Unleashing the Potential of Medical NLP in Healthcare
Healthcare organizations are collecting and storing more patient data than ever. However, much of this data is in unstructured form, making it difficult to derive valuable insights from it. Fortunately, advances in natural language processing (NLP) are making it possible for healthcare organizations to derive searchable, actionable insights from data that was previously inaccessible.
Despite these advancements, caution is necessary when using NLP technology. For medical NLP applications, especially in complex fields like healthcare, inaccurate results can be disastrous. Therefore, it is essential to leverage deep-learning models and ensure that medical experts knowledgeable in medical language are building these models, and to make them explainable to medical experts.
Healthcare providers generate massive quantities of historical patient data that is stored in electronic health records (EHR) systems, with most of this data in unstructured text. However, unstructured data isn’t actionable unless it can be retrieved, understood, and contextualized. Historically, providers have had human experts pore through unstructured data to uncover and interpret relevant clinical information. This process can’t be scaled to allow millions of unstructured notes to be accessed and gleaned for useful information in a timely fashion.
In recent years, healthcare organizations have tried to apply NLP to unstructured clinical data. Results have been subpar, though, because the NLP technology used to date has been far from medical grade. Thus, it fails when presented with ambiguity in the form of data it can’t interpret.
Deep learning is quickly transforming how medical NLP can help both providers at the point of care and medical researchers by quickly finding relevant clinical information within massive amounts of unstructured data. However, it’s essential to ensure deep learning models are trained on highly accurate medical knowledge. This presents significant opportunities for healthcare providers and researchers to discover valuable information related to disease progression, treatment efficacy, population health trends, and many other use cases that would have been infeasible to identify using manual data review and analysis techniques.
The goal of medical NLP and deep learning models for medicine should not be to replace clinicians but to provide them with the most accurate and relevant clinical information about a patient at the point of care. Accomplishing this will require a collaboration between AI-powered medical NLP and clinicians with vast medical knowledge, which will finally deliver on the promise of medical NLP.
About the Author
Anoop Sarkar, PhD, is the Chief Technology Officer for emtelligent, a leader in the development of clinical-grade natural language processing (NLP) software for healthcare organizations. Dr. Sarkar is a renowned expert in machine learning for NLP and also serves as Professor of Computer Science at Simon Fraser University in British Columbia. He holds a PhD in Computer Science from the University of Pennsylvania.
Editor Notes
It is fascinating to see how technological advancements like NLP are transforming the healthcare sector, allowing for better patient care and research opportunities. GPT-3, for example, is an AI language model that can encode, evaluate information, and generate text, and is making strides in text understanding, language translation, and even assisting in therapy sessions. However, it’s vital to ensure that we use this technology responsibly and collaboratively with medical professionals. To stay up to date with the latest advancements in AI and healthcare, be sure to visit GPT News Room.
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