Paul Heider

Assistant Professor

Medical University of South Carolina

United States

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Paul Heider is an Assistant Professor at the Medical University of South Carolina, USA. His research areas include child abuse and neglect identification in pediatric care, the application of artificial intelligence in clinical trials and data utilization, and natural language processing for health informatics. Recent publications highlight his work on leveraging AI for clinical study matching, developing evaluation frameworks for NLP tools, and implementing predictive analytics for COVID-19.

Articles (10)

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2025

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2025

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2024

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2023

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2023

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2023

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2022

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2021

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2021

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2020

Collaborators (5)

Aviv Landau

University of Pennsylvania

UNITED STATES

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Youngjun Kim

Ohio State University

UNITED STATES

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Alexander Alekseyenko

Professor

Medical University of South Carolina

UNITED STATES

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Jihad Obeid

Professor

Medical University of South Carolina

UNITED STATES

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Katie Kirchoff

Medical University of South Carolina

UNITED STATES

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Paul Heider

Research Interests

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Articles (10)

Estimating the Prevalence of Child Abuse and Neglect Among Adolescents in Primary Care Through Diagnoses Codes and Free-Text EHR Clinical Notes

Leveraging artificial intelligence for clinical study matching: Key threads for interweaving data science and implementation science. (Preprint)

An Extensible Evaluation Framework Applied to Clinical Text Deidentification Natural Language Processing Tools: Multisystem and Multicorpus Study

Initial development of tools to identify child abuse and neglect in pediatric primary care.

Piloting an automated clinical trial eligibility surveillance and provider alert system based on artificial intelligence and standard data models.

Representing and utilizing clinical textual data for real world studies: An OHDSI approach.

Comparing Multiple Models for Section Header Classification with Feature Evaluation.

Natural language processing enabling COVID-19 predictive analytics to support data-driven patient advising and pooled testing

A Hybrid Model for Family History Information Identification and Relation Extraction: Development and Evaluation of an End-to-End Information Extraction System

An artificial intelligence approach to COVID-19 infection risk assessment in virtual visits: A case report

Collaborators (5)

Aviv Landau

Youngjun Kim

Alexander Alekseyenko

Jihad Obeid

Katie Kirchoff

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