Distributed learning on 20 000+ lung cancer patients – The Personal Health Train

Abstract

Background and purpose: Access to healthcare data is indispensable for scientific progress and innovation. Sharing healthcare data is time-consuming and notoriously difficult due to privacy and regulatory concerns. The Personal Health Train (PHT) provides a privacy-by-design infrastructure connecting FAIR (Findable, Accessible, Interoperable, Reusable) data sources and allows distributed data analysis and machine learning. Patient data never leaves a healthcare institute. Materials and methods: Lung cancer patient-specific databases (tumor staging and post-treatment survival information) of oncology departments were translated according to a FAIR data model and stored locally in a graph database. Software was installed locally to enable deployment of distributed machine learning algorithms via a central server. Algorithms (MATLAB, code and documentation publicly available) are patient privacy-preserving as only summary statistics and regression coefficients are exchanged with the central server. A logistic regression model to predict post-treatment two-year survival was trained and evaluated by receiver operating characteristic curves (ROC), root mean square prediction error (RMSE) and calibration plots. Results: In 4 months, we connected databases with 23 203 patient cases across 8 healthcare institutes in 5 countries (Amsterdam, Cardiff, Maastricht, Manchester, Nijmegen, Rome, Rotterdam, Shanghai) using the PHT. Summary statistics were computed across databases. A distributed logistic regression model predicting post-treatment two-year survival was trained on 14 810 patients treated between 1978 and 2011 and validated on 8 393 patients treated between 2012 and 2015. Conclusion:The PHT infrastructure demonstrably overcomes patient privacy barriers to healthcare data sharing and enables fast data analyses across multiple institutes from different countries with different regulatory regimens. This infrastructure promotes global evidence-based medicine while prioritizing patient privacy.

Development of a Clinical Decision Support System for Severity Risk Prediction and Triage of COVID-19 Patients at Hospital Admission: an International Multicenter Study

“…Thirdly, the threshold to go to the hospital and hospitalisation management can vary from country to country, and we are also aware that RNA viruses can mutate rapidly and could have an impact of the performance of the models. We therefore propose that those models should be continuously updated to achieve a better performance for example using privacy preserving distributed learning approaches [ 32 , 33 ]. Fourthly, the CT features used for this study are semantic features from the first CT scan, and radiomics or deep learning approaches may improve its prognostic performance, and follow-up CT scans may yield more information.…”

Development of a Clinical Decision Support System for Severity Risk Prediction and Triage of COVID-19 Patients at Hospital Admission: an International Multicenter Study

“…We therefore propose that those models should be continuously updated for example using privacy-preserving distributed learning approaches. 29,30 Fourth, the CT features used for this study are semantic features from the first CT scan, and quantitative features automatically extracted from CT images using radiomics or deep learning approaches may improve its prognostic performance, and follow-up CT scan may yield more information. Finally, there is also the fundamental weakness of nomograms, which do not give a confidence interval to the final output.…”

Sharing Is Caring—Data Sharing Initiatives in Healthcare

“…Stations containing FAIR data may be controlled by individuals, (general) physicians, biobanks, hospitals and public or private data repositories. The Personal Health Train was applied recently to a project with 20,000+ lung cancer patients [47] and will also be used in the Coronary ARtery disease: Risk estimations and Interventions for prevention and EaRly detection (CARRIER) project [48]. Likely more projects will follow.…”