Prosperi, Mattia

Causal inference and counterfactual prediction in machine learning for actionable healthcare

Prosperi, M., Guo, Y., Sperrin, M., Koopman, J. S., Min, J. S., He, X., Rich, S., Wang, M., Buchan, I. E., & Bian, J. (2020) · Nature Machine Intelligence

Advanced Causal Inference TechniquesMachine Learning in Healthcare

Cited by 459

A systematic review of the effectiveness of mobile apps for monitoring and management of mental health symptoms or disorders

Wang, K., Varma, D. S., & Prosperi, M. (2018) · Journal of Psychiatric Research

Digital Mental Health InterventionsMobile Health and mHealth Applications

Cited by 308

Big data hurdles in precision medicine and precision public health

Prosperi, M., Min, J. S., Bian, J., & Modave, F. (2018) · BMC Medical Informatics and Decision Making

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 200

Evolution pathways of IgE responses to grass and mite allergens throughout childhood

Custovic, A., Sonntag, H.-J., Buchan, I. E., Belgrave, D., Simpson, A., & Prosperi, M. C. F. (2015) · Journal of Allergy and Clinical Immunology

Allergic Rhinitis and SensitizationAsthma and respiratory diseases

Cited by 159

Identification of important factors in an inpatient fall risk prediction model to improve the quality of care using EHR and electronic administrative data: A machine-learning approach

Lindberg, D. S., Prosperi, M., Bjarnadottir, R. I., Thomas, J., Crane, M., Chen, Z., Shear, K., Solberg, L. M., Snigurska, U. A., Wu, Y., et al. (2020) · International Journal of Medical Informatics

Balance, Gait, and Falls PreventionMachine Learning in Healthcare

Cited by 95

Assessing the practice of data quality evaluation in a national clinical data research network through a systematic scoping review in the era of real-world data

Bian, J., Lyu, T., Loiacono, A., Viramontes, T. M., Lipori, G., Guo, Y., Wu, Y., Prosperi, M., George, T. J., Harle, C. A., et al. (2020) · Journal of the American Medical Informatics Association

Electronic Health Records SystemsData Quality and Management

Cited by 92

Challenges in interpreting allergen microarrays in relation to clinical symptoms: A machine learning approach

Prosperi, M. C. F., Belgrave, D., Buchan, I., Simpson, A., & Custovic, A. (2014) · Pediatric Allergy and Immunology

Asthma and respiratory diseasesAllergic Rhinitis and Sensitization

Cited by 60

Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach

Fraccaro, P., Nicolo, M., Bonetto, M., Giacomini, M., Weller, P., Traverso, C. E., Prosperi, M., & OSullivan, D. (2015) · BMC Ophthalmology

Retinal Imaging and AnalysisArtificial Intelligence in Healthcare

Cited by 48

Developing a computable phenotype for identifying children, adolescents, and young adults with diabetes using electronic health records in the DiCAYA Network.

Shao, H., Thorpe, L. E., Islam, S., Bian, J., Guo, Y., Li, P., Bost, S., Dabelea, D., Conway, R., Crume, T., et al. (2025) · Diabetes Care

Machine Learning in HealthcareMedical Coding and Health Information

Cited by 8

Protocol for the development of a reporting guideline for causal and counterfactual prediction models in biomedicine

Xu, J., Guo, Y., Wang, F., Xu, H., Lucero, R., Bian, J., & Prosperi, M. (2022) · BMJ Open

Meta-analysis and systematic reviewsAdvanced Causal Inference Techniques

Cited by 6