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Barbara Di Camillo on Muck Rack

Barbara Di Camillo

As seen in: Nature, PLOS, BMJ Open Diabetes Research & Care, Frontiers, MDPI, Diabetes Research and Clinical Practice
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Articles

The BRAINTEASER Datasets: Clinical, Wearable and Environmental Data for ALS & MS Progression Modeling - Scientific Data

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By Guglielmo Faggioli, Laura Menotti, Stefano Marchesin, Lara Ahmad, Anca Loredana Alungulese, Giovanni Birolo, Pietro Bosoni, Paola Cavalla, Mamede De Carvalho, Sergio Martinez, Sergio Martínez, Marta Gromicho, Enrico Longato, Sara Madeira, Umberto Manera, Erica Tavazzi, Vladimir Urošević, Barbara Di Camillo, Maria Fernanda Cabrera-Umpiérrez
| Nature Verified
Abstract Amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS) are debilitating diseases with unpredictable progression. Artificial Intelligence-based tools for modelling disease progression could significantly improve the quality of life for patients and caregivers while supporting clinicians in delivering more personalized and timely care. However, the limited availability of data hinders the development, testing, and reproducibility of such predictive tools.
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Bone Marrow CD34+/lin- Cells of Patients with Chronic-Phase Chronic Myeloid Leukemia (CP-CML) After 12 Months of Nilotinib Treatment Exhibit a Different Gene Expression Signature Compared to the Diagnosis and the Corresponding Cells from Healthy Subjects

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By Alessandra Trojani, Ester Pungolino, Barbara Di Camillo, Luca Emanuele Bossi
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Deep-learning-based natural-language-processing models to identify cardiovascular disease hospitalisations of patients with diabetes from routine visits’ text | Scientific Reports

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By Barbara Di Camillo
| Nature Verified
Abstract Writing notes is the most widespread method to report clinical events. Therefore, most of the information about the disease history of a patient remains locked behind free-form text. Natural language processing (NLP) provides a solution to automatically transform free-form text into structured data.
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