Increasing availability of electronic health databases capturing real-world experiences with medical products has garnered much interest in their use for pharmacoepidemiologic and pharmacovigilance studies. Applications of Computational Statistics > Health and Medical Data/Informatics.A possible solution to improve data quality and accessibility of unstructured data is developing machine learning methods that can generate clinically relevant synthetic data, and accelerating further research on privacy preserving techniques such as deidentification and pseudonymization of clinical text. Further, generalizing and explaining the result of machine learning models are important problems for healthcare, and these are open challenges. Data quality is also an important challenge in which methods for improving data completeness, conformity and plausibility are needed. However, access to clinical data is still very restricted due to data sensitivity and ethical issues. Although there exist many challenges that have not been fully addressed, which can hinder the use of unstructured data, there are clear opportunities for well‐designed diagnosis and decision support tools that efficiently incorporate both structured and unstructured data for extracting useful information and provide better outcomes. Advanced statistical algorithms in natural language processing, machine learning, deep learning, and radiomics have increasingly been used for analyzing clinical text and images. Among unstructured data, clinical text and images are the two most popular and important sources of information.
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Besides structured data, unstructured data in EHRs can provide extra, valuable information but the analytics processes are complex, time‐consuming, and often require excessive manual effort.
Electronic health records (EHR) contain a lot of valuable information about individual patients and the whole population.