An Evolving Ecosystem for Natural Language Processing in Department of Veterans Affairs

2017 ◽  
Vol 41 (2) ◽  
Author(s):  
Jennifer H. Garvin ◽  
Megha Kalsy ◽  
Cynthia Brandt ◽  
Stephen L. Luther ◽  
Guy Divita ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Veterans Affairs ◽  
Department Of Veterans Affairs

scholarly journals Automating Quality Measures for Heart Failure Using Natural Language Processing: A Descriptive Study in the Department of Veterans Affairs (Preprint)

2017 ◽  
Author(s):  
Jennifer Hornung Garvin ◽  
Youngjun Kim ◽  
Glenn Temple Gobbel ◽  
Michael E Matheny ◽  
Andrew Redd ◽  
...  
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Left Ventricular ◽  
Veterans Affairs ◽  
Quality Measure ◽  
Left Ventricular Ejection ◽  
Department Of Veterans Affairs

BACKGROUND We developed an accurate, stakeholder-informed, automated, natural language processing (NLP) system to measure the quality of heart failure (HF) inpatient care, and explored the potential for adoption of this system within an integrated health care system. OBJECTIVE To accurately automate a United States Department of Veterans Affairs (VA) quality measure for inpatients with HF. METHODS We automated the HF quality measure Congestive Heart Failure Inpatient Measure 19 (CHI19) that identifies whether a given patient has left ventricular ejection fraction (LVEF) <40%, and if so, whether an angiotensin-converting enzyme inhibitor or angiotensin-receptor blocker was prescribed at discharge if there were no contraindications. We used documents from 1083 unique inpatients from eight VA medical centers to develop a reference standard (RS) to train (n=314) and test (n=769) the Congestive Heart Failure Information Extraction Framework (CHIEF). We also conducted semi-structured interviews (n=15) for stakeholder feedback on implementation of the CHIEF. RESULTS The CHIEF classified each hospitalization in the test set with a sensitivity (SN) of 98.9% and positive predictive value of 98.7%, compared with an RS and SN of 98.5% for available External Peer Review Program assessments. Of the 1083 patients available for the NLP system, the CHIEF evaluated and classified 100% of cases. Stakeholders identified potential implementation facilitators and clinical uses of the CHIEF. CONCLUSIONS The CHIEF provided complete data for all patients in the cohort and could potentially improve the efficiency, timeliness, and utility of HF quality measurements.

Ascertainment of Veterans With Metastatic Prostate Cancer in Electronic Health Records: Demonstrating the Case for Natural Language Processing

2021 ◽  
pp. 1005-1014
Author(s):  
Patrick R. Alba ◽  
Anthony Gao ◽  
Kyung Min Lee ◽  
Tori Anglin-Foote ◽  
Brian Robison ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Metastatic Prostate Cancer ◽  
Cancer Registries ◽  
Veterans Affairs ◽  
Department Of Veterans Affairs ◽  
Cancer Registry Data ◽  
Number Of Patients

PURPOSE Prostate cancer (PCa) is among the leading causes of cancer deaths. While localized PCa has a 5-year survival rate approaching 100%, this rate drops to 31% for metastatic prostate cancer (mPCa). Thus, timely identification of mPCa is a crucial step toward measuring and improving access to innovations that reduce PCa mortality. Yet, methods to identify patients diagnosed with mPCa remain elusive. Cancer registries provide detailed data at diagnosis but are not updated throughout treatment. This study reports on the development and validation of a natural language processing (NLP) algorithm deployed on oncology, urology, and radiology clinical notes to identify patients with a diagnosis or history of mPCa in the Department of Veterans Affairs. PATIENTS AND METHODS Using a broad set of diagnosis and histology codes, the Veterans Affairs Corporate Data Warehouse was queried to identify all Veterans with PCa. An NLP algorithm was developed to identify patients with any history or progression of mPCa. The NLP algorithm was prototyped and developed iteratively using patient notes, grouped into development, training, and validation subsets. RESULTS A total of 1,144,610 Veterans were diagnosed with PCa between January 2000 and October 2020, among which 76,082 (6.6%) were identified by NLP as having mPCa at some point during their care. The NLP system performed with a specificity of 0.979 and sensitivity of 0.919. CONCLUSION Clinical documentation of mPCa is highly reliable. NLP can be leveraged to improve PCa data. When compared to other methods, NLP identified a significantly greater number of patients. NLP can be used to augment cancer registry data, facilitate research inquiries, and identify patients who may benefit from innovations in mPCa treatment.

scholarly journals Natural Language Processing of Serum Protein Electrophoresis Reports in the Veterans Affairs Health Care System

2020 ◽  
pp. 749-756
Author(s):  
Justine H. Ryu ◽  
Andrew J. Zimolzak
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Serum Protein ◽  
Language Processing ◽  
Monoclonal Gammopathy ◽  
Veterans Affairs ◽  
Protein Electrophoresis ◽  
Serum Protein Electrophoresis ◽  
Laboratory Results

PURPOSE Serum protein electrophoresis (SPEP) is a clinical tool used to screen for monoclonal gammopathy, thus it is a critical tool in the evaluation of patients with multiple myeloma. However, SPEP laboratory results are usually returned as short text reports, which are not amenable to simple computerized processing for large-scale studies. We applied natural language processing (NLP) to detect monoclonal gammopathy in SPEP laboratory results and compared its performance at multiple hospitals using both a rules-based manual system and a machine-learning algorithm. METHODS We used the data from the VA Corporate Data Warehouse, which comprises data from 20 million unique individuals. SPEP reports were collected from July to December 2015 at 5 Veterans Affairs Medical Centers. Of these reports, we annotated the presence or absence of monoclonal gammopathy in 300 reports. We applied a machine learning–based NLP and a manual rules-based NLP to detect monoclonal gammopathy in SPEP reports at each of the hospitals, then applied the model from 1 hospital to each of the other hospitals. RESULTS The learning system achieved an area under the receiver operating characteristic curve of 0.997, and the rules-based system achieved an accuracy of 0.99. When a model trained on 1 hospital’s data was applied to a different hospital, however, accuracy varied greatly, and the learning-based models performed better than the rules-based model. CONCLUSION Binary classification of short clinical texts such as SPEP reports may be a particularly attractive target on which to train highly accurate NLP systems.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

Sign in / Sign up

Export Citation Format

Share Document