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 (in press) (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.

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.