Journal Article

FZJ-2023-02448

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Developing an Artificial Intelligence-Based Representation of a Virtual Patient Model for Real-Time Diagnosis of Acute Respiratory Distress Syndrome

Barakat, C. (Corresponding author)FZJ* ; Sharafutdinov, K.RWTH* ; Busch, J.FZJ* ; Saffaran, S. ; Bates, D. G. ; Hardman, J. G. ; Schuppert, A.RWTH* ; Brynjólfsson, S. ; Fritsch, S.FZJ* ; Riedel, M.FZJ*

2023
MDPI Basel

This record in other databases:    

Please use a persistent id in citations: doi:10.3390/diagnostics13122098  doi:10.34734/FZJ-2023-02448

Abstract: Acute Respiratory Distress Syndrome (ARDS) is a condition that endangers the lives of many Intensive Care Unit patients through gradual reduction of lung function. Due to its heterogeneity, this condition has been difficult to diagnose and treat, although it has been the subject of continuous research, leading to the development of several tools for modeling disease progression on the one hand, and guidelines for diagnosis on the other, mainly the “Berlin Definition”. This paper describes the development of a deep learning-based surrogate model of one such tool for modeling ARDS onset in a virtual patient: the Nottingham Physiology Simulator. The model-development process takes advantage of current machine learning and data-analysis techniques, as well as efficient hyperparameter-tuning methods, within a high-performance computing-enabled data science platform. The lightweight models developed through this process present comparable accuracy to the original simulator (per-parameter R2 > 0.90). The experimental process described herein serves as a proof of concept for the rapid development and dissemination of specialised diagnosis support systems based on pre-existing generalised mechanistic models, making use of supercomputing infrastructure for the development and testing processes and supported by open-source software for streamlined implementation in clinical routines.

---

Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)
2. EUROCC - National Competence Centres in the framework of EuroHPC (951732) (951732)
3. RAISE - Research on AI- and Simulation-Based Engineering at Exascale (951733) (951733)
4. SMITH - Medizininformatik-Konsortium - Beitrag Forschungszentrum Jülich (01ZZ1803M) (01ZZ1803M)
5. DEEP-EST - DEEP - Extreme Scale Technologies (754304) (754304)

Appears in the scientific report 2023

---

Database coverage:
; ; ; ; Article Processing Charges ; Clarivate Analytics Master Journal List ; Current Contents - Clinical Medicine ; DOAJ Seal ; Ebsco Academic Search ; Essential Science Indicators ; Fees ; IF < 5 ; JCR ; PubMed Central ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

---