scholarly journals Toward the Development of Data Governance Standards for Using Clinical Free-Text Data in Health Research: Position Paper

10.2196/16760 ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. e16760
Author(s):  
Kerina H Jones ◽  
Elizabeth M Ford ◽  
Nathan Lea ◽  
Lucy J Griffiths ◽  
Lamiece Hassan ◽  
...  
Keyword(s):  
Personal Data ◽  
Position Paper ◽  
Free Text ◽  
Data Governance ◽  
Text Data ◽  
Public Benefit ◽  
The United Kingdom ◽  
Protection Legislation ◽  
Text Information ◽  
Rich Information

Background Clinical free-text data (eg, outpatient letters or nursing notes) represent a vast, untapped source of rich information that, if more accessible for research, would clarify and supplement information coded in structured data fields. Data usually need to be deidentified or anonymized before they can be reused for research, but there is a lack of established guidelines to govern effective deidentification and use of free-text information and avoid damaging data utility as a by-product. Objective This study aimed to develop recommendations for the creation of data governance standards to integrate with existing frameworks for personal data use, to enable free-text data to be used safely for research for patient and public benefit. Methods We outlined data protection legislation and regulations relating to the United Kingdom for context and conducted a rapid literature review and UK-based case studies to explore data governance models used in working with free-text data. We also engaged with stakeholders, including text-mining researchers and the general public, to explore perceived barriers and solutions in working with clinical free-text. Results We proposed a set of recommendations, including the need for authoritative guidance on data governance for the reuse of free-text data, to ensure public transparency in data flows and uses, to treat deidentified free-text data as potentially identifiable with use limited to accredited data safe havens, and to commit to a culture of continuous improvement to understand the relationships between the efficacy of deidentification and reidentification risks, so this can be communicated to all stakeholders. Conclusions By drawing together the findings of a combination of activities, we present a position paper to contribute to the development of data governance standards for the reuse of clinical free-text data for secondary purposes. While working in accordance with existing data governance frameworks, there is a need for further work to take forward the recommendations we have proposed, with commitment and investment, to assure and expand the safe reuse of clinical free-text data for public benefit.

scholarly journals Toward the Development of Data Governance Standards for Using Clinical Free-Text Data in Health Research: Position Paper (Preprint)

2019 ◽  
Author(s):  
Kerina H Jones ◽  
Elizabeth M Ford ◽  
Nathan Lea ◽  
Lucy J Griffiths ◽  
Lamiece Hassan ◽  
...  
Keyword(s):  
Personal Data ◽  
Position Paper ◽  
Free Text ◽  
Data Governance ◽  
Text Data ◽  
Public Benefit ◽  
The United Kingdom ◽  
Protection Legislation ◽  
Text Information ◽  
Rich Information

BACKGROUND Clinical free-text data (eg, outpatient letters or nursing notes) represent a vast, untapped source of rich information that, if more accessible for research, would clarify and supplement information coded in structured data fields. Data usually need to be deidentified or anonymized before they can be reused for research, but there is a lack of established guidelines to govern effective deidentification and use of free-text information and avoid damaging data utility as a by-product. OBJECTIVE This study aimed to develop recommendations for the creation of data governance standards to integrate with existing frameworks for personal data use, to enable free-text data to be used safely for research for patient and public benefit. METHODS We outlined data protection legislation and regulations relating to the United Kingdom for context and conducted a rapid literature review and UK-based case studies to explore data governance models used in working with free-text data. We also engaged with stakeholders, including text-mining researchers and the general public, to explore perceived barriers and solutions in working with clinical free-text. RESULTS We proposed a set of recommendations, including the need for authoritative guidance on data governance for the reuse of free-text data, to ensure public transparency in data flows and uses, to treat deidentified free-text data as potentially identifiable with use limited to accredited data safe havens, and to commit to a culture of continuous improvement to understand the relationships between the efficacy of deidentification and reidentification risks, so this can be communicated to all stakeholders. CONCLUSIONS By drawing together the findings of a combination of activities, we present a position paper to contribute to the development of data governance standards for the reuse of clinical free-text data for secondary purposes. While working in accordance with existing data governance frameworks, there is a need for further work to take forward the recommendations we have proposed, with commitment and investment, to assure and expand the safe reuse of clinical free-text data for public benefit.

scholarly journals Developing data governance standards for using free-text data in research (TexGov)

2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Kerina Jones ◽  
Elizabeth Ford ◽  
Nathan Lea ◽  
Lucy Griffiths ◽  
Sharon Heys ◽  
...  
Keyword(s):  
Language Processing ◽  
Data Extraction ◽  
Personal Data ◽  
Free Text ◽  
Data Governance ◽  
Text Data ◽  
Additional Detail ◽  
Damage Data ◽  
Protection Legislation ◽  
Minimal Data

BackgroundFree-text data represent a vast, untapped source of rich information to guide research and public service delivery. Free-text data contain a wealth of additional detail that, if more accessible, would clarify and supplement information coded in structured data fields. Personal data usually need to be de-identified or anonymised before they can be used for purposes such as audit and research, but there are major challenges in finding effective methods to de-identify free-text that do not damage data utility as a by-product. The main aim of the TexGov project is to work towards data governance standards to enable free-text data to be used safely for public benefit. MethodsWe conducted: a rapid literature review to explore the data governance models used in working with free-text data, plus case studies of systems making de-identified free-text data available for research; we engaged with text mining researchers and the general public to explore barriers and solutions in working with free-text; and we outlined (UK) data protection legislation and regulations for context. ResultsWe reviewed 50 articles and the models of 4 systems providing access to de-identified free-text. The main emerging themes were: i) patient involvement at identifiable and de-identified data stages; ii) questions of consent and notification for the reuse of free-text data; iii) working with identifiable data for Natural Language Processing algorithm development; and iv) de-identification methods and thresholds of reliability. ConclusionWe have proposed a set of recommendations, including: ensuring public transparency in data flows and uses; adhering to the principles of minimal data extraction; treating de-identified blacklisted free-text as potentially identifiable with use limited to accredited data safe-havens; and, the need to commit to a culture of continuous improvement to understand the relationships between accuracy of de-identification and re-identification risk, so this can be communicated to all stakeholders.

scholarly journals Should free-text data in electronic medical records be shared for research? A citizens’ jury study in the UK

2020 ◽  
Vol 46 (6) ◽  
pp. 367-377 ◽  
Author(s):  
Elizabeth Ford ◽  
Malcolm Oswald ◽  
Lamiece Hassan ◽  
Kyle Bozentko ◽  
Goran Nenadic ◽  
...  
Keyword(s):  
Clinical Information ◽  
Service Planning ◽  
Structured Data ◽  
Free Text ◽  
Patient Privacy ◽  
Text Data ◽  
The Public ◽  
Public Benefit ◽  
The Uk ◽  
Privacy Risks

BackgroundUse of routinely collected patient data for research and service planning is an explicit policy of the UK National Health Service and UK government. Much clinical information is recorded in free-text letters, reports and notes. These text data are generally lost to research, due to the increased privacy risk compared with structured data. We conducted a citizens’ jury which asked members of the public whether their medical free-text data should be shared for research for public benefit, to inform an ethical policy.MethodsEighteen citizens took part over 3 days. Jurors heard a range of expert presentations as well as arguments for and against sharing free text, and then questioned presenters and deliberated together. They answered a questionnaire on whether and how free text should be shared for research, gave reasons for and against sharing and suggestions for alleviating their concerns.ResultsJurors were in favour of sharing medical data and agreed this would benefit health research, but were more cautious about sharing free-text than structured data. They preferred processing of free text where a computer extracted information at scale. Their concerns were lack of transparency in uses of data, and privacy risks. They suggested keeping patients informed about uses of their data, and giving clear pathways to opt out of data sharing.ConclusionsInformed citizens suggested a transparent culture of research for the public benefit, and continuous improvement of technology to protect patient privacy, to mitigate their concerns regarding privacy risks of using patient text data.

Employing AI with NLP to Combine EHR’s Structured and Free Text Data to Identify NVAF to Decrease Strokes and Death (Preprint)

2021 ◽  
Author(s):  
Peter Elkin ◽  
Sarah Mullin ◽  
Jack Mardekian ◽  
Chris Crowner ◽  
Sylvester Sakilay ◽  
...  
Keyword(s):  
Language Processing ◽  
Oral Anticoagulants ◽  
Unstructured Data ◽  
Free Text ◽  
Text Data ◽  
Clinical Notes ◽  
The Us ◽  
Text Information ◽  
Icd Codes ◽  
The University

BACKGROUND Non-Valvular Atrial Fibrillation (NVAF), affects almost 6 million Americans and is a major contributor to strokes; but is significantly undiagnosed and undertreated despite explicit guidelines for oral anticoagulation. OBJECTIVE We investigate if use of semi-supervised natural language processing (NLP) of electronic health record’s (EHRs’) free-text information combined with structured EHR data improves NVAF discovery and treatment--perhaps offering a method to prevent thousands of deaths and save billions of dollars. METHODS We abstracted a set of 96,681 participants from the University at Buffalo’s faculty practice’s EHR. NLP was used to index the notes and compare the ability to identify NVAF, CHA2DS2 VASc and HAS-BLED scores using unstructured data (ICD codes) vs. Structured plus Unstructured data from clinical notes. Additionally, we analyzed data from 63,296,120 participants in the Optum and Truven databases to determine the NVAF’s frequency, rates of CHA2DS2 VASc ≥ 2 and no contraindications to oral anticoagulants (OAC), rates of stroke and death in the untreated population, and first year’s costs after stroke. 16,17 RESULTS The structured-plus-unstructured method would have identified 3,976,056 additional true NVAF cases (p<0.001) and improved sensitivity for CHA2DS2-VASc and HAS-BLED scores compared to the structured data alone (P=0.00195, and P<0.001 respectively), a 32.1% improvement. For the US this method would prevent an estimated 176,537 strokes, save 10,575 lives, and save over $13.5 billion. CONCLUSIONS AI-informed bio-surveillance combining NLP of free-text information with structured EHR data improves data completeness, could prevent thousands of strokes, and save lives and funds. This method is applicable to many disorders, with profound public health consequences. CLINICALTRIAL None

scholarly journals Issues and Considerations for Effective Text Data Retrieval

2019 ◽  
Vol 9 (1) ◽  
pp. 1442-1445
Keyword(s):  
Data Mining ◽  
Data Retrieval ◽  
Text Retrieval ◽  
Experimental Results ◽  
Free Text ◽  
Data Sets ◽  
Retrieval Process ◽  
Text Data ◽  
Text Information ◽  
Major Domain

Text data retrieval is one of the major domain for extracting knowledge from the stored data sets. Within the text information, the text meaningful numerical codes extracted unstructured process information is to make the free text associated with the unstructured nature of data mining in a different stream. Number of procedure is constructed to Performing this operations most effectively. This paper focuses one of the text retrieval process, experimental results verified proposed methods works well with most of the documents.

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.

Data protection and passenger name record in judicial criminal matters under the EU–UK Trade and Cooperation Agreement

2021 ◽  
pp. 203228442199492
Author(s):  
Catherine Van de Heyning
Keyword(s):  
United Kingdom ◽  
Data Protection ◽  
Personal Data ◽  
Cooperation Agreement ◽  
Judicial Cooperation ◽  
The United Kingdom ◽  
Record Data ◽  
Criminal Matters ◽  
The Impact ◽  
The Eu

The submission discusses the provisions in the EU–UK Trade and Cooperation Agreement on data protection as well as the consequences for the exchange of passenger name record data in the field of criminal and judicial cooperation. The author concludes that the impact of the Agreement will depend on the resolvement of the United Kingdom to uphold the standards of protection of personal data equivalent to the EU’s in order to reach an adequacy decision.

scholarly journals Predicting adult neuroscience intensive care unit admission from emergency department triage using a retrospective, tabular-free text machine learning approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eyal Klang ◽  
Benjamin R. Kummer ◽  
Neha S. Dangayach ◽  
Amy Zhong ◽  
M. Arash Kia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Learning Model ◽  
Free Text ◽  
Combined Model ◽  
Text Data ◽  
Machine Learning Model ◽  
Record Data

AbstractEarly admission to the neurosciences intensive care unit (NSICU) is associated with improved patient outcomes. Natural language processing offers new possibilities for mining free text in electronic health record data. We sought to develop a machine learning model using both tabular and free text data to identify patients requiring NSICU admission shortly after arrival to the emergency department (ED). We conducted a single-center, retrospective cohort study of adult patients at the Mount Sinai Hospital, an academic medical center in New York City. All patients presenting to our institutional ED between January 2014 and December 2018 were included. Structured (tabular) demographic, clinical, bed movement record data, and free text data from triage notes were extracted from our institutional data warehouse. A machine learning model was trained to predict likelihood of NSICU admission at 30 min from arrival to the ED. We identified 412,858 patients presenting to the ED over the study period, of whom 1900 (0.5%) were admitted to the NSICU. The daily median number of ED presentations was 231 (IQR 200–256) and the median time from ED presentation to the decision for NSICU admission was 169 min (IQR 80–324). A model trained only with text data had an area under the receiver-operating curve (AUC) of 0.90 (95% confidence interval (CI) 0.87–0.91). A structured data-only model had an AUC of 0.92 (95% CI 0.91–0.94). A combined model trained on structured and text data had an AUC of 0.93 (95% CI 0.92–0.95). At a false positive rate of 1:100 (99% specificity), the combined model was 58% sensitive for identifying NSICU admission. A machine learning model using structured and free text data can predict NSICU admission soon after ED arrival. This may potentially improve ED and NSICU resource allocation. Further studies should validate our findings.

Method for hiding text data in an image

2021 ◽  
Vol 83 (1) ◽  
pp. 72-79
Author(s):  
O.A. Kan ◽  
◽  
N.A. Mazhenov ◽  
K.B. Kopbalina ◽  
G.B. Turebaeva ◽  
...  
Keyword(s):  
Secret Message ◽  
Source Image ◽  
Hidden Information ◽  
Text Data ◽  
Code Table ◽  
Ascii Code ◽  
Text Information ◽  
Graphic File ◽  
Image Pixels ◽  
Digital Fingerprints

The main problem: The article deals with the issues of hiding text information in a graphic file. A formula for hiding text information in image pixels is proposed. A steganography scheme for embedding secret text in random image pixels has been developed. Random bytes are pre-embedded in each row of pixels in the source image. As a result of the operations performed, a key image is obtained. The text codes are embedded in random bytes of pixels of a given RGB channel. To form a secret message, the characters of the ASCII code table are used. Demo encryption and decryption programs have been developed in the Python 3.5.2 programming language. A graphic file is used as the decryption key. Purpose: To develop an algorithm for embedding text information in random pixels of an image. Methods: Among the methods of hiding information in graphic images, the LSB method of hiding information is widely used, in which the lower bits in the image bytes responsible for color encoding are replaced by the bits of the secret message. Analysis of methods of hiding information in graphic files and modeling of algorithms showed an increase in the level of protection of hidden information from detection. Results and their significance: Using the proposed steganography scheme and the algorithm for embedding bytes of a secret message in a graphic file, protection against detection of hidden information is significantly increased. The advantage of this steganography scheme is that for decryption, a key image is used, in which random bytes are pre-embedded. In addition, the entire pixel bits of the container image are used to display the color shades. It can also be noted that the developed steganography scheme allows not only to transmit secret information, but also to add digital fingerprints or hidden tags to the image.

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