scholarly journals Privacy protected text analysis in DataSHIELD

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Rebecca Wilson ◽  
Oliver Butters ◽  
Demetris Avraam ◽  
Andrew Turner ◽  
Paul Burton
Keyword(s):  
Text Analysis ◽  
Individual Patient Data ◽  
Patient Data ◽  
British Library ◽  
Free Text ◽  
Sensitive Information ◽  
Proof Of Concept ◽  
Text Data ◽  
Health Records ◽  
Different Sources

ABSTRACT ObjectivesDataSHIELD (www.datashield.ac.uk) was born of the requirement in the biomedical and social sciences to co-analyse individual patient data (microdata) from different sources, without disclosing identity or sensitive information. Under DataSHIELD, raw data never leaves the data provider and no microdata or disclosive information can be seen by the researcher. The analysis is taken to the data - not the data to the analysis. Text data can be very disclosive in the biomedical domain (patient records, GP letters etc). Similar, but different, issues are present in other domains - text could be copyrighted, or have a large IP value, making sharing impractical. ApproachBy treating text in an analogous way to individual patient data we assessed if DataSHIELD could be adapted and implemented for text analysis, and circumvent the key obstacles that currently prevent it. ResultsUsing open digitised text data held by the British Library, a DataSHIELD proof-of-concept infrastructure and prototype DataSHIELD functions for free text analysis were developed. ConclusionsWhilst it is possible to analyse free text within a DataSHIELD infrastructure, the challenge is creating generalised and resilient anti-disclosure methods for free text analysis. There are a range of biomedical and health sciences applications for DataSHIELD methods of privacy protected analysis of free text including analysis of electronic health records and analysis of qualitative data e.g. from social media.

scholarly journals Incorporating natural language processing to improve classification of axial spondyloarthritis using electronic health records

Rheumatology ◽  
2019 ◽  
Vol 59 (5) ◽  
pp. 1059-1065 ◽  
Author(s):  
Sizheng Steven Zhao ◽  
Chuan Hong ◽  
Tianrun Cai ◽  
Chang Xu ◽  
Jie Huang ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Predictive Value ◽  
Area Under The Curve ◽  
Free Text ◽  
Text Data ◽  
Health Records ◽  
Disease Concepts ◽  
Icd Codes ◽  
Electronic Health

Abstract Objectives To develop classification algorithms that accurately identify axial SpA (axSpA) patients in electronic health records, and compare the performance of algorithms incorporating free-text data against approaches using only International Classification of Diseases (ICD) codes. Methods An enriched cohort of 7853 eligible patients was created from electronic health records of two large hospitals using automated searches (⩾1 ICD codes combined with simple text searches). Key disease concepts from free-text data were extracted using NLP and combined with ICD codes to develop algorithms. We created both supervised regression-based algorithms—on a training set of 127 axSpA cases and 423 non-cases—and unsupervised algorithms to identify patients with high probability of having axSpA from the enriched cohort. Their performance was compared against classifications using ICD codes only. Results NLP extracted four disease concepts of high predictive value: ankylosing spondylitis, sacroiliitis, HLA-B27 and spondylitis. The unsupervised algorithm, incorporating both the NLP concept and ICD code for AS, identified the greatest number of patients. By setting the probability threshold to attain 80% positive predictive value, it identified 1509 axSpA patients (mean age 53 years, 71% male). Sensitivity was 0.78, specificity 0.94 and area under the curve 0.93. The two supervised algorithms performed similarly but identified fewer patients. All three outperformed traditional approaches using ICD codes alone (area under the curve 0.80–0.87). Conclusion Algorithms incorporating free-text data can accurately identify axSpA patients in electronic health records. Large cohorts identified using these novel methods offer exciting opportunities for future clinical research.

scholarly journals The FDA MyStudies app: a reusable platform for distributed clinical trials and real-world evidence studies

JAMIA Open ◽  
2020 ◽  
Author(s):  
Zachary Wyner ◽  
Sascha Dublin ◽  
Christina Chambers ◽  
Shyam Deval ◽  
Chayim Herzig-Marx ◽  
...  
Keyword(s):  
Data Storage ◽  
Health Research ◽  
Mobile Application ◽  
Public Health Research ◽  
Patient Data ◽  
Proof Of Concept ◽  
Health Records ◽  
Secure Storage ◽  
Real World Evidence ◽  
Role Based

Abstract We developed a mobile application and secure patient data storage platform, FDA MyStudies, to address privacy, engagement, and extensibility challenges in mobile clinical research. The system extends the capabilities of the mobile frameworks Apple ResearchKit and ResearchStack through an intuitive front-end application and secure storage environment that can support health research studies. The platform supports single or multisite studies via role-based access and can be implemented within highly secure data environments. As a proof-of-concept, pregnant women participated in a descriptive study via the app in which data not routinely captured in electronic health records (EHR) were collected and linked with existing patient data to provide a more wholistic view of the patient and illustrate how patient data combined with EHR data could be used to support public health research.

DARR: A Free-text Analysis System for the Automatic Documentation of Radiological Reports

1977 ◽  
Vol 16 (03) ◽  
pp. 144-153 ◽  
Author(s):  
E. Vaccari ◽  
W. Delaney ◽  
A. Chiesa
Keyword(s):  
Natural Language ◽  
Text Analysis ◽  
Automatic Documentation ◽  
Free Text ◽  
Software System ◽  
Automatic Translation ◽  
Radiological Report ◽  
Analysis System ◽  
Content Processing

A software system for the automatic free-text analysis and retrieval of radiological reports is presented. Such software involves: (1) automatic translation of the specific natural language in a formalized metalanguage in order to transform the radiological report in a »normalized report« analyzable by computer; (2) content processing of the normalized report to select desired information. The approach used to accomplish point (1) is described in detail referring to a specific application.

A Medical Text Analysis System for German - Syntax Analysis

1991 ◽  
Vol 30 (04) ◽  
pp. 275-283 ◽  
Author(s):  
P. M. Pietrzyk
Keyword(s):  
Text Analysis ◽  
Free Text ◽  
New Approach ◽  
Syntax Analysis ◽  
Medical Text ◽  
Medical Language ◽  
Computerized Processing ◽  
Language Data ◽  
Analysis System ◽  
German Syntax

Abstract:Much information about patients is stored in free text. Hence, the computerized processing of medical language data has been a well-known goal of medical informatics resulting in different paradigms. In Gottingen, a Medical Text Analysis System for German (abbr. MediTAS) has been under development for some time, trying to combine and to extend these paradigms. This article concentrates on the automated syntax analysis of German medical utterances. The investigated text material consists of 8,790 distinct utterances extracted from the summary sections of about 18,400 cytopathological findings reports. The parsing is based upon a new approach called Left-Associative Grammar (LAG) developed by Hausser. By extending considerably the LAG approach, most of the grammatical constructions occurring in the text material could be covered.

A Numerically Coded File of Operative Procedures Derived from a Free Text Data Collection System : A Measure of the Accuracy

1976 ◽  
Vol 15 (01) ◽  
pp. 21-28 ◽  
Author(s):  
Carmen A. Scudiero ◽  
Ruth L. Wong
Keyword(s):  
Data Collection ◽  
Pap Smear ◽  
Operative Procedures ◽  
Free Text ◽  
Collection System ◽  
Process Data ◽  
Text Data ◽  
Data Collection System ◽  
History Of ◽  
Correlation System

A free text data collection system has been developed at the University of Illinois utilizing single word, syntax free dictionary lookup to process data for retrieval. The source document for the system is the Surgical Pathology Request and Report form. To date 12,653 documents have been entered into the system.The free text data was used to create an IRS (Information Retrieval System) database. A program to interrogate this database has been developed to numerically coded operative procedures. A total of 16,519 procedures records were generated. One and nine tenths percent of the procedures could not be fitted into any procedures category; 6.1% could not be specifically coded, while 92% were coded into specific categories. A system of PL/1 programs has been developed to facilitate manual editing of these records, which can be performed in a reasonable length of time (1 week). This manual check reveals that these 92% were coded with precision = 0.931 and recall = 0.924. Correction of the readily correctable errors could improve these figures to precision = 0.977 and recall = 0.987. Syntax errors were relatively unimportant in the overall coding process, but did introduce significant error in some categories, such as when right-left-bilateral distinction was attempted.The coded file that has been constructed will be used as an input file to a gynecological disease/PAP smear correlation system. The outputs of this system will include retrospective information on the natural history of selected diseases and a patient log providing information to the clinician on patient follow-up.Thus a free text data collection system can be utilized to produce numerically coded files of reasonable accuracy. Further, these files can be used as a source of useful information both for the clinician and for the medical researcher.

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