1
|
Schapranow MP, Bayat M, Rasheed A, Naik M, Graf V, Schmidt D, Budde K, Cardinal H, Sapir-Pichhadze R, Fenninger F, Sherwood K, Keown P, Günther OP, Pandl KD, Leiser F, Thiebes S, Sunyaev A, Niemann M, Schimanski A, Klein T. NephroCAGE-German-Canadian Consortium on AI for Improved Kidney Transplantation Outcome: Protocol for an Algorithm Development and Validation Study. JMIR Res Protoc 2023; 12:e48892. [PMID: 38133915 PMCID: PMC10770792 DOI: 10.2196/48892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 09/25/2023] [Accepted: 09/28/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Recent advances in hardware and software enabled the use of artificial intelligence (AI) algorithms for analysis of complex data in a wide range of daily-life use cases. We aim to explore the benefits of applying AI to a specific use case in transplant nephrology: risk prediction for severe posttransplant events. For the first time, we combine multinational real-world transplant data, which require specific legal and technical protection measures. OBJECTIVE The German-Canadian NephroCAGE consortium aims to develop and evaluate specific processes, software tools, and methods to (1) combine transplant data of more than 8000 cases over the past decades from leading transplant centers in Germany and Canada, (2) implement specific measures to protect sensitive transplant data, and (3) use multinational data as a foundation for developing high-quality prognostic AI models. METHODS To protect sensitive transplant data addressing the first and second objectives, we aim to implement a decentralized NephroCAGE federated learning infrastructure upon a private blockchain. Our NephroCAGE federated learning infrastructure enables a switch of paradigms: instead of pooling sensitive data into a central database for analysis, it enables the transfer of clinical prediction models (CPMs) to clinical sites for local data analyses. Thus, sensitive transplant data reside protected in their original sites while the comparable small algorithms are exchanged instead. For our third objective, we will compare the performance of selected AI algorithms, for example, random forest and extreme gradient boosting, as foundation for CPMs to predict severe short- and long-term posttransplant risks, for example, graft failure or mortality. The CPMs will be trained on donor and recipient data from retrospective cohorts of kidney transplant patients. RESULTS We have received initial funding for NephroCAGE in February 2021. All clinical partners have applied for and received ethics approval as of 2022. The process of exploration of clinical transplant database for variable extraction has started at all the centers in 2022. In total, 8120 patient records have been retrieved as of August 2023. The development and validation of CPMs is ongoing as of 2023. CONCLUSIONS For the first time, we will (1) combine kidney transplant data from nephrology centers in Germany and Canada, (2) implement federated learning as a foundation to use such real-world transplant data as a basis for the training of CPMs in a privacy-preserving way, and (3) develop a learning software system to investigate population specifics, for example, to understand population heterogeneity, treatment specificities, and individual impact on selected posttransplant outcomes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/48892.
Collapse
Affiliation(s)
- Matthieu-P Schapranow
- Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany
| | - Mozhgan Bayat
- Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany
| | - Aadil Rasheed
- Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany
| | - Marcel Naik
- Department of Nephrology and Medical Intensive Care, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Verena Graf
- Geschäftsbereich IT, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Danilo Schmidt
- Geschäftsbereich IT, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Klemens Budde
- Department of Nephrology and Medical Intensive Care, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Héloïse Cardinal
- Research Centre, Centre Hospitalier de l'Université de Montréal, Montréal, QC, Canada
| | - Ruth Sapir-Pichhadze
- Division of Nephrology and Multi-Organ Transplant Program, Department of Medicine and Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montréal, QC, Canada
| | - Franz Fenninger
- Division of Nephrology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Karen Sherwood
- Division of Nephrology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Paul Keown
- Division of Nephrology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | | | - Konstantin D Pandl
- Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Florian Leiser
- Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Scott Thiebes
- Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Ali Sunyaev
- Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | | | | | | |
Collapse
|
2
|
Toussaint PA, Leiser F, Thiebes S, Schlesner M, Brors B, Sunyaev A. Explainable artificial intelligence for omics data: a systematic mapping study. Brief Bioinform 2023; 25:bbad453. [PMID: 38113073 PMCID: PMC10729786 DOI: 10.1093/bib/bbad453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 07/28/2023] [Accepted: 11/08/2023] [Indexed: 12/21/2023] Open
Abstract
Researchers increasingly turn to explainable artificial intelligence (XAI) to analyze omics data and gain insights into the underlying biological processes. Yet, given the interdisciplinary nature of the field, many findings have only been shared in their respective research community. An overview of XAI for omics data is needed to highlight promising approaches and help detect common issues. Toward this end, we conducted a systematic mapping study. To identify relevant literature, we queried Scopus, PubMed, Web of Science, BioRxiv, MedRxiv and arXiv. Based on keywording, we developed a coding scheme with 10 facets regarding the studies' AI methods, explainability methods and omics data. Our mapping study resulted in 405 included papers published between 2010 and 2023. The inspected papers analyze DNA-based (mostly genomic), transcriptomic, proteomic or metabolomic data by means of neural networks, tree-based methods, statistical methods and further AI methods. The preferred post-hoc explainability methods are feature relevance (n = 166) and visual explanation (n = 52), while papers using interpretable approaches often resort to the use of transparent models (n = 83) or architecture modifications (n = 72). With many research gaps still apparent for XAI for omics data, we deduced eight research directions and discuss their potential for the field. We also provide exemplary research questions for each direction. Many problems with the adoption of XAI for omics data in clinical practice are yet to be resolved. This systematic mapping study outlines extant research on the topic and provides research directions for researchers and practitioners.
Collapse
Affiliation(s)
- Philipp A Toussaint
- Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany
- HIDSS4Health – Helmholtz Information and Data Science School for Health, Karlsruhe, Heidelberg, Germany
| | - Florian Leiser
- Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Scott Thiebes
- Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Matthias Schlesner
- Biomedical Informatics, Data Mining and Data Analytics, Faculty of Applied Computer Science and Medical Faculty, University of Augsburg, Augsburg, Germany
| | - Benedikt Brors
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Translational Oncology, National Center for Tumor Diseases, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ali Sunyaev
- Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany
| |
Collapse
|
3
|
Leiser F, Rank S, Schmidt-Kraepelin M, Thiebes S, Sunyaev A. Medical informed machine learning: A scoping review and future research directions. Artif Intell Med 2023; 145:102676. [PMID: 37925206 DOI: 10.1016/j.artmed.2023.102676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 06/15/2023] [Accepted: 10/02/2023] [Indexed: 11/06/2023]
Abstract
Combining domain knowledge (DK) and machine learning is a recent research stream to overcome multiple issues like limited explainability, lack of data, and insufficient robustness. Most approaches applying informed machine learning (IML), however, are customized to solve one specific problem. This study analyzes the status of IML in medicine by conducting a scoping literature review based on an existing taxonomy. We identified 177 papers and analyzed them regarding the used DK, the implemented machine learning model, and the motives for performing IML. We find an immense role of expert knowledge and image data in medical IML. We then provide an overview and analysis of recent approaches and supply five directions for future research. This review can help develop future medical IML approaches by easily referencing existing solutions and shaping future research directions.
Collapse
Affiliation(s)
- Florian Leiser
- Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Sascha Rank
- Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | | | - Scott Thiebes
- Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Ali Sunyaev
- Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany.
| |
Collapse
|