires

  115 shared

• Ramkiran Gouripeddi

  106 shared

• David M. Grant

  University of Nottingham

  81 shared

• Anita M. Orendt

  University of Utah

  74 shared

• V. Bazterra

  University of Illinois Chicago

  62 shared

• Dora G. de Kowalewski

  University of Buenos Aires

  44 shared

• Randy Madsen

  University of Utah

  40 shared

Education

• Ph.D., Physics

  University of Buenos Aires

  1982

Awards & honors

• Reed Gardener Award on Faculty Excellence from the Departmen…
• Fellow of the American College of Medical Informatics (2014)
• Fellow of the Academy of Science Health Educators (2017)