A Decentralized Framework for Biostatistics and Privacy Concerns

Mangold, Paul; Filiot, Alexandre; Moussa, Mouhamed Djahoum; Sobanski, Vincent; Ficheur, Grégoire; Andrey, Paul; Lamer, Antoine

doi:10.3233/shti200710

Cited by 6 publications

(5 citation statements)

References 5 publications

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“…In a previous study, we demonstrated that odd-ratios and their confidence intervals resulting from a decentralized logistic regression were consistent with the ones obtained with the traditional centralized model [3]. Other studies implemented Support-Vector Machine, Principal Components Analysis or Neural Networks using FL [4].…”

Section: Introductionsupporting

confidence: 76%

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Specifications for the Routine Implementation of Federated Learning in Hospitals Networks

Lamer

Filiot

Bouillard

et al. 2021

Studies in Health Technology and Informatics

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We collected user needs to define a process for setting up Federated Learning in a network of hospitals. We identified seven steps: consortium definition, architecture implementation, clinical study definition, data collection, initialization, model training and results sharing. This process adapts certain steps from the classical centralized multicenter framework and brings new opportunities for interaction thanks to the architecture of the Federated Learning algorithms. It is open for completion to cover a variety of scenarios.

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Section: Introductionsupporting

confidence: 76%

“…We did not address the choice of a specific tool or library to perform federated computations. We note that existing tools [3,8] could fit within our framework, but usually only tackle the model training step, and are for the most still limited to simulated network environments.…”

Section: Discussionmentioning

confidence: 99%

Specifications for the Routine Implementation of Federated Learning in Hospitals Networks

Lamer

Filiot

Bouillard

et al. 2021

Studies in Health Technology and Informatics

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“…The material provided here could be used and enhanced by other centers. In combination with federated learning [ 47 ], the OMOP CDM provides tools needed for conducting reproducible research.…”

Section: Discussionmentioning

confidence: 99%

Transforming Anesthesia Data Into the Observational Medical Outcomes Partnership Common Data Model: Development and Usability Study

Lamer¹,

Abou-Arab²,

Bourgeois³

et al. 2021

J Med Internet Res

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Background Electronic health records (EHRs, such as those created by an anesthesia management system) generate a large amount of data that can notably be reused for clinical audits and scientific research. The sharing of these data and tools is generally affected by the lack of system interoperability. To overcome these issues, Observational Health Data Sciences and Informatics (OHDSI) developed the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to standardize EHR data and promote large-scale observational and longitudinal research. Anesthesia data have not previously been mapped into the OMOP CDM. Objective The primary objective was to transform anesthesia data into the OMOP CDM. The secondary objective was to provide vocabularies, queries, and dashboards that might promote the exploitation and sharing of anesthesia data through the CDM. Methods Using our local anesthesia data warehouse, a group of 5 experts from 5 different medical centers identified local concepts related to anesthesia. The concepts were then matched with standard concepts in the OHDSI vocabularies. We performed structural mapping between the design of our local anesthesia data warehouse and the OMOP CDM tables and fields. To validate the implementation of anesthesia data into the OMOP CDM, we developed a set of queries and dashboards. Results We identified 522 concepts related to anesthesia care. They were classified as demographics, units, measurements, operating room steps, drugs, periods of interest, and features. After semantic mapping, 353 (67.7%) of these anesthesia concepts were mapped to OHDSI concepts. Further, 169 (32.3%) concepts related to periods and features were added to the OHDSI vocabularies. Then, 8 OMOP CDM tables were implemented with anesthesia data and 2 new tables (EPISODE and FEATURE) were added to store secondarily computed data. We integrated data from 5,72,609 operations and provided the code for a set of 8 queries and 4 dashboards related to anesthesia care. Conclusions Generic data concerning demographics, drugs, units, measurements, and operating room steps were already available in OHDSI vocabularies. However, most of the intraoperative concepts (the duration of specific steps, an episode of hypotension, etc) were not present in OHDSI vocabularies. The OMOP mapping provided here enables anesthesia data reuse.

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“…Studies have consistently shown that FL models outperformed traditional single-institution ML architectures [148,[151][152][153][154][155][156] and may be comparable to models built via central learning (centralized database) [137,138,[143][144][145][146][147][148][149][150]. In some studies, the FL approach has even been shown to be superior to alternative collaborative learning methods [137,145].…”

Section: Federated Learningmentioning

confidence: 99%

Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology

Lim

Hong²,

Lam

et al. 2022

Current Opinion in Ophthalmology

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Purpose of reviewThe application of artificial intelligence (AI) in medicine and ophthalmology has experienced exponential breakthroughs in recent years in diagnosis, prognosis, and aiding clinical decision-making. The use of digital data has also heralded the need for privacy-preserving technology to protect patient confidentiality and to guard against threats such as adversarial attacks. Hence, this review aims to outline novel AI-based systems for ophthalmology use, privacy-preserving measures, potential challenges, and future directions of each. Recent findingsSeveral key AI algorithms used to improve disease detection and outcomes include: Data-driven, imagedriven, natural language processing (NLP)-driven, genomics-driven, and multimodality algorithms. However, deep learning systems are susceptible to adversarial attacks, and use of data for training models is associated with privacy concerns. Several data protection methods address these concerns in the form of blockchain technology, federated learning, and generative adversarial networks. SummaryAI-applications have vast potential to meet many eyecare needs, consequently reducing burden on scarce healthcare resources. A pertinent challenge would be to maintain data privacy and confidentiality while supporting AI endeavors, where data protection methods would need to rapidly evolve with AI technology needs. Ultimately, for AI to succeed in medicine and ophthalmology, a balance would need to be found between innovation and privacy.

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A Decentralized Framework for Biostatistics and Privacy Concerns

Cited by 6 publications

References 5 publications

Specifications for the Routine Implementation of Federated Learning in Hospitals Networks

Specifications for the Routine Implementation of Federated Learning in Hospitals Networks

Transforming Anesthesia Data Into the Observational Medical Outcomes Partnership Common Data Model: Development and Usability Study

Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology

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