Modern Clinical Text Mining: A Guide and Review.

Area of Science:

• Health Informatics
• Natural Language Processing
• Machine Learning

Background:

• Electronic health records (EHRs) contain vast amounts of unstructured text data.
• This unstructured data holds significant potential for healthcare quality improvement, research, and operations.
• Current methods often fail to fully leverage the rich information within clinical notes.

Purpose of the Study:

• To provide an overview of clinical text mining for newcomers to the field.
• To highlight recent advancements in clinical text mining methods, including deep learning.
• To identify barriers to implementing these advanced techniques in real-world healthcare settings.

Main Methods:

• Review of recent literature on clinical text mining techniques.
• Focus on machine learning and deep learning approaches applied to EHR data.
• Analysis of challenges and practical considerations for implementation.

Main Results:

• Clinical text mining has evolved significantly, moving from rule-based systems to sophisticated machine learning and deep learning models.
• New tasks and methods have emerged, enhancing the ability to extract information from EHRs.
• Significant barriers remain in translating technical advancements into practical healthcare applications.

Conclusions:

• Clinical text mining offers powerful tools for unlocking the value in EHR unstructured data.
• Understanding recent advances and implementation challenges is crucial for effective adoption.
• Bridging the gap between research and practice is essential for realizing the full potential of clinical text mining.