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Clinical Trials01:16

Clinical Trials

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Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
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Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
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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 problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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Optimizing clinical trials recruitment via deep learning.

Jelena Gligorijevic1, Djordje Gligorijevic1, Martin Pavlovski1,2

  • 1Center for Data Analytics and Biomedical Informatics, Temple University, Philadelphia, Pennsylvania, USA.

Journal of the American Medical Informatics Association : JAMIA
|June 13, 2019
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Summary
This summary is machine-generated.

DeepMatch (DM) is a novel deep learning approach that optimizes clinical trial investigator selection. This method improves investigator ranking and performance detection, leading to more efficient and cost-effective drug development.

Keywords:
clinical trialsdeep learningdeep matchingelectronic health recordspointwise ranking

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Area of Science:

  • Biomedical research
  • Clinical trial management
  • Artificial intelligence in healthcare

Background:

  • Clinical trials are crucial for new treatment development but are complex and expensive.
  • Selecting high-enrolling investigators is vital for efficient trial execution and cost control.
  • Current methods for investigator selection may not be optimal.

Purpose of the Study:

  • To introduce DeepMatch (DM), a novel deep learning approach for optimizing clinical trial investigator selection.
  • To rank investigators based on their predicted enrollment performance for new clinical trials.
  • To improve the efficiency and reduce the cost of clinical trial execution.

Main Methods:

  • DeepMatch (DM) utilizes deep learning to analyze heterogeneous data from investigators and trials.
  • The approach learns from existing data to predict investigator enrollment performance.
  • Investigators are ranked based on their expected performance in upcoming clinical trials.

Main Results:

  • A large-scale evaluation on 2618 studies demonstrated DM's effectiveness.
  • DM improved investigator ranking by up to 19% compared to state-of-the-art methods.
  • DM enhanced the detection of top/bottom performing investigators by up to 10%.

Conclusions:

  • DeepMatch (DM) offers substantial improvements over current industry standards for investigator selection.
  • DM enhances the assessment of investigator enrollment potential and speeds up list generation.
  • The approach facilitates data-informed decisions for selecting investigators, optimizing trials, and reducing therapy costs.