Beyond the Randomized Clinical Trial: Innovative Data Science to Close the Pediatric Evidence Gap

Goulooze, Sebastiaan C.; Zwep, Laura B.; Vogt, Julia E.; Krekels, Elke H. J.; Hankemeier, Thomas; Anker, John N. van den; Knibbe, Catherijne A. J.

doi:10.1002/cpt.1744

Cited by 30 publications

(26 citation statements)

References 74 publications

(172 reference statements)

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“…In recent years, medical/health data science and practices are among the issues that are carefully emphasized by various government agencies as well as private companies (Goulooze et al 2019;Paul et al 2017;Paul et al 2016;Ford et al 2019;Dixon et al 2020;Dammann and Smart 2019). Image processing methods are also one of the basic algorithms used when developing these software solutions and applications.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: A topic-based hierarchical publish/subscribe messaging middleware for COVID-19 detection in X-ray image and its metadata

Eken

2020

Soft Comput

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Putting real-time medical data processing applications into practice comes with some challenges such as scalability and performance. Processing medical images from different collaborators is an example of such applications, in which chest X-ray data are processed to extract knowledge. It is not easy to process data and get the required information in real time using central processing techniques when data get very large in size. In this paper, real-time data are filtered and forwarded to the right processing node by using the proposed topic-based hierarchical publish/subscribe messaging middleware in the distributed scalable network of collaborating computation nodes instead of classical approaches of centralized computation. This enables processing streaming medical data in near real time and makes a warning system possible. End users have the capability of filtering/searching. The returned search results can be images (COVID-19 or non-COVID-19) and their meta-data are gender and age. Here, COVID-19 is detected using a novel capsule network-based model from chest X-ray images. This middleware allows for a smaller search space as well as shorter times for obtaining search results. Keywords Capsule networks Á Topic-based hierarchical publish/subscribe Á COVID-19 detection Á Hybrid intelligence Á X-ray images Á Medical data management Communicated by Valentina E. Balas.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: A topic-based hierarchical publish/subscribe messaging middleware for COVID-19 detection in X-ray image and its metadata

Eken

2020

Soft Comput

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“…Leveraging different data sources, such as RCTs and EHRs, to train machine learning methods provides more evidence for the effectiveness of treatments. 84,85 PK/ PD approaches may allow us to introduce priors into machine learning methods and improve their interpretability and performance. 9,17,82 The potential to use PK/PD models to drive machine learning models or to support validation are important aspects that require further collaboration across the quantitative sciences.…”

Section: Limitations and Future Research Directionsmentioning

confidence: 99%

From Real‐World Patient Data to Individualized Treatment Effects Using Machine Learning: Current and Future Methods to Address Underlying Challenges

Bica

Alaa

Lambert

et al. 2020

Clin Pharma and Therapeutics

133

125

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Clinical decision making needs to be supported by evidence that treatments are beneficial to individual patients. Although randomized control trials (RCTs) are the gold standard for testing and introducing new drugs, due to the focus on specific questions with respect to establishing efficacy and safety vs. standard treatment, they do not provide a full characterization of the heterogeneity in the final intended treatment population. Conversely, real‐world observational data, such as electronic health records (EHRs), contain large amounts of clinical information about heterogeneous patients and their response to treatments. In this paper, we introduce the main opportunities and challenges in using observational data for training machine learning methods to estimate individualized treatment effects and make treatment recommendations. We describe the modeling choices of the state‐of‐the‐art machine learning methods for causal inference, developed for estimating treatment effects both in the cross‐section and longitudinal settings. Additionally, we highlight future research directions that could lead to achieving the full potential of leveraging EHRs and machine learning for making individualized treatment recommendations. We also discuss how experimental data from RCTs and Pharmacometric and Quantitative Systems Pharmacology approaches can be used to not only improve machine learning methods, but also provide ways for validating them. These future research directions will require us to collaborate across the scientific disciplines to incorporate models based on RCTs and known disease processes, physiology, and pharmacology into these machine learning models based on EHRs to fully optimize the opportunity these data present.

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“…The framework integrates all available data and information on the disease and the compounds. In addition to PK and PD models, systems biology or systems pharmacology models [18] and machine learning based on, for example, imaging data [20] or even artificial intelligence (AI) [21,22] are important tools. Figure 1 shows the proposed MID3 strategy for the rapid development of anti-TB regimens through the prediction of human-concentration-time relationships (PK), exposure-response relationships (PK-PD) and DDIs to select FIH doses, as well as the prediction of Phase II and Phase III drug regimens.…”

Section: Introductionmentioning

confidence: 99%

Model-Informed Drug Discovery and Development Strategy for the Rapid Development of Anti-Tuberculosis Drug Combinations

et al. 2020

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The increasing emergence of drug-resistant tuberculosis requires new effective and safe drug regimens. However, drug discovery and development are challenging, lengthy and costly. The framework of model-informed drug discovery and development (MID3) is proposed to be applied throughout the preclinical to clinical phases to provide an informative prediction of drug exposure and efficacy in humans in order to select novel anti-tuberculosis drug combinations. The MID3 includes pharmacokinetic-pharmacodynamic and quantitative systems pharmacology models, machine learning and artificial intelligence, which integrates all the available knowledge related to disease and the compounds. A translational in vitro-in vivo link throughout modeling and simulation is crucial to optimize the selection of regimens with the highest probability of receiving approval from regulatory authorities. In vitro-in vivo correlation (IVIVC) and physiologically-based pharmacokinetic modeling provide powerful tools to predict pharmacokinetic drug-drug interactions based on preclinical information. Mechanistic or semi-mechanistic pharmacokinetic-pharmacodynamic models have been successfully applied to predict the clinical exposure-response profile for anti-tuberculosis drugs using preclinical data. Potential pharmacodynamic drug-drug interactions can be predicted from in vitro data through IVIVC and pharmacokinetic-pharmacodynamic modeling accounting for translational factors. It is essential for academic and industrial drug developers to collaborate across disciplines to realize the huge potential of MID3.

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Beyond the Randomized Clinical Trial: Innovative Data Science to Close the Pediatric Evidence Gap

Cited by 30 publications

References 74 publications

RETRACTED ARTICLE: A topic-based hierarchical publish/subscribe messaging middleware for COVID-19 detection in X-ray image and its metadata

RETRACTED ARTICLE: A topic-based hierarchical publish/subscribe messaging middleware for COVID-19 detection in X-ray image and its metadata

From Real‐World Patient Data to Individualized Treatment Effects Using Machine Learning: Current and Future Methods to Address Underlying Challenges

Model-Informed Drug Discovery and Development Strategy for the Rapid Development of Anti-Tuberculosis Drug Combinations

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