How AI-based technologies improve clinical trial design, site selection and competitive intelligence

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Clinical trials form the cornerstone of evidence-based medicine and are essential to establishing the safety and efficacy of new drugs. However, only some of the information in clinical trial reports is well-structured and searchable via keywords; much of the information is buried in unstructured text.

In the past, uncovering actionable insights from this unstructured text meant that documents such as clinical trial reports had to be searched and read individually, a process that can be time-consuming and subject to human error. It is estimated that 80% of clinical data is unstructured and difficult to analyze.

To overcome these limitations, life sciences companies are using natural language processing (NLP), an artificial intelligence-based technology that extracts and synthesizes high-value information hidden in unstructured text. NLP-based text mining solutions can an…

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The role of natural language processing in advancing disease research 

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In any area of disease research, a deep understanding of recent and future trends surrounding a particular condition is crucial to the drug discovery process. But with the volume of scientific literature increasing all the time, it is difficult to manually sift through all the existing information and correlate data in such a way to produce meaningful direction. This predicament can lead to the misallocation of resources on research in areas that are less likely to yield promising treatments.

By analyzing all literature related to a specific condition or disease, researchers can better identify which areas will likely lead to a breakthrough. Natural language processing (NLP) uses a combination of linguistics, artificial intelligence, and computer science to understand text in the same way as people. Researchers can use NLP in trend analysis to determine the rate at whic…

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What’s driving the natural language processing revolution in pharma and life sciences

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Pharmaceutical and life sciences companies are faced with a constant stream of new data flowing into often siloed information systems. About 80% of that information exists in unstructured text that is difficult to extract and use, despite its paramount importance in driving clinical and commercial outcomes.

As a result, these organizations find themselves increasingly overwhelmed with volumes of inaccessible data. At the same time, researchers and data scientists lack effective search tools to find the right information in this “big data” tsunami, causing them to miss opportunities to enhance patient safety, improve clinical trial design, identify previously undetected biomarkers and better understand the voice of the customer.

To overcome the limitations of time-consuming, manual searches through mountains of data, pharma and life sciences companies are looking to artificial…

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