Chute, Christopher G

Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications

Savova, G. K., Masanz, J. J., Ogren, P. V., Zheng, J., Sohn, S., Kipper-Schuler, K. C., & Chute, C. G. (2010) · Journal of the American Medical Informatics Association

Biomedical Text Mining and OntologiesMachine Learning in Healthcare

Cited by 1,967

Validity of Self-Reported Waist and Hip Circumferences in Men and Women

Rimm, E. B., Stampfer, M. J., Colditz, G. A., Chute, C. G., Litin, L. B., & Willett, W. C. (1990) · Epidemiology

Body Composition Measurement TechniquesStatistical Methods in Epidemiology

Cited by 1,015

The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment

Haendel, M. A., Chute, C. G., Bennett, T. D., Eichmann, D. A., Guinney, J., Kibbe, W. A., Payne, P. R. O., Pfaff, E. R., Robinson, P. N., Saltz, J. H., et al. (2021) · Journal of the American Medical Informatics Association

Research Data Management PracticesElectronic Health Records Systems

Cited by 553

The Human Phenotype Ontology in 2024: phenotypes around the world

Gargano, M. A., Matentzoglu, N., Coleman, B., Addo-Lartey, E. B., Anagnostopoulos, A. V., Anderton, J., Avillach, P., Bagley, A. M., Bakštein, E., Balhoff, J. P., et al. (2024) · Nucleic Acids Research

Biomedical Text Mining and OntologiesGenomics and Rare Diseases

Cited by 410

Electronic Medical Records for Genetic Research: Results of the eMERGE Consortium

Kho, A. N., Pacheco, J. A., Peissig, P. L., Rasmussen, L., Newton, K. M., Weston, N., Crane, P. K., Pathak, J., Chute, C. G., Bielinski, S. J., et al. (2011) · Science Translational Medicine

Electronic Health Records SystemsGenetic Associations and Epidemiology

Cited by 336

Characterizing long COVID: deep phenotype of a complex condition

Deer, R. R., Rock, M. A., Vasilevsky, N., Carmody, L., Rando, H., Anzalone, A. J., Basson, M. D., Bennett, T. D., Bergquist, T., Boudreau, E. A., et al. (2021) · EBioMedicine

Long-Term Effects of COVID-19Biomedical Text Mining and Ontologies

Cited by 200

Impact of data fragmentation across healthcare centers on the accuracy of a high-throughput clinical phenotyping algorithm for specifying subjects with type 2 diabetes mellitus

Wei, W.-Q., Leibson, C. L., Ransom, J. E., Kho, A. N., Caraballo, P. J., Chai, H. S., Yawn, B. P., Pacheco, J. A., & Chute, C. G. (2012) · Journal of the American Medical Informatics Association

Machine Learning in HealthcareMedical Coding and Health Information

Cited by 112

Mondo: Unifying diseases for the world, by the world

Vasilevsky, N. A., Matentzoglu, N. A., Toro, S., Flack, J. E., Hegde, H., Unni, D. R., Alyea, G. F., Amberger, J. S., Babb, L., Balhoff, J. P., et al. (2022) · medRxiv

Biomedical Text Mining and OntologiesSemantic Web and Ontologies

Cited by 107

A Census of Disease Ontologies

Haendel, M. A., McMurry, J. A., Relevo, R., Mungall, C. J., Robinson, P. N., & Chute, C. G. (2018) · Annual Review of Biomedical Data Science

Biomedical Text Mining and OntologiesSemantic Web and Ontologies

Cited by 49

The promise of AI and technology to improve quality of life and care for older adults

Abadir, P. M., Chellappa, R., Choudhry, N., Demiris, G., Ganesan, D., Karlawish, J., Li, R. M., Moore, J. H., Walston, J. D., AITCs, D. &. A. O. T., et al. (2023) · Nature Aging

Technology Use by Older AdultsArtificial Intelligence in Healthcare and Education

Cited by 24