Persona:
Castillo Cara, José Manuel

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0000-0002-2990-7090
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Castillo Cara
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José Manuel
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2020 - 20242
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Autor: Carrillo Larco, Rodrigo M. × search.filters.applied.f.itemDepartamento: Inteligencia Artificial ×

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Mostrando 1 - 2 de 2
  • Miniatura
    Publicación
    Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach
    (Taylor & Francis, 2020-06-15) Carrillo Larco, Rodrigo M.; Castillo Cara, José Manuel
    Background: The COVID-19 pandemic has attracted the attention of researchers and clinicians whom have provided evidence about risk factors and clinical outcomes. Research on the COVID-19 pandemic benefiting from open-access data and machine learning algorithms is still scarce yet can produce relevant and pragmatic information. With country-level pre-COVID-19-pandemic variables, we aimed to cluster countries in groups with shared profiles of the COVID-19 pandemic. Methods: Unsupervised machine learning algorithms (k-means) were used to define data-driven clusters of countries; the algorithm was informed by disease prevalence estimates, metrics of air pollution, socio-economic status and health system coverage. Using the one-way ANOVA test, we compared the clusters in terms of number of confirmed COVID-19 cases, number of deaths, case fatality rate and order in which the country reported the first case. Results: The model to define the clusters was developed with 155 countries. The model with three principal component analysis parameters and five or six clusters showed the best ability to group countries in relevant sets. There was strong evidence that the model with five or six clusters could stratify countries according to the number of confirmed COVID-19 cases (p<0.001). However, the model could not stratify countries in terms of number of deaths or case fatality rate. Conclusions: A simple data-driven approach using available global information before the COVID-19 pandemic, seemed able to classify countries in terms of the number of confirmed COVID-19 cases. The model was not able to stratify countries based on COVID-19 mortality data.
  • Miniatura
    Publicación
    A multimodal approach using fundus images and text meta-data in a machine learning classifier with embeddings to predict years with self-reported diabetes – an exploratory analysis
    (Elsevier, 2024-05-22) Carrillo Larco, Rodrigo M.; Bravo Rocca, Gusseppe; Castillo Cara, José Manuel; Xu, Xiaolin; Bernabé Ortiz, Antonio; https://orcid.org/0000-0002-2090-1856; https://orcid.org/0000-0001-6824-1124; https://orcid.org/0000-0002-8203-9878; https://orcid.org/0000-0002-6834-1376
    Aims Machine learning models can use image and text data to predict the number of years since diabetes diagnosis; such model can be applied to new patients to predict, approximately, how long the new patient may have lived with diabetes unknowingly. We aimed to develop a model to predict self-reported diabetes duration. Methods We used the Brazilian Multilabel Ophthalmological Dataset. Unit of analysis was the fundus image and its meta-data, regardless of the patient. We included people 40 + years and fundus images without diabetic retinopathy. Fundus images and meta-data (sex, age, comorbidities and taking insulin) were passed to the MedCLIP model to extract the embedding representation. The embedding representation was passed to an Extra Tree Classifier to predict: 0–4, 5–9, 10–14 and 15 + years with self-reported diabetes. Results There were 988 images from 563 people (mean age = 67 years; 64 % were women). Overall, the F1 score was 57 %. The group 15 + years of self-reported diabetes had the highest precision (64 %) and F1 score (63 %), while the highest recall (69 %) was observed in the group 0–4 years. The proportion of correctly classified observations was 55 % for the group 0–4 years, 51 % for 5–9 years, 58 % for 10–14 years, and 64 % for 15 + years with self-reported diabetes. Conclusions The machine learning model had acceptable accuracy and F1 score, and correctly classified more than half of the patients according to diabetes duration. Using large foundational models to extract image and text embeddings seems a feasible and efficient approach to predict years living with self-reported diabetes.