Heller, Katherine

Do no harm: a roadmap for responsible machine learning for health care

Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F., Jung, K., Heller, K., Kale, D., Saeed, M., et al. (2019) · Nature Medicine

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 1,266

Underspecification Presents Challenges for Credibility in Modern Machine Learning

D'Amour, A., Heller, K., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., Chen, C., Deaton, J., Eisenstein, J., Hoffman, M. D., et al. (2020) · arXiv

Domain Adaptation and Few-Shot LearningAdversarial Robustness in Machine Learning

Cited by 398

Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study

Sendak, M. P., Ratliff, W., Sarro, D., Alderton, E., Futoma, J., Gao, M., Nichols, M., Revoir, M., Yashar, F., Miller, C., et al. (2020) · JMIR Medical Informatics

Sepsis Diagnosis and TreatmentMachine Learning in Healthcare

Cited by 287

Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): a retrospective, single-site study

Corey, K. M., Kashyap, S., Lorenzi, E., Lagoo-Deenadayalan, S. A., Heller, K., Whalen, K., Balu, S., Heflin, M. T., McDonald, S. R., Swaminathan, M., et al. (2018) · PLOS Medicine

Machine Learning in HealthcareCardiac, Anesthesia and Surgical Outcomes

Cited by 204

A Shared Vision for Machine Learning in Neuroscience

Vu, M.-A. T., Adalı, T., Ba, D., Buzsáki, G., Carlson, D., Heller, K., Liston, C., Rudin, C., Sohal, V. S., Widge, A. S., et al. (2018) · The Journal of Neuroscience

Functional Brain Connectivity StudiesUndergraduate Neuroscience Education and Research

Cited by 191

An insula-frontostriatal network mediates flexible cognitive control by adaptively predicting changing control demands

Jiang, J., Beck, J., Heller, K., & Egner, T. (2015) · Nature Communications

Neural and Behavioral Psychology StudiesNeurotransmitter Receptor Influence on Behavior

Cited by 160

A toolbox for surfacing health equity harms and biases in large language models

Pfohl, S. R., Cole-Lewis, H., Sayres, R., Neal, D., Asiedu, M., Dieng, A., Tomasev, N., Rashid, Q. M., Azizi, S., Rostamzadeh, N., et al. (2024) · Nature Medicine

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 113

Machine learning for early detection of sepsis: an internal and temporal validation study

Bedoya, A. D., Futoma, J., Clement, M. E., Corey, K., Brajer, N., Lin, A., Simons, M. G., Gao, M., Nichols, M., Balu, S., et al. (2020) · JAMIA Open

Sepsis Diagnosis and TreatmentMachine Learning in Healthcare

Cited by 109

Tackling bias in AI health datasets through the STANDING TOGETHER initiative

Ganapathi, S., Palmer, J., Alderman, J. E., Calvert, M., Espinoza, C., Gath, J., Ghassemi, M., Heller, K., Mckay, F., Karthikesalingam, A., et al. (2022) · Nature Medicine

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 82

Federated and Differentially Private Learning for Electronic Health Records

Pfohl, S. R., Dai, A. M., & Heller, K. (2019) · arXiv

Privacy-Preserving Technologies in DataMachine Learning in Healthcare

Cited by 45