About

Colin A Ellis, MD, is an Assistant Professor of Neurology at the University of Pennsylvania, affiliated with the Department of Neurology at the Hospital of the University of Pennsylvania. He holds a ScB in Cognitive Neuroscience from Brown University and earned his MD from the University of Pennsylvania School of Medicine. Dr. Ellis specializes in the management of seizures and epilepsy, with clinical expertise in EEG, anti-seizure medications, surgical treatment of epilepsy, neurostimulation, and implantable devices. He also has expertise in genetics and the genetic causes of epilepsy, providing genetic evaluations for patients with epilepsy at the University of Pennsylvania and the Children's Hospital of Philadelphia. His research focuses on the genetic basis of epilepsy, including the discovery of causative genes, heritability within families, and utilizing genetics to improve diagnosis and treatment. Additionally, he is involved in mining electronic health records using natural language processing to advance epilepsy research.

Research topics

• Psychiatry
• Medicine
• Computer Science
• Artificial Intelligence
• Natural Language Processing
• Machine Learning
• Internal medicine
• Data Mining
• Intensive care medicine
• Gerontology
• Demography
• Pathology
• Bioinformatics
• Family medicine
• Environmental health
• Genetics
• Biology

Selected publications

• Disparities in Genetic Testing for Neurologic Disorders

  Neurology · 2024 · 23 citations

  Senior authorCorresponding
  • Medicine
  • Demography
  • Gerontology

  BACKGROUND AND OBJECTIVES: Genetic testing is now the standard of care for many neurologic conditions. Health care disparities are unfortunately widespread in the US health care system, but disparities in the utilization of genetic testing for neurologic conditions have not been studied. We tested the hypothesis that access to and results of genetic testing vary according to race, ethnicity, sex, socioeconomic status, and insurance status for adults with neurologic conditions. METHODS: We analyzed retrospective data from patients who underwent genetic evaluation and testing through our institution's neurogenetics program. We tested for differences between demographic groups in 3 steps of a genetic evaluation pathway: (1) attending a neurogenetic evaluation, (2) completing genetic testing, and (3) receiving a diagnostic result. We compared patients on this genetic evaluation pathway with the population of all neurology outpatients at our institution, using univariate and multivariable logistic regression analyses. RESULTS: < 0.001). Among patients who underwent evaluation, there were no disparities in the likelihood of completing genetic testing, nor in the likelihood of a diagnostic result after adjusting for age. Analyses restricted to specific indications for genetic testing supported these findings. DISCUSSION: We observed unequal utilization of our clinical neurogenetics program for patients from marginalized and minoritized demographic groups, especially Black patients. Among patients who do undergo evaluation, all groups benefit similarly from genetic testing when it is indicated. Understanding and removing barriers to accessing genetic testing will be essential to health care equity and optimal care for all patients with neurologic disorders.

  PublisherOA PDFDOI

• Genetic testing in adults with neurologic disorders: indications, approach, and clinical impacts

  Journal of Neurology · 2023 · 18 citations

  Senior authorCorresponding
  • Medicine
  • Intensive care medicine
  • Bioinformatics
  PublisherOA PDFDOI

• Extracting seizure frequency from epilepsy clinic notes: a machine reading approach to natural language processing

  Journal of the American Medical Informatics Association · 2022 · 62 citations

  • Computer Science
  • Computer Science
  • Artificial Intelligence

  OBJECTIVE: Seizure frequency and seizure freedom are among the most important outcome measures for patients with epilepsy. In this study, we aimed to automatically extract this clinical information from unstructured text in clinical notes. If successful, this could improve clinical decision-making in epilepsy patients and allow for rapid, large-scale retrospective research. MATERIALS AND METHODS: We developed a finetuning pipeline for pretrained neural models to classify patients as being seizure-free and to extract text containing their seizure frequency and date of last seizure from clinical notes. We annotated 1000 notes for use as training and testing data and determined how well 3 pretrained neural models, BERT, RoBERTa, and Bio_ClinicalBERT, could identify and extract the desired information after finetuning. RESULTS: The finetuned models (BERTFT, Bio_ClinicalBERTFT, and RoBERTaFT) achieved near-human performance when classifying patients as seizure free, with BERTFT and Bio_ClinicalBERTFT achieving accuracy scores over 80%. All 3 models also achieved human performance when extracting seizure frequency and date of last seizure, with overall F1 scores over 0.80. The best combination of models was Bio_ClinicalBERTFT for classification, and RoBERTaFT for text extraction. Most of the gains in performance due to finetuning required roughly 70 annotated notes. DISCUSSION AND CONCLUSION: Our novel machine reading approach to extracting important clinical outcomes performed at or near human performance on several tasks. This approach opens new possibilities to support clinical practice and conduct large-scale retrospective clinical research. Future studies can use our finetuning pipeline with minimal training annotations to answer new clinical questions.

  DOI

Recent grants

• Polygenic risk in familial epilepsy

  NIH · $1.1M · 2021–2027

Frequent coauthors

• Ingo Helbig

  University of Pennsylvania

  62 shared

• Ingrid E. Scheffer

  Florey Institute of Neuroscience and Mental Health

  47 shared

• David Lewis‐Smith

  46 shared

• Samuel F. Berkovic

  Austin Health

  41 shared

• Ruth Ottman

  Columbia University Irving Medical Center

  30 shared

• Dennis Lal

  29 shared

• Peter D. Galer

  University of Pennsylvania

  27 shared

• Sarah Weckhuysen

  VIB-UAntwerp Center for Molecular Neurology

  27 shared

Education

• M.D.

  University of Pennsylvania

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