Elizabeth Geena Woo*, Michael C Burkhart*, Emily Alsentzer, Brett Beaulieu-Jones Preprint (2024)
*co-first authors

Our team demonstrated that synthetic data distillation can fine-tune smaller, open-source large-language models (LLMs) to achieve performance similar to larger models in extracting clinical information. This smaller model outperforms its base version and sometimes even the larger model. This approach will enable more scalable and cost-efficient clinical information extraction, improving tasks like patient phenotyping.

Brett K Beaulieu-Jones, Francesca Frau, Sylvie Bozzi, Karen J Chandross, M Judith Peterschmitt, Caroline Cohen, Catherine Coulovrat, Dinesh Kumar, Mark J Kruger, Scott L Lipnick, Lane Fitzsimmons, Isaac S Kohane, Clemens R Scherzer. npj Parkinson's disease (2024)

Our team compared Parkinson's disease (PD) progression across research and real-world populations, utilizing real-world data (RWD) and large language models for detailed characterization. It finds that patients in real-world settings are diagnosed later and start treatment later than those in research populations, with faster motor and cognitive progression in real-world cohorts. The study highlights the differences between research and real-world populations, emphasizing the need to use diverse data sources and account for biases in clinical trial design and analyses.

Beaulieu-Jones, Brett K., Mauricio F Villamar, Phil Scordis, Ana Paula Bartmann, Waqar Ali, Benjamin D Wissel, Emily Alsentzer, Johann de Jong, Arijit Patra, Isaac Kohane. Lancet Digital Health (2023)

Our team demonstrated machine learning models, particularly large language models pre-trained on domain-specific data, are highly effective in predicting seizure recurrence in children after an initial seizure-like event. These models outperformed traditional structured data approaches and indicate that clinical notes contain significant information useful for the prediction of seizure recurrence.

Lane Fitzsimmons, Undiagnosed Diseases Network, Brett Beaulieu-Jones*, Shilpa Nadimpalli Kobren* Preprint (in press) (2024)
*co-corresponding

The biological mechanisms causing extreme symptoms in rare disease patients are complex and often elusive. This study analyzes genotype and phenotype data from the UK Biobank to understand the pathways leading to seizures in undiagnosed patients from the Undiagnosed Diseases Network. By examining milder, related symptoms in UK Biobank participants with similar genetic variants, the study aims to shed light on the molecular mechanisms behind these rare conditions

Lane Fitzsimmons, Francesca Frau, Sylvie Bozzi, Karen Chandross, Brett Beaulieu-Jones Preprint (2024)

This study analyzed Parkinson's disease (PD) progression by examining clinical events across different Hoehn & Yahr (H&Y) stages extracted using natural language processing. It provides a view of healthcare utilization at different H&Y stages, models expected H&Y progression and demonstrates the potential value for a therapeutic which would slow progression.

Beaulieu-Jones, Brett K., William Yuan, Gabriel A. Brat, Andrew L. Beam, Griffin Weber, Marshall Ruffin, and Isaac S. Kohane. NPJ digital medicine (2021)

We trained deep learning models on clinician-initiated administrative data for 42.9 million admissions and found performance close to full EMR-based benchmarks for inpatient outcomes. These models rely heavily on clinical behavior, and should not be used for individualized clinical decision making. For meaningful clinical guidance, models should outperform these benchmarks using data sources that capture patient state rather than clinician actions (i.e., looking over their shoulder).

Beaulieu‐Jones, Brett K., Samuel G. Finlayson, William Yuan, Russ B. Altman, Isaac S. Kohane, Vinay Prasad, and Kun‐Hsing Yu. Clinical Pharmacology & Therapeutics (2020)

The 21st Century Cures Act requires the US FDA to create guidelines for using real-world evidence (RWE) in the regulatory process. While RWE has led to crucial medical findings, it faces challenges in proving treatment efficacy compared to randomized controlled trials. In this review article, we summarized the advantages and limitations of RWE, identified the key opportunities for RWE, and pointed the way forward to maximize the potential of RWE for regulatory purposes.

Beaulieu-Jones, Brett K., Zhiwei Steven Wu, Chris Williams, Ran Lee, Sanjeev P. Bhavnani, James Brian Byrd, and Casey S. Greene. Circulation: Cardiovascular Quality and Outcomes (2019)

Our team has developed a method using deep neural networks to generate synthetic data that closely resembles real participants from the SPRINT trial, ensuring privacy while maintaining the utility of the data for research. This technique allows for the sharing of clinical data with researchers for secondary analysis without risking patient privacy.

Beaulieu-Jones, Brett K., and Casey S. Greene. Nature biotechnology (2017)

Continuous analysis is a workflow that integrates Docker container technology with continuous integration to automatically rerun computational analyses upon any changes in source code or data. This approach facilitates effortless reproducibility of research results for peers and provides an audit trail for data analyses, enhancing transparency and reliability in scientific studies.

Beaulieu-Jones, Brett K., and Casey S. Greene. Journal of biomedical informatics (2016)

We developed a semi-supervised learning technique to improve the extraction of phenotypes from electronic health records, aiding in the identification of disease subtypes and genetic associations. This method has shown promise in enhancing classification accuracy and predicting patient outcomes, even with limited high-quality data.