Insights that speak to the head and the heart.

Authenticx generates NLU algorithms specifically for healthcare to share immersive and intelligent insights.

Learn More

Myths vs. Facts: How Artificial Intelligence Is Changing the Way Healthcare Listens


Software that connects qualitative human emotion to quantitative metrics.​

Data collection a is only part of the equation. Understand the data by listening snd engaging with the stories your customers are sharing.

See Authenticx in Action

Clinical NLP

Natural language processing (NLP) often comes up in conversations about artificial intelligence (AI) technologies, but NLP focuses on allowing systems to ‘understand’, ‘interpret’, and even generate human language. In recent years as AI technologies have exploded in popularity and functional capabilities, NLP has emerged as a vital tool for healthcare professionals and researchers in clinical settings because of NLP’s ability to analyze, extract, and glean insights from unstructured healthcare data – from documents like clinical notes to medical imaging reports, patient-generated healthcare data, and much more. 

Clinical natural language processing refers to the ways in which machine learning technology can help extract meaningful information from various sources of clinical data. While clinical NLP and medical NLP are terms that are oftentimes used interchangeably, they both refer to the same processes. 

Because the overwhelming majority of healthcare data exists in the form of unstructured text, clinical NLP can offer healthcare professionals and medical researchers a variety of benefits. It can be difficult to discern substantive or important information from unstructured datasets, which is where NLP comes in handy. By using advanced techniques, NLP can help parse and extract relevant information to help guide medical professionals in administering the best possible care to patients. 

Some primary applications of clinical NLP include medical coding, clinical decision support, adverse event detection, as well as population health management initiatives. NLP in medical coding refers to the implementation of natural language processing techniques in order to extract procedural and diagnostic information from clinical textual data and convert it into more standardized codes that can help with billing or other administrative purposes

Clinical NLP can also play a critical part in clinical decision support, where the technology can help extract relevant information from clinical notes and other sources of unstructured data in order to provide physicians with accurate, real-time recommendations and even alerts related to patient care. When healthcare providers have a greater arsenal of tools that are their disposal, patient outcomes may improve, and the overall quality of care that patients have access to will likely increase. 

Additionally, clinical NLP applications can also support population health management applications. By analyzing gross volumes of clinical text data, NLP algorithms can identify certain trends or patterns in patient populations, which can then be used to help develop more effective interventions and improve overall population health, which substantially benefits everyone – not just doctors and their patients. 

Natural Language Processing

We covered some of the essentials related to natural language processing in the first section, but what is NLP exactly? The basic principles of NLP – natural language processing – involve analyzing human language from numerous perspectives. NLP algorithms are capable of gleaning information about syntax, semantics, and pragmatics, and they break down the language into smaller pieces like words, phrases, and sentences, in order to apply statistical and machine learning techniques to understand the meaning and context of the language

Such a robust piece of technology can be applied to the context of clinical data in a handful of ways. For instance, NLP can be leveraged to extract important information from electronic health records – information like patient demographics, previous diagnoses, and effective treatments. This information can then be used to support healthcare professionals in clinical decision-making, and ultimately improve the likelihood of positive patient outcomes. 

NLP projects with source code are also widely available, making it easier now than it ever has been before for healthcare organizations and medical researchers to apply NLP techniques to clinical datasets. These projects often include pre-trained models and libraries that can be customized or personalized for specific use cases like clinical text classification or clinical coding. 

Natural language processing for healthcare can also be utilized to help analyze unstructured data, identify patients that would be good candidates for medical trials, and help monitor adverse events in real-time, alerting healthcare professionals instantaneously when a patient experiences a serious or life-threatening event. 

Medical NLP Dataset

A medical NLP dataset can be a critical resource for training and evaluating machine learning models within the healthcare industry. These datasets typically consist of a large volume of medical text, including clinical notes, radiology reports, pathology reports, and discharge summaries, among many others. And because the process is largely automated, AI technologies are able to process much more information than human data entry specialists could, given the same amount of time. 

Sources of data for medical NLP datasets may vary somewhat, but they usually come from research studies, clinical trials, and electronic health records. Such datasets are frequently collected from multiple healthcare institutions, which may yield a variety of different textual formats, including free-text, semi-structured, and structured data. 

In order to create high-quality medical NLP datasets, data annotation, and accurate labeling are absolutely crucial. This process can involve tagging documents and text with relevant labels, such as named entities, medical concepts, and relationships between multiple concepts. While this process can be tedious, time-consuming, and requires domain expertise, the combined efforts generally yield more accurate and reliable datasets. 

In recent years, open-source medical NLP libraries have grown in popularity because of their ability to provide researchers and other healthcare professionals with access to pre-trained models and tools to help create and annotate a medical NLP library. The Clinical NLP Workshop also provides a global platform for researchers to share medical NLP datasets and evaluate machine learning models on a standardized dataset. 

Medical NLP datasets play a significant role in modern healthcare applications by enhancing the development and evaluation of machine-learning models. The data pre-processing techniques, the sources of data, and the process of data annotation and/or data labeling are essential components in creating high-quality NLP datasets. And resources like the open-source medical NLP libraries and the Clinical NLP Workshop provide researchers and healthcare experts with invaluable resources to create and evaluate medical NLP models. 

How It Works

Gain a deeper level understanding of contact center conversations with AI solutions.

See a Preview


Pull customer interaction data across vendors, products, and services into a single source of truth.


Collect quantitative and qualitative information to understand patterns and uncover opportunities.


Confidently take action with insights that close the gap between your organization and your customers.

Medical NLP Models

Medical NLP models are machine learning algorithms that are capable of leveraging natural language processing technology in order to support healthcare providers by streamlining workflows, improving patient care, and helping medical professionals make better-informed decisions. And better-informed decisions often lead to better health outcomes.

One of the main benefits of medical NLP models is their ability to automate and streamline tasks that were traditionally performed manually. Since NLP models can be effectively leveraged to extract significant information (patient diagnoses, symptoms, treatments, etc.) from medical notes, healthcare providers can populate EHRs with this information and reduce the amount of time, effort, and resources needed to make valuable information more accessible to physicians trying to provide the best quality care. 

An additional benefit of medical NLP models is their capabilities in regard to improving patient care. By analyzing scores of clinical text data, NLP models can rapidly identify patterns or trends that human reviewers sometimes miss. In some cases, NLP models can identify patients who are at an elevated risk of developing a particular disease or cancer, allowing the patient’s healthcare providers to take preventative measures and ultimately improve patient outcomes. 

NLP for EHR is yet another example of an important application of medical NLP models. EHRs are usually populated by unstructured data, which can make it difficult for healthcare professionals to extract the relevant information they need. By relying on NLP models to extract and analyze data from EHRs, healthcare providers can glean significant insights into patient health, disease progression, as well as treatment outcomes. NLP medical notes can also be analyzed by machine learning algorithms to extract information that can further support patient care processes. 

Benefits of Natural Language Processing in Healthcare

One of the most significant benefits of natural language processing in healthcare is the potential to enhance clinical decision-making by extracting important information from large amounts of unstructured data. Additionally, NLP technologies can help to improve patient outcomes by providing personalized care based on patient-specific data. 

NLP in healthcare research papers has also been an extremely valuable tool for healthcare providers and medical researchers. By automating the analysis aspect of clinical notes and other troves of unstructured data, researchers can rapidly conduct large-scale studies like never before. Additionally, NLP can help identify patients for clinical trials, which can save healthcare organizations both time and valuable resources. 

Healthcare providers must also take into account the ethical considerations regarding NLP technology when seeking to implement natural language processing in healthcare applications. For example, patient privacy must remain protected and confidential, and when healthcare providers utilize NLP technologies, patients must receive adequate notice. Furthermore, healthcare providers must ensure that algorithms used in NLP solutions are as fair and unbiased as possible. 

Natural language processing in medicine is also useful in the integration of electronic health records (EHRs). NLP can be leveraged to automatically extract relevant information from EHRs like medical histories, patient demographics, previous diagnoses, and more, which can then be used to help healthcare providers make informed clinical decisions. 

Ultimately, natural language processing is a powerful tool that’s already begun to revolutionize the healthcare industry. By enabling the analysis of unstructured health data and facilitating better clinical decision-making, NLP can improve patient outcomes and contribute greatly to medical research initiatives.

    Copy link