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NATURAL LANGUAGE PROCESSING: A Brief Tutorial for Health Systems

Key Concepts in Natural Language Processing

Presented by Frank Manion, Ph.D., FAMIA

Dr. Manion is VP for Innovation at Melax Technologies, guiding the development of next-generation NLP applications. He has held C-level positions in biomedical informatics and IT at two major comprehensive cancer centers. His own research is in the area of formal semantics and biomedical ontologies.

Dr. Manion presented the "Natural Language Processing: A Brief Tutorial for Health Systems" webinar for the Houston, TX chapter of HIMSS in February 2021.

This eBook overviews key concepts presented during Dr. Manion's webinar.

What is Natural Language Processing?

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) and Computer Science that applies algorithmic approaches to derive data and information from textual information.

Why Does it Matter?

Studies have shown that 80% to 90% of available information and data in Electronic Health Record (EHR) text artifacts (notes, H&P, radiology, pathology, etc.) remain accessible ONLY through the text notes.

Is NLP Being Used in Healthcare Settings?

Yes, both public domain and commercial software packages are available for clinical NLP. Systems are rated across a number of tasks to achieve an F1 performance of at least 90% accuracy. National and international challenge projects are used to test NLP systems against known results to check the accuracy level.

NLP is highly regarded by:

What the National Committee for Quality Assurance ( says:

"Moving to more uniform implementation of national electronic data transfer standards and use of technology such as natural language processing are some of the ways we can begin to address this [quality measures/HEDIS].”

How can clinicians and researchers access the 80% to 90% of data available only in text notes?

Natural language processing's methodology identifies relevant unstructured data and relational information.

Extracting data based on relationships makes the information actionable. For example, NLP can find and exclude familial history in a patient's medical history so that a family member's health history is not confused with the patient's. NLP can determine whether the patient has had a history of hypertension or if hypertension is a family trait.

Similarly, NLP can verify whether a symptom noted in a chart relates to a specific condition, such as COVID.

Examples of NLP Uses:

NLP Learns Quickly, so Users see Results Quickly

Machine learning-based methodologies identify and learn patterns and features from the existing annotated text. Once the patterns are identified, machine learning-based NLP can quickly employ that learning to other unstructured data.

Rule-based NLP learns from a pre-determined set of codes and rules and recognizes those patterns in documents. It's a good system for one-off projects.

Melax NLP Builds Pipelines to Facilitate Information Flow

With a pipeline, a user can amass information on any number of conditions, co-morbidities, or cohorts within a set of unstructured data. Melax offers 13 pre-built pipelines that help track cancer, social determinants, and COVID-19 signs and symptoms, to name a few. Melax also provides 12 additional sub-pipelines to help users dive deeper into clinical data.

Pre-built data extraction conduits provide seamless use while also offering the capability to apply advanced Melax technology if needed.

The Melax Tech NLP Product Line:

CLAMP, Clinical Language Annotation Modeling, and Processing Toolkit

Melax Tech's CLAMP is a comprehensive clinical Natural Language Processing (NLP) software.

CLAMP enables the recognition and automatic encoding of clinical information.

LANN, a Literate Annotation Tool

Melax Tech's LANN, is designed for efficient, high-quality text annotation to support information extraction tasks, including named entity recognition, relation extraction, and concept normalization in clinical NLP.

A turn-key team-focused application to develop and implement your gold standard for annotation.

EHR Chart Review

By leveraging state-of-art information retrieval and information extraction technologies, VITAL NLP provides a powerful yet user-friendly platform for quickly searching millions of clinical documents in EHRs, to efficiently identify cohorts of patients of interest and specific phenotypes for each patient in a cohort.

What our Clients say...

Ciitizen's goal is to empower seven billion citizens across the planet with all of their health data. LANN gives us the ability to rapidly scale our annotation efforts across our clinical NLP models by providing a comprehensive and easy-to-use interface both for the data scientists and subject matter experts. Ciitizen was impressed by Melax Tech’s hands-on team of bioinformaticians and the support we received to achieve our goals. We value Melax Tech's cooperation in building the next generation of toolsets.

- Tom Corcoran, Data Scientist, Machine Learning Engineer

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