Abstract
The COVID-19 pandemic brought several diagnostic challenges, including the post-infectious sequelae multisystem inflammatory syndrome in children (MIS-C). Some of the clinical features of this syndrome can be found in other pathologies such as Kawasaki disease, toxic shock syndrome, and endemic typhus. Endemic typhus, or murine typhus, is an acute infection treated much differently than MIS-C, so early detection is crucial to a favorable prognosis for patients with these disorders. Clinical Decision Support Systems (CDSS) are computer systems designed to support the decision-making of medical teams about their patients and intended to improve uprising clinical challenges in healthcare. In this article, we present a CDSS to distinguish between MIS-C and typhus, which includes a scoring system that allows the timely distinction of both pathologies using only clinical and laboratory features typically available within the first six hours of presentation to the Emergency Department. The proposed approach was trained and tested on datasets of 87 typhus patients and 133 MIS-C patients. A comparison was made against five well-known statistical and machine-learning models. A second dataset with 111 MIS-C patients was used to verify the effectiveness and robustness of the AI-MET system. The performance assessment for AI-MET and the five statistical and machine learning models was performed by computing sensitivity, specificity, accuracy, and precision. The AI-MET system scores 100 percent in the five metrics used on the training and testing dataset and 99 percent on the validation dataset. Statistical analysis tests were also performed to evaluate the robustness and ensure a thorough and balanced evaluation, in addition to demonstrating the statistical significance of MET-30 performance compared to the baseline models.
| Original language | English (US) |
|---|---|
| Article number | 109815 |
| Journal | Computers in Biology and Medicine |
| Volume | 188 |
| DOIs | |
| State | Published - Apr 2025 |
Funding
Abraham Bautista-Castillo received a B.S. in Biomedical Engineering and an M.S. in Electronics and Computer Engineering from the University of Guadalajara, Guadalajara, Mexico, in 2012 and 2016, respectively. From 2016 to 2018, he was a professor of Electronics Engineering and Computation, Robotics, Informatics, and Biomedical Engineering at the University of Guadalajara. He graduated with a Ph.D. in Biomedical Engineering at the University of Houston, Houston, TX, USA in 2024. His research interests include biomedical image analysis, artificial intelligence in healthcare, and robotics. Abraham\u2019s awards and honors include being twice granted the National Council of Science and Technology scholarship from Mexico in 2014 and 2020 and the Presidential Fellowship on behalf of the University of Houston Graduate School and the Cullen College of Engineering. Ioannis A. Kakadiaris received a B.S. in Physics from the University of Athens, Athens, Greece, in 1989, an M.S. degree in Computer Science from the Northeastern University, Boston, USA, in 1991, and a Ph.D. degree in Computer Science from the University of Pennsylvania, Philadelphia, PA, in 1997. He is a Hugh Roy and Lillie Cranz Cullen University Professor of Computer Science at the University of Houston, Houston, TX, USA. He is also the founder and director of the Computational Biomedicine Lab at the University of Houston. His research interests include biomedical image analysis and artificial intelligence in healthcare. Dr. Kakadiaris\u2019s awards and honors include twice winning the U.H. Computer Science Research Excellence Award, the NSF Early Career Development Award, the Schlumberger Technical Foundation Award, the U.H. Enron Teaching Excellence Award, and the James Muller Vulnerable Plaque Young Investigator Prize. Email:[email protected] Angela Chun received a B.A. in Psychology and a minor in Biochemistry and Cell Biology from Rice University in Houston, Texas, in 2013. As a Rice\u2013Baylor Medical Scholar, she obtained her M.D. degree from Baylor College of Medicine. She completed her Pediatrics Residency at Lurie Children\u2019s Hospital with Northwestern University in Chicago, Illinois, then returned to Houston, Texas, to complete her fellowship training in Pediatric Rheumatology, during which she obtained a master\u2019s degree in Education from the University of Houston. She is interested in education as a form of patient advocacy. Her research interests include multisystem inflammatory syndrome in children (MIS-C), vasculitis, juvenile dermatomyositis, musculoskeletal ultrasound, and acute care within pediatric rheumatology. Dr. Chun\u2019s awards and honors include winning the Institute of Orthopedic Research and Education Eppright Award, Force and Motion/ORS Young Scientist Award, Texas Society of Critical Care Medicine Alan Fields Award, McGaw Exemplary Model of Professionalism, Texas Children\u2019s Department of Pediatrics Outstanding Fellow Award, Texas Children\u2019s Department of Pediatrics Subspecialty Fellow Advanced Degree Scholar Award, and the Arthritis Foundation Curriculum Development Solutions to Growing the Diversity in the Rheumatology Workforce grant. This work was partly supported by NIH grant number R33HD105593 . Abraham Bautista-Castillo is also supported by the National Council of Science and Technology of Mexico , scholarship number 739528 . Icons and diagrams shown in this work were obtained and created through free licenses from Icons8 [38] and Draw.io [39] , respectively. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors. They do not necessarily reflect the views of the NIH, other funders, the position, or the policy of the Government, and no official endorsement should be inferred.
Keywords
- COVID-19
- Clinical decision support system
- Deep learning
- Endemic typhus
- MIS-C
- Typhus
ASJC Scopus subject areas
- Health Informatics
- Computer Science Applications
