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Baumgart A, Beck G, Ghezel-Ahmadi D. [Artificial intelligence in intensive care medicine]. Med Klin Intensivmed Notfmed 2024; 119:189-198. [PMID: 38546864 DOI: 10.1007/s00063-024-01117-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 01/29/2024] [Accepted: 02/05/2024] [Indexed: 04/05/2024]
Abstract
The integration of artificial intelligence (AI) into intensive care medicine has made considerable progress in recent studies, particularly in the areas of predictive analytics, early detection of complications, and the development of decision support systems. The main challenges remain availability and quality of data, reduction of bias and the need for explainable results from algorithms and models. Methods to explain these systems are essential to increase trust, understanding, and ethical considerations among healthcare professionals and patients. Proper training of healthcare professionals in AI principles, terminology, ethical considerations, and practical application is crucial for the successful use of AI. Careful assessment of the impact of AI on patient autonomy and data protection is essential for its responsible use in intensive care medicine. A balance between ethical and practical considerations must be maintained to ensure patient-centered care while complying with data protection regulations. Synergistic collaboration between clinicians, AI engineers, and regulators is critical to realizing the full potential of AI in intensive care medicine and maximizing its positive impact on patient care. Future research and development efforts should focus on improving AI models for real-time predictions, increasing the accuracy and utility of AI-based closed-loop systems, and overcoming ethical, technical, and regulatory challenges, especially in generative AI systems.
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Affiliation(s)
- André Baumgart
- Zentrum für Präventivmedizin und Digitale Gesundheit, Medizinische Fakultät Mannheim der Universität Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Deutschland.
| | - Grietje Beck
- Abteilung für Anästhesiologie, Intensivmedizin und Schmerzmedizin, Universitätsmedizin Mannheim gGmbH, Medizinische Fakultät Mannheim der Universität Heidelberg, Mannheim, Deutschland
| | - David Ghezel-Ahmadi
- Abteilung für Anästhesiologie, Intensivmedizin und Schmerzmedizin, Universitätsmedizin Mannheim gGmbH, Medizinische Fakultät Mannheim der Universität Heidelberg, Mannheim, Deutschland
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2
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Samadi ME, Guzman-Maldonado J, Nikulina K, Mirzaieazar H, Sharafutdinov K, Fritsch SJ, Schuppert A. A hybrid modeling framework for generalizable and interpretable predictions of ICU mortality across multiple hospitals. Sci Rep 2024; 14:5725. [PMID: 38459085 PMCID: PMC10923850 DOI: 10.1038/s41598-024-55577-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 02/26/2024] [Indexed: 03/10/2024] Open
Abstract
The development of reliable mortality risk stratification models is an active research area in computational healthcare. Mortality risk stratification provides a standard to assist physicians in evaluating a patient's condition or prognosis objectively. Particular interest lies in methods that are transparent to clinical interpretation and that retain predictive power once validated across diverse datasets they were not trained on. This study addresses the challenge of consolidating numerous ICD codes for predictive modeling of ICU mortality, employing a hybrid modeling approach that integrates mechanistic, clinical knowledge with mathematical and machine learning models . A tree-structured network connecting independent modules that carry clinical meaning is implemented for interpretability. Our training strategy utilizes graph-theoretic methods for data analysis, aiming to identify the functions of individual black-box modules within the tree-structured network by harnessing solutions from specific max-cut problems. The trained model is then validated on external datasets from different hospitals, demonstrating successful generalization capabilities, particularly in binary-feature datasets where label assessment involves extrapolation.
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Affiliation(s)
- Moein E Samadi
- Institute for Computational Biomedicine, RWTH Aachen University, Aachen, Germany.
| | | | - Kateryna Nikulina
- Institute for Computational Biomedicine, RWTH Aachen University, Aachen, Germany
| | - Hedieh Mirzaieazar
- Institute for Computational Biomedicine, RWTH Aachen University, Aachen, Germany
| | | | - Sebastian Johannes Fritsch
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
- Center for Advanced Simulation and Analytics (CASA), Forschungszentrum Jülich, Jülich, Germany
| | - Andreas Schuppert
- Institute for Computational Biomedicine, RWTH Aachen University, Aachen, Germany
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3
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Sander J, Simon P, Hinske C. [Big data and artificial intelligence in anesthesia : Reality or fiction?]. DIE ANAESTHESIOLOGIE 2024; 73:77-84. [PMID: 38066215 DOI: 10.1007/s00101-023-01362-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/28/2023] [Indexed: 02/08/2024]
Abstract
Big data and artificial intelligence are buzzwords that everyone is talking about and yet always provide a touch of science fiction to the scenery. What is the status of these topics in anesthesia? Are the first robots already rolling through the corridors while doctors are getting bored as all the work has been done? Spoiler alert! We are still far away from achieving this. Initially, paper charts and analogue notes stand in the way of comprehensive digitization. Source systems need to be merged and data standardized, harmonized and validated. Therefore, the friendly android that is rolling towards us, waving and holding a freshly brewed cup of coffee in our thoughts will have to wait; however, a glimpse of the future is already evident in some clinics and the first promising developments are already showing what could be the standard tomorrow. Learning algorithms calculate the length of stay individually for each patient in the intensive care unit (ICU), reducing negative consequences such as readmission and mortality. The field of ultrasound technology for regional anesthesia and closed-loop anesthesia systems is also demonstrating the benefits of artificial intelligence (AI)-assisted technologies in practice. The efforts are diverse and ambitious but they repeatedly collide with privacy challenges and significant capital expenditure, which weigh heavily on an already financially strained healthcare system; however, anyone who listens carefully to the medical staff knows that robots are not what they would expect and the buzzwords big data and artificial intelligence might be less science fiction than initially assumed.
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Affiliation(s)
- J Sander
- Institut für Digitale Medizin (IDM), Universitätsklinikum Augsburg, Gutenbergstr. 7, 86356, Neusäß, Deutschland.
| | - P Simon
- Klinik für Anästhesiologie und Operative Intensivmedizin, Universitätsklinikum Augsburg, Augsburg, Deutschland
| | - C Hinske
- Institut für Digitale Medizin (IDM), Universitätsklinikum Augsburg, Gutenbergstr. 7, 86356, Neusäß, Deutschland
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Marx G. [Acute respiratory distress syndrome (ARDS): New perspectives in diagnostics and therapy]. Anasthesiol Intensivmed Notfallmed Schmerzther 2024; 59:10-11. [PMID: 38190821 DOI: 10.1055/a-2214-4025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
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Barakat CS, Sharafutdinov K, Busch J, Saffaran S, Bates DG, Hardman JG, Schuppert A, Brynjólfsson S, Fritsch S, Riedel M. Developing an Artificial Intelligence-Based Representation of a Virtual Patient Model for Real-Time Diagnosis of Acute Respiratory Distress Syndrome. Diagnostics (Basel) 2023; 13:2098. [PMID: 37370993 DOI: 10.3390/diagnostics13122098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 06/10/2023] [Accepted: 06/15/2023] [Indexed: 06/29/2023] Open
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.
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Affiliation(s)
- Chadi S Barakat
- Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany
- School of Engineering and Natural Science, University of Iceland, 107 Reykjavik, Iceland
- SMITH Consortium of the German Medical Informatics Initiative, 07747 Leipzig, Germany
| | - Konstantin Sharafutdinov
- SMITH Consortium of the German Medical Informatics Initiative, 07747 Leipzig, Germany
- Joint Research Centre for Computational Biomedicine, University Hospital RWTH Aachen, 52074 Aachen, Germany
| | - Josefine Busch
- Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Sina Saffaran
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK
| | - Declan G Bates
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK
| | | | - Andreas Schuppert
- SMITH Consortium of the German Medical Informatics Initiative, 07747 Leipzig, Germany
- Joint Research Centre for Computational Biomedicine, University Hospital RWTH Aachen, 52074 Aachen, Germany
| | - Sigurður Brynjólfsson
- School of Engineering and Natural Science, University of Iceland, 107 Reykjavik, Iceland
| | - Sebastian Fritsch
- Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany
- SMITH Consortium of the German Medical Informatics Initiative, 07747 Leipzig, Germany
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, 52074 Aachen, Germany
| | - Morris Riedel
- Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany
- School of Engineering and Natural Science, University of Iceland, 107 Reykjavik, Iceland
- SMITH Consortium of the German Medical Informatics Initiative, 07747 Leipzig, Germany
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Sharafutdinov K, Fritsch SJ, Iravani M, Ghalati PF, Saffaran S, Bates DG, Hardman JG, Polzin R, Mayer H, Marx G, Bickenbach J, Schuppert A. Computational Simulation of Virtual Patients Reduces Dataset Bias and Improves Machine Learning-Based Detection of ARDS from Noisy Heterogeneous ICU Datasets. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2023; 5:611-620. [PMID: 39184970 PMCID: PMC11342939 DOI: 10.1109/ojemb.2023.3243190] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 01/30/2023] [Accepted: 02/01/2023] [Indexed: 08/27/2024] Open
Abstract
Goal: Machine learning (ML) technologies that leverage large-scale patient data are promising tools predicting disease evolution in individual patients. However, the limited generalizability of ML models developed on single-center datasets, and their unproven performance in real-world settings, remain significant constraints to their widespread adoption in clinical practice. One approach to tackle this issue is to base learning on large multi-center datasets. However, such heterogeneous datasets can introduce further biases driven by data origin, as data structures and patient cohorts may differ between hospitals. Methods: In this paper, we demonstrate how mechanistic virtual patient (VP) modeling can be used to capture specific features of patients' states and dynamics, while reducing biases introduced by heterogeneous datasets. We show how VP modeling can be used for data augmentation through identification of individualized model parameters approximating disease states of patients with suspected acute respiratory distress syndrome (ARDS) from observational data of mixed origin. We compare the results of an unsupervised learning method (clustering) in two cases: where the learning is based on original patient data and on data derived in the matching procedure of the VP model to real patient data. Results: More robust cluster configurations were observed in clustering using the model-derived data. VP model-based clustering also reduced biases introduced by the inclusion of data from different hospitals and was able to discover an additional cluster with significant ARDS enrichment. Conclusions: Our results indicate that mechanistic VP modeling can be used to significantly reduce biases introduced by learning from heterogeneous datasets and to allow improved discovery of patient cohorts driven exclusively by medical conditions.
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Affiliation(s)
- Konstantin Sharafutdinov
- Institute for Computational BiomedicineRWTH Aachen University52062AachenGermany
- Joint Research Center for Computational BiomedicineRWTH Aachen University52062AachenGermany
- SMITH Consortium of the German Medical Informatics Initiative04103LeipzigGermany
| | - Sebastian Johannes Fritsch
- SMITH Consortium of the German Medical Informatics Initiative04103LeipzigGermany
- Department of Intensive Care MedicineUniversity Hospital RWTH Aachen52056AachenGermany
- Juelich Supercomputing CentreForschungszentrum Juelich52428JuelichGermany
| | - Mina Iravani
- Institute for Computational BiomedicineRWTH Aachen University52062AachenGermany
- Joint Research Center for Computational BiomedicineRWTH Aachen University52062AachenGermany
- SMITH Consortium of the German Medical Informatics Initiative04103LeipzigGermany
| | - Pejman Farhadi Ghalati
- Institute for Computational BiomedicineRWTH Aachen University52062AachenGermany
- Joint Research Center for Computational BiomedicineRWTH Aachen University52062AachenGermany
| | - Sina Saffaran
- School of EngineeringUniversity of WarwickCV4 7ALCoventryU.K.
| | - Declan G. Bates
- School of EngineeringUniversity of WarwickCV4 7ALCoventryU.K.
| | | | - Richard Polzin
- Institute for Computational BiomedicineRWTH Aachen University52062AachenGermany
- Joint Research Center for Computational BiomedicineRWTH Aachen University52062AachenGermany
- SMITH Consortium of the German Medical Informatics Initiative04103LeipzigGermany
| | - Hannah Mayer
- SMITH Consortium of the German Medical Informatics Initiative04103LeipzigGermany
- Systems Pharmacology & MedicineBayer AG51368LeverkusenGermany
| | - Gernot Marx
- SMITH Consortium of the German Medical Informatics Initiative04103LeipzigGermany
- Department of Intensive Care MedicineUniversity Hospital RWTH Aachen52056AachenGermany
| | - Johannes Bickenbach
- SMITH Consortium of the German Medical Informatics Initiative04103LeipzigGermany
- Department of Intensive Care MedicineUniversity Hospital RWTH Aachen52056AachenGermany
| | - Andreas Schuppert
- Institute for Computational BiomedicineRWTH Aachen University52062AachenGermany
- Joint Research Center for Computational BiomedicineRWTH Aachen University52062AachenGermany
- SMITH Consortium of the German Medical Informatics Initiative04103LeipzigGermany
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7
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Sharafutdinov K, Bhat JS, Fritsch SJ, Nikulina K, E. Samadi M, Polzin R, Mayer H, Marx G, Bickenbach J, Schuppert A. Application of convex hull analysis for the evaluation of data heterogeneity between patient populations of different origin and implications of hospital bias in downstream machine-learning-based data processing: A comparison of 4 critical-care patient datasets. Front Big Data 2022; 5:603429. [DOI: 10.3389/fdata.2022.603429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Accepted: 10/03/2022] [Indexed: 11/07/2022] Open
Abstract
Machine learning (ML) models are developed on a learning dataset covering only a small part of the data of interest. If model predictions are accurate for the learning dataset but fail for unseen data then generalization error is considered high. This problem manifests itself within all major sub-fields of ML but is especially relevant in medical applications. Clinical data structures, patient cohorts, and clinical protocols may be highly biased among hospitals such that sampling of representative learning datasets to learn ML models remains a challenge. As ML models exhibit poor predictive performance over data ranges sparsely or not covered by the learning dataset, in this study, we propose a novel method to assess their generalization capability among different hospitals based on the convex hull (CH) overlap between multivariate datasets. To reduce dimensionality effects, we used a two-step approach. First, CH analysis was applied to find mean CH coverage between each of the two datasets, resulting in an upper bound of the prediction range. Second, 4 types of ML models were trained to classify the origin of a dataset (i.e., from which hospital) and to estimate differences in datasets with respect to underlying distributions. To demonstrate the applicability of our method, we used 4 critical-care patient datasets from different hospitals in Germany and USA. We estimated the similarity of these populations and investigated whether ML models developed on one dataset can be reliably applied to another one. We show that the strongest drop in performance was associated with the poor intersection of convex hulls in the corresponding hospitals' datasets and with a high performance of ML methods for dataset discrimination. Hence, we suggest the application of our pipeline as a first tool to assess the transferability of trained models. We emphasize that datasets from different hospitals represent heterogeneous data sources, and the transfer from one database to another should be performed with utmost care to avoid implications during real-world applications of the developed models. Further research is needed to develop methods for the adaptation of ML models to new hospitals. In addition, more work should be aimed at the creation of gold-standard datasets that are large and diverse with data from varied application sites.
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Chancen der Digitalisierung für innovative Gesundheitsforschung und -versorgung. Anasthesiol Intensivmed Notfallmed Schmerzther 2022; 57:169-171. [PMID: 35320839 DOI: 10.1055/a-1736-9540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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