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Denis Newman-Griffis, PhD

Lecturer in Data Science

Information School

University of Sheffield

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I'm a researcher interested in language and information, and how we can use computers to study both of them.

As a Lecturer (Assistant Professor) at the University of Sheffield, I study the practical effectiveness and responsible design of AI technologies for medicine and health.

At the University of Pittsburgh, I worked on defining what's involved in building practical natural language processing (NLP) methods for health and disability information.

At the Ohio State University, I studied representation learning technologies for modeling the semantics of new and emerging information domains.

At the National Institutes of Health, I led the development of NLP technologies for function and disability information to help support the US Social Security Administration's disability benefits process.

Statement of Positionality: I am a White, queer, non-binary, non-disabled person from a middle class background.
 

Pronouns: they/them

 
 

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Research Interests

I believe the most exciting science is interdisciplinary. My work draws on artificial intelligence, medical informatics, linguistics, clinical medicine, and more to build systems that help real people ask diverse questions about data.

Function and Disability Informatics

Disability in one form or another is a near-universal experience, but it hasn't been a traditional focus area for medical informatics. I study the language we use to talk about functioning and disability and build NLP systems to turn information about function or the impact of disability into actionable data.

Translational NLP

NLP technologies are designed to connect people to information. All too often, the gap between a groundbreaking innovation in basic NLP methods and successfully using that innovation in practice means that new advances get lost in the shuffle and important research opportunities are lost. I study the processes of translating between innovation and application and finding the rich research questions in messy, real-world problems.

Corpus Linguistics meets Representation Learning

Computational tools can help us analyze superhuman amounts of text and capture linguistic patterns in oceans of data. I'm interested in turning NLP technologies, particularly for representation learning, into a lens to ask: what can I learn about this text and the people who wrote it?

 

Recent Publications

Information Extraction Framework for Disability Determination Using a Mental Functioning Use-Case

Information Extraction Framework for Disability Determination Using a Mental Functioning Use-Case

Ayah Zirikly, Bart Desmet, Denis Newman-Griffis, Elizabeth E Marfeo, Christine McDonough, Howard Goldman, Leighton Chan

JMIR Medical Informatics (2022) 10(3): e32245

Challenges and opportunities for analyzing mental functioning with NLP in disability policy contexts.

See on GitHub
Project Website
Half the picture: Word frequencies reveal racial differences in clinical documentation, but not their causes

Half the picture: Word frequencies reveal racial differences in clinical documentation, but not their causes

Jacqueline Penn, Denis Newman-Griffis

Proceedings of the 2022 AMIA Informatics Summit

Medical documentation differs in style and content for White vs Black patients.

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Project Website
Digital Scarlet Letters: Sexually Transmitted Infections in the Electronic Medical Record

Digital Scarlet Letters: Sexually Transmitted Infections in the Electronic Medical Record

Sarah Bennett, Denis Newman-Griffis, Mary Catherine Beach, Marielle Gross

Sexually Transmitted Diseases (2022) 49(6):70-74

Ethical analysis of the documentation of STI history in pregnancy in the electronic medical record.

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Project Website
Linking Free Text Documentation of Functioning and Disability to the ICF with Natural Language Processing

Linking Free Text Documentation of Functioning and Disability to the ICF with Natural Language Processing

Denis Newman-Griffis, Jonathan Camacho Maldonado, Pei-Shu Ho, Maryanne Sacco, Rafael Jimenez Silva, Julia Porcino, Leighton Chan

Frontiers in Rehabilitation Sciences (2021) 2:742702

New systems for linking information about Activities of Daily Living in clinical notes to the ICF.

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Project Website
Improving broad-coverage medical entity linking with semantic type prediction and large-scale datasets

Improving broad-coverage medical entity linking with semantic type prediction and large-scale datasets

Shikhar Vashishth, Denis Newman-Griffis, Rishabh Joshi, Ritam Dutt, Carolyn P Rosé

Journal of Biomedical Informatics (2021) 121:103880

New, modular approach for semantic type filtering to improve any biomedical information extraction pipeline.

Project Website