Modern clinical research: How rapid learning health care and cohort multiple randomised clinical trials complement traditional evidence based medicine

Lambin, Philippe; Zindler, Jaap; Voorde, Lien Van De; Jacobs, Maria; Eekers, Daniëlle; Peerlings, Jurgen; Reymen, Bart; Larue, Ruben T. H. M.; Deist, Timo M.; Jong, Evelyn E.C. de; Even, Aniek J.G.; Berlanga, Adriana; Cheng, Qing; Carvalho, Sara; Leijenaar, Ralph T.H.; Zegers, Catharina M.L.; Limbergen, Evert Van; Berbée, Maaike; Elmpt, Wouter van; Oberije, Cary; Houben, Ruud; Dekker, André; Boersma, Liesbeth; Verhaegen, Frank; Bosmans, Geert; Hoebers, Frank; Smits, Kim M.; Walsh, Seán

doi:10.3109/0284186x.2015.1062136

Cited by 63 publications

(49 citation statements)

References 57 publications

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“…Evolution in clinical practice produces legacy issues for modeling. 80,81 In this study, bNED is defined by either the American Society for Therapeutic Radiology and Oncology (ASTRO-three consecutive PSA rises post the PSA nadir) or the Phoenix Consensus Conference (PCC-2 ng/ml PSA rise above the PSA nadir) definition of biochemical failure. 82 Control rates defined by the ASTRO definition appear to be worse at 2-5 yr, are convergent at 5-8 yr, and superior at 8-12 yr in comparison with the PCC definition.…”

Section: L the Definition Of Clinical Outcomesmentioning

confidence: 99%

A validated tumor control probability model based on a meta‐analysis of low, intermediate, and high‐risk prostate cancer patients treated by photon, proton, or carbon‐ion radiotherapy

et al. 2016

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Section: L the Definition Of Clinical Outcomesmentioning

confidence: 99%

A validated tumor control probability model based on a meta‐analysis of low, intermediate, and high‐risk prostate cancer patients treated by photon, proton, or carbon‐ion radiotherapy

et al. 2016

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“…While various definitions of the LHS exist in the literature, the most authoritative source is the Institute of Medicine (IOM), which envisions “the development of a continuously learning health system in which science, informatics, incentives, and culture are aligned for continuous improvement and innovation, with best practices seamlessly embedded in the delivery process and new knowledge captured as an integral by-product of the delivery experience” [1]. The current operationalization of various LHS initiatives also demonstrates the important role that patient data plays in supporting continuous learning, improving decision-making [2–5], informing new research directions [6], and creating more efficient, effective, and safe systems [7]. …”

Section: Introductionmentioning

confidence: 99%

Architectural frameworks: defining the structures for implementing learning health systems

et al. 2017

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BackgroundThe vision of transforming health systems into learning health systems (LHSs) that rapidly and continuously transform knowledge into improved health outcomes at lower cost is generating increased interest in government agencies, health organizations, and health research communities. While existing initiatives demonstrate that different approaches can succeed in making the LHS vision a reality, they are too varied in their goals, focus, and scale to be reproduced without undue effort. Indeed, the structures necessary to effectively design and implement LHSs on a larger scale are lacking. In this paper, we propose the use of architectural frameworks to develop LHSs that adhere to a recognized vision while being adapted to their specific organizational context. Architectural frameworks are high-level descriptions of an organization as a system; they capture the structure of its main components at varied levels, the interrelationships among these components, and the principles that guide their evolution. Because these frameworks support the analysis of LHSs and allow their outcomes to be simulated, they act as pre-implementation decision-support tools that identify potential barriers and enablers of system development. They thus increase the chances of successful LHS deployment.DiscussionWe present an architectural framework for LHSs that incorporates five dimensions—goals, scientific, social, technical, and ethical—commonly found in the LHS literature. The proposed architectural framework is comprised of six decision layers that model these dimensions. The performance layer models goals, the scientific layer models the scientific dimension, the organizational layer models the social dimension, the data layer and information technology layer model the technical dimension, and the ethics and security layer models the ethical dimension. We describe the types of decisions that must be made within each layer and identify methods to support decision-making.ConclusionIn this paper, we outline a high-level architectural framework grounded in conceptual and empirical LHS literature. Applying this architectural framework can guide the development and implementation of new LHSs and the evolution of existing ones, as it allows for clear and critical understanding of the types of decisions that underlie LHS operations. Further research is required to assess and refine its generalizability and methods.

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“…Large companies such as Apple, Google, Facebook and Microsoft are applying and promoting such technologies for their customer privacy concerns [68]. Another approach is the utilization of distributed (rapid) learning presented in Eurocat [69,70], where algorithms instead of data are shared across the different institutions. With all these exciting breakthroughs in ML and their potential in oncology, one still needs to be careful when wielding such methods and meticulously design the data validation experiments to avoid pitfalls of overfitting and misinformation [71].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning and Imaging Informatics in Oncology

et al. 2018

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In the era of personalized and precision medicine, informatics technologies utilizing machine learning (ML) and quantitative imaging are witnessing a rapidly increasing role in medicine in general and in oncology in particular. This expanding role ranges from computer-aided diagnosis to decision support of treatments with the potential to transform the current landscape of cancer management. In this review, we aim to provide an overview of ML methodologies and imaging informatics techniques and their recent application in modern oncology. We will review example applications of ML in oncology from the literature, identify current challenges and highlight future potentials.

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Modern clinical research: How rapid learning health care and cohort multiple randomised clinical trials complement traditional evidence based medicine

Cited by 63 publications

References 57 publications

A validated tumor control probability model based on a meta‐analysis of low, intermediate, and high‐risk prostate cancer patients treated by photon, proton, or carbon‐ion radiotherapy

A validated tumor control probability model based on a meta‐analysis of low, intermediate, and high‐risk prostate cancer patients treated by photon, proton, or carbon‐ion radiotherapy

Architectural frameworks: defining the structures for implementing learning health systems

Machine Learning and Imaging Informatics in Oncology

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