Elizabeth Ford

Orcid: 0000-0001-5613-8509

According to our database1, Elizabeth Ford authored at least 11 papers between 2016 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2023
Challenges Encountered and Lessons Learned when Using a Novel Anonymised Linked Dataset of Health and Social Care Records for Public Health Intelligence: The Sussex Integrated Dataset.
Inf., February, 2023

Ethical issues when using digital biomarkers and artificial intelligence for the early detection of dementia.
WIREs Data. Mining. Knowl. Discov., 2023

2022
Understanding Public Priorities and Perceptions of the Use of Linked Healthcare Data in South East England.
Proceedings of the Challenges of Trustable AI and Added-Value on Health, 2022

2021
Barriers and facilitators to the adoption of electronic clinical decision support systems: a qualitative interview study with UK general practitioners.
BMC Medical Informatics Decis. Mak., 2021

The Potential of Research Drawing on Clinical Free Text to Bring Benefits to Patients in the United Kingdom: A Systematic Review of the Literature.
Frontiers Digit. Health, 2021

2020
Toward an Ethical Framework for the Text Mining of Social Media for Health Research: A Systematic Review.
Frontiers Digit. Health, 2020

2019
Identifying undetected dementia in UK primary care patients: a retrospective case-control study comparing machine-learning and standard epidemiological approaches.
BMC Medical Informatics Decis. Mak., 2019

Towards machine-learning informed early detection of dementia in UK primary care.
Proceedings of the AMIA 2019, 2019

Using Social Media to Study Mental Health Conditions - Challenges and Opportunities.
Proceedings of the AMIA 2019, 2019

Conceptualisation and Annotation of Drug Nonadherence Information for Knowledge Extraction from Patient-Generated Texts.
Proceedings of the 5th Workshop on Noisy User-generated Text, 2019

2016
Extracting information from the text of electronic medical records to improve case detection: a systematic review.
J. Am. Medical Informatics Assoc., 2016


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