All Insights Case Study Mining Unstructured Data Using NLP And ML To Improve Quality Of Patient Care

    Mining Unstructured Data Using NLP And ML To Improve Quality Of Patient Care

    Artificial Intelligence

    Mining Unstructured Data Using NLP And ML To Improve Quality Of Patient Care

    Leverage Machine Learning (ML)/Natural Language Processing (NLP) to capture and analyze unstructured data, EHR and EMR to improve quality of patient care.

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    Mining Unstructured Data Using NLP And ML To Improve Quality Of Patient Care

    Over 80% of enterprise data today is unstructured1 including text, voice, image or pdf files, and their volume continues to expand.

    Converting big data into meaningful information is a growing challenge. For Life Sciences and healthcare, making sense of unstructured data presents an opportunity to get a deeper understanding of patients, physicians, and other stakeholders. Processing, managing and utilizing these untapped assets has been made possible by artificial intelligence (AI), big data technologies and advanced analytics like Machine Learning (ML) and Natural Language Processing (NLP).

    Axtria’s use case showcases the use of ML/NLP to capture biomarker data and other biographic information from scanned Electronic Health Records (EHR) and Electronic Medical Records (EMR) for a diversified healthcare company to:

    • better understand the disease area (BRCA)
    • identify a new cancer patient population
    • aid physician (HCP) decision making thereby improving treatment patterns
    • improving patient adherence through disease progression, and
    • improving the overall quality of care

    Learn More - "AI/ML Trends Taking The Life Sciences Industry By Storm"

    Download the case study to understand Axtria’s approach to analyze ~7 terabytes of unstructured data.

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