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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Key Contributions in Clinical Research Informatics.

Christel Daniel1,2, Ali Bellamine1, Dipak Kalra3

  • 1Information Technology Department, AP-HP, F-75012 Paris, France.

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Summary

Clinical Research Informatics (CRI) research in 2020 focused on data science, particularly AI/ML algorithms using real-world data. The COVID-19 pandemic highlighted the critical need for international data sharing and collaborative analysis.

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Area of Science:

  • Clinical Research Informatics (CRI)
  • Data Science
  • Artificial Intelligence/Machine Learning (AI/ML)

Background:

  • Current research in Clinical Research Informatics (CRI) is rapidly evolving.
  • The year 2020 saw significant advancements in the field.

Purpose of the Study:

  • To summarize key contributions to Clinical Research Informatics (CRI) in 2020.
  • To identify and highlight the best papers published in the field during that year.

Main Methods:

  • A comprehensive bibliographic search of CRI literature was conducted using PubMed.
  • A rigorous double-blind review process, followed by external peer review and a consensus meeting, was employed to select the top papers.

Main Results:

  • Four best papers were selected from 877 published in 2020.
  • Selected papers cover disease trajectory inference from clinical documents, improved synthetic Electronic Health Record (EHR) data generation using Generative Adversarial Networks (GANs), advanced methods for adverse drug event detection, and the implications of data quality assessment for EHR analysis.

Conclusions:

  • The CRI field's primary focus is on data science, with a strong emphasis on AI/ML algorithms and the use of real-world and synthetic EHR data.
  • The COVID-19 pandemic underscored the vital importance of timely, high-quality international data sharing and collaborative analysis for informing policy decisions.