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Data-driven Clinical Decision Processes

Section edited by Enrico Capobianco

Changes and transformations enabled by Big Data have direct effects on Translational Medicine. At one end, superior precision is expected from a more data-intensive and individualized medicine, thus accelerating scientific discovery and innovation (in diagnosis, therapy, disease management etc.). At the other end, the scientific method needs to adapt to the increased diversity that data presents, and this too can be a beneficial aspect, potentially revealing more on how a disease manifests or progresses.

Patient-focused health data provides augmented complexity too, far beyond the simple need of testing hypotheses or validating models. Clinical decision support systems (CDSS) will increasingly deal with such complexity by developing efficient high-performance algorithms and creating a next generation inferential tools for clinical use. Additionally, new protocols for sharing digital information and effectively integrating patients data will need to be CDSS embedded features in view of suitable data harmonization aimed at improved diagnosis, therapy assessment and prevention.

This section welcomes multidisciplinary research, and aims to promote scientific interactions and accelerate the establishment of CDSS in the clinical practice.

  1. The burden of chronic and societal diseases is affected by many risk factors that can change over time. The minimalisation of disease-associated risk factors may contribute to long-term health. Therefore, new ...

    Authors: Eva Kriegova, Milos Kudelka, Martin Radvansky and Jiri Gallo
    Citation: Journal of Translational Medicine 2021 19:68
  2. An important task in developing accurate public health intervention evaluation methods based on historical interrupted time series (ITS) records is to determine the exact lag time between pre- and post-interve...

    Authors: Hossein Bonakdari, Jean-Pierre Pelletier and Johanne Martel-Pelletier
    Citation: Journal of Translational Medicine 2020 18:466
  3. Checking appropriateness of blood transfusion for quality assurance required enormous usage of time and human resources from the healthcare system. We report here a new machine learning algorithm for checking ...

    Authors: Yuanyuan Yao, Jenny Cifuentes, Bin Zheng and Min Yan
    Citation: Journal of Translational Medicine 2019 17:340

    The Letter to the Editor to this article has been published in Journal of Translational Medicine 2021 19:175

  4. The aim of the present review is to discuss how the promising field of biobanking can support health care research strategies. As the concept has evolved over time, biobanks have grown from simple biological s...

    Authors: Luigi Coppola, Alessandra Cianflone, Anna Maria Grimaldi, Mariarosaria Incoronato, Paolo Bevilacqua, Francesco Messina, Simona Baselice, Andrea Soricelli, Peppino Mirabelli and Marco Salvatore
    Citation: Journal of Translational Medicine 2019 17:172
  5. The available prognostic scoring systems for severe acute pancreatitis (SAP) have limitations that restrict their clinical value. The aim of this study was to develop a simple model (score) that could rapidly ...

    Authors: Wandong Hong, Keith D. Lillemoe, Shuang Pan, Vincent Zimmer, Evangelos Kontopantelis, Simon Stock, Maddalena Zippi, Chao Wang and Mengtao Zhou
    Citation: Journal of Translational Medicine 2019 17:146
  6. Chronic kidney disease (CKD) leads to end-stage renal failure and cardiovascular events. An attribute to these progressions is abnormalities in inflammation, which can be evaluated using the neutrophil-to-lymp...

    Authors: Qiongjing Yuan, Jinwei Wang, Zhangzhe Peng, Qiaoling Zhou, Xiangcheng Xiao, Yanyun Xie, Wei Wang, Ling Huang, Wenbin Tang, Danni Sun, Luxia Zhang, Fang Wang, Ming-Hui Zhao, Lijian Tao, Kevin He and Hui Xu
    Citation: Journal of Translational Medicine 2019 17:86
  7. Changes and transformations enabled by Big Data have direct effects on Translational Medicine. At one end, superior precision is expected from a more data-intensive and individualized medicine, thus accelerati...

    Authors: Enrico Capobianco
    Citation: Journal of Translational Medicine 2019 17:44
  8. Immunotherapies have emerged as one of the most promising approaches to treat patients with cancer. Recently, the entire medical oncology field has been revolutionized by the introduction of immune checkpoints...

    Authors: Giuseppe V. Masucci, Alessandra Cesano, Alexander Eggermont, Bernard A. Fox, Ena Wang, Francesco M. Marincola, Gennaro Ciliberto, Kevin Dobbin, Igor Puzanov, Janis Taube, Jennifer Wargo, Lisa H. Butterfield, Lisa Villabona, Magdalena Thurin, Michael A. Postow, Paul M. Sondel…
    Citation: Journal of Translational Medicine 2017 15:223
  9. The progression of complex diseases, such as diabetes and cancer, is generally a nonlinear process with three stages, i.e., normal state, pre-disease state, and disease state, where the pre-disease state is a ...

    Authors: Pei Chen, Yongjun Li, Xiaoping Liu, Rui Liu and Luonan Chen
    Citation: Journal of Translational Medicine 2017 15:217