Mayo Clinic NLP System for Patient Smoking Status Identification

The UK General Practice Research Database (GPRD) is a valuable source of information for health services research. It contains coded data supplemented by free text (physicians' notes and letters). However, due to the difficulty of extracting useful information and the cost of anonymisation, this text is seldom utilised in epidemiological research. We annotated the records of 344 women in the year prior to a diagnosis of ovarian cancer and developed a method for automatically detecting mentions of symptoms in text. We estimated the incidence of five commonly presenting symptoms using: (1) coded symptoms, (2) codes augmented by symptoms automatically extracted from text, and (3) a 'gold standard' dataset of codes and text tagged by three clinically trained annotators. The estimates of incidence of each symptom increased by at least 40% when coded information was enhanced using the manually tagged free text. Our automatic method extracted a significant proportion of this extra information. Our straightforward approach should be extremely useful for medical researchers who wish to validate studies based on codes, or to accurately assess symptoms, using information that can be automatically extracted from unanonymised free text.

show abstract

“…of patients [25]. Recently, more general medical text processing tools have been developed and made available [4,24].…”

Section: Related Workmentioning

confidence: 99%

Automatically estimating the incidence of symptoms recorded in GP free text notes

Koeling

Tate

Carroll

2011

Proceedings of the First International Workshop on Managing Interoperability and Complexity in Health Systems

View full text Add to dashboard Cite

show abstract

“…Indicators of socio-economic status, such as measures of occupational prestige, unemployment, education, and homelessness (Hollister et al 2016), along with country-of-origin (Farber-Eger et al 2017) have been extracted from clinical freetext. Smoking status and alcohol use have also been extracted using natural language processing (Chen and Garcia-Webb 2014;Savova et al). While text extraction has limitations, the use of structured elements (billing codes, etc.)…”

Section: Information On the Individual Levelmentioning

confidence: 99%

Integrating Community-Level Data Resources for Precision Medicine Research

et al. 2017

View full text Add to dashboard Cite

Precision Medicine focuses on collecting and using individual-level data to improve healthcare outcomes. To date, research efforts have been motivated by molecular-scale measurements, such as incorporating genomic data into clinical use. In many cases however, environmental, social, and economic factors are much more predictive of health outcomes, yet are not systematically used in clinical practice due to the difficulties in measurement and quantification. Advances in both the availability of electronic health information, environmental exposure data, and the more systematic use of geo-coding now provide ways to systematically assess community-level indicators of health, and link these factors to electronic health records for evaluating their influence on disease outcomes. In this workshop, we discuss new electronic sources of community-level data, and provide insight into their utility and validity when compared with gold-standard data collection approaches.

show abstract

“…cTAKES was first applied to phenotype extraction studies [43] and then was extended to identify document-level patient smoking status [45] and patient level summarization in the first i2b2 challenge [46]. The system was used to generate features for a state-of-the-art system in the 2010 i2b2 challenge on relation extraction of medical problems, tests, and treatments [47].…”

Section: Pipelinementioning

confidence: 99%

Natural Language Processing in Biomedicine: A Unified System Architecture Overview

Doan

Conway

Phương³

et al. 2014

Methods in Molecular Biology

View full text Add to dashboard Cite

In modern electronic medical records (EMR) much of the clinically important data -signs and symptoms, symptom severity, disease status, etc. -are not provided in structured data fields, but rather are encoded in clinician generated narrative text. Natural language processing (NLP) provides a means of "unlocking" this important data source for applications in clinical decision support, quality assurance, and public health. This chapter provides an overview of representative NLP systems in biomedicine based on a unified architectural view. A general architecture in an NLP system consists of two main components: background knowledge that includes biomedical knowledge resources and a framework that integrates NLP tools to process text. Systems differ in both components, which we will review briefly. Additionally, challenges facing current research efforts in biomedical NLP include the paucity of large, publicly available annotated corpora, although initiatives that facilitate data sharing, system evaluation, and collaborative work between researchers in clinical NLP are starting to emerge. IntroductionIn modern electronic medical records (EMR) most of the clinically important data -signs and symptoms, symptom severity, disease status, etc. -is not provided in structured data fields, but are rather encoded in clinician-generated narrative text. Natural language processing (NLP) provides a means of "unlocking" this important data source, converting unstructured text to structured, actionable data for use in applications for clinical decision support, quality assurance, and public health surveillance. There are currently many NLP systems that have been 2 successfully applied to biomedical text. It is not our goal to review all of them in this chapter, but rather to provide an overview of how the field evolved from producing monolithic software built on platforms that were available at the time they were developed to contemporary component-based systems built on top of general frameworks. More importantly, the performance of these systems is tightly associated with their "ingredients" (i.e., modules that are used to form its background knowledge), and how these modules are combined on top of the general framework. We highlight certain systems based on their landmark status as well as on the diversity of components and frameworks they are based on. [7]. The review in this chapter differs from previous work in that it emphasizes the historical development of landmark clinical NLP systems, and presents each system in light of a unified system architecture.We consider that each NLP system in biomedicine contains two main components: biomedical background knowledge and a framework that integrates NLP tools. In the rest of this paper, we will first outline our model architecture for NLP systems in biomedicine, before going on to review and summarize representative NLP systems, starting with an early NLP system, LSP-MLP, and closing our discussion with the presentation of a more recent system, cTAKES. Finally, we will discuss...

show abstract

Mayo Clinic NLP System for Patient Smoking Status Identification

Cited by 95 publications

References 5 publications

Automatically estimating the incidence of symptoms recorded in GP free text notes

Automatically estimating the incidence of symptoms recorded in GP free text notes

Integrating Community-Level Data Resources for Precision Medicine Research

Natural Language Processing in Biomedicine: A Unified System Architecture Overview

Contact Info

Product

Resources

About