Skip to main content Link Menu Expand (external link) Document Search Copy Copied

Microservice Text Analysis Platform

MTAP is a framework for developing text analysis pipelines. It utilizes the gRPC Framework for communication between independently deployed, scalable, cross-language components.

We develop and maintain a text analysis system for clinical text called BioMedICUS. As requirements changed, we wanted to support Python components interoperability with our existing components developed in Java. In order to process millions of notes, ability to deploy at scale is a requirement. Furthermore, MTAP facilitates users with minimal development experience, making creating components easier and allowing for deployment to a production environment. These requirements led us to develop our own framework for text analysis based on the microservice architecture pattern.

MTAP Benefits

Ease of Use

MTAP takes care of all the communication between different components, and provides a distributed object model for text analysis artifacts. All you have to worry about is writing the text analysis code.


By using the microservice pattern, text analysis components can be deployed once and then mixed and matched in different pipelines. Components written in different languages can interoperate without hassle. We also provide a RESTful API gateway that lets you call components using HTTP.


MTAP is designed to bridge the gap between prototyping new ideas and deploying them into a production environment. It supports multiple methods of running and debugging components, from locally without using any network infastructure to deploying services that listen on network interfaces.

What is BioMedICUS?

The BioMedical Information Collection and Understanding System (BioMedICUS) is a system for large-scale text analysis and processing of biomedical and clinical reports. It provides general NLP processing such as sentence detection, part of speech tagging, and acronym detection tailored for clinical text, as clinical-specific processing such as the detection of UMLS (Unified Medical Language System) concepts in text. We use BioMedICUS at the University of Minnesota to process millions of clinical notes for use by researchers, for example via our information extraction system PIER (Patient Information Extraction for Research).

How does BioMedICUS use MTAP?

BioMedICUS uses MTAP as a foundational framework and for running BioMedICUS processors. When we develop a component for BioMedICUS, we develop it using MTAP classes which are responsible for the lower level management of moving data around, horizontal scaling of components during deployment, and providing a data model for storing artifacts of clinical text processing. BioMedICUS handles the text processing algorithms, MTAP handles running the processes and moving data between those algorithms.

What are microservices and how does that fit into MTAP?

Microservices are an architecture pattern in which services are broken into de-coupled, independently deployable components that communicate on a network using standardized protocols. In the case of text processing, there are many components that rely on the work of upstream components in the form of a processing pipeline. In MTAP, processing components in a pipeline are each deployed individually as “microservices”. This enables a lot of flexibility in how the components are developed and deployed, they can be programmed in different languages, they can be on different machines, they can be scaled at different levels depending on need.

NLP/IE Background

MTAP is developed at the University of Minnesota by the NLP/IE Group in the Institute for Health Informatics.