Research output per year
Research output per year
Personal profile
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
Education/Academic qualification
Computer Science, Master of Science, Interpretable Machine Learning models to predict healthy ageing in People Living with HIV, University of Modena and Reggio Emilia
Award Date: 11 Oct 2019
Classical Music, Master of Arts, Higher Institute of Musical Studies of Modena and Carpi
Award Date: 11 Oct 2013
Fingerprint
Dive into the research topics where Davide Ferrari is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
- 1 Similar Profiles
Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
-
Multi-objective Symbolic Regression for Clinician-in-the-loop Machine Learning algorithms
Ferrari, D., Wang, Y., Edgeworth, J. & Curcin, V., 28 Oct 2024, In: European journal of public health. 34, Supplement_3, ckae144.1202.Research output: Contribution to journal › Article › peer-review
Open AccessFile10 Downloads (Pure) -
Question answering systems for health professionals at the point of care - a systematic review
Kell, G., Roberts, A., Umansky, S., Qian, L., Ferrari, D., Soboczenski, F., Wallace, B., Patel, N. & Marshall, I. J., 13 Jan 2024, (Accepted/In press) In: Journal of the American Medical Informatics Association : JAMIA.Research output: Contribution to journal › Article › peer-review
Open AccessFile125 Downloads (Pure) -
Using interpretable Machine Learning to predict bloodstream infection and antimicrobial resistance in patients admitted to ICU: early alert predictors based on EHR data to guide antimicrobial stewardship
Ferrari, D., Arina, P., Edgeworth, J., Curcin, V., Guidetti , V., Mandreoli, F. & Wang, Y., 16 Oct 2024, In: PLOS digital health. 3, 10, e0000641.Research output: Contribution to journal › Article › peer-review
Open AccessFile19 Downloads (Pure) -
Multi-objective Symbolic Regression to Generate Data-driven, Non-fixed Structure and Intelligible Mortality Predictors using EHR: Binary Classification Methodology and Comparison with State-of-the-art
Ferrari, D., Guidetti, V., Wang, Y. & Curcin, V., 29 Apr 2023, In: Proceedings / AMIA. 2022, eCollection , p. 442-451 10 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile38 Downloads (Pure) -
Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis
CovidRetro collaboration, 12 Jul 2022, In: BMJ (Clinical research ed.). 378, p. e069881 e069881.Research output: Contribution to journal › Article › peer-review
Open Access34 Citations (Scopus)