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Model‐based diagnosis with FTTell: Diagnosing early pediatric failure to thrive.

Authors :
Levi‐Soskin, Natali
Yasin, Fatma
Dori, Dov
Shaoul, Ron
Source :
Systems Engineering. Sep2023, Vol. 26 Issue 5, p548-566. 19p.
Publication Year :
2023

Abstract

Pediatric Failure To Thrive (FTT), commonly presented in young infants, is often not diagnosed on time or missed. Lack of timely infants' diagnosis can adversely affect their growth and development. We have developed and successfully tested FTTell—a model‐based system for diagnosing FTT during common pediatric follow up. FTTell is an executable model‐based diagnostic tool for diagnosing FTT. We use Object‐Process Methodology extended with Methodical Approach to Executable Integrative Modeling, enabling qualitative considerations and quantitative parameters of the problem to be modeled jointly, enabling FTT diagnosis. The validity of FTTell is demonstrated on data collected from 100 infants. For each child, FTTell calculates a score indicating FTT presence and severity. We compared the systems' outcomes to a pediatric gastroenterologist expert severity assessment. Of the 100 infants, the system initially yielded 82% validity. Reassessment improved it to 87% validity. Pediatricians may miss infants with FTT, especially in borderline cases. FTTell can effectively serve as a FTT diagnosis tool, boosting pediatricians' correct diagnosis and proper investigation. Our cloud‐based system can be continuously updated with the latest research findings. FTTell can diagnose FTT and its severity in infants with 87% accuracy. Pediatricians can use this model‐based standardized approach to improve their FTT diagnosis and provide appropriate timely intervention when needed. Model‐based diagnosis is a novel application of conceptual models, and OPM ISO 19450 is especially fit for this purpose. The model‐based diagnosis approach can be extended beyond medicine to diagnosing problems with engineered, technological, and socio‐technical systems. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10981241
Volume :
26
Issue :
5
Database :
Academic Search Index
Journal :
Systems Engineering
Publication Type :
Academic Journal
Accession number :
171349300
Full Text :
https://doi.org/10.1002/sys.21674