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    Developing and Validating an Automatic Support System for Tumor Coding in Pathology Reports in Spanish
    (American Society of Clinical Oncology (ASCO), 2025-02)
    Fabián Villena
    ;
    Pablo Báez
    ;
    Sergio Peñafiel
    ;
    Matías Rojas
    ;
    Inti Paredes
    Purpose Pathology reports provide valuable information for cancer registries to understand, plan, and implement strategies to mitigate the impact of cancer. However, coding essential information from unstructured reports is performed by experts in a time-consuming manual process. We developed and validated a novel two-step automatic coding system that first recognizes tumor morphology and topography mentions from free text and then suggests codes from the International Classification of Diseases for Oncology (ICD-O) in Spanish. Materials and Methods We created an annotated corpus of tumor morphology and topography mentions consisting of 1,101 documents. We combined it with the CANTEMIST corpus (Cancer Text Mining Shared Task). Specifically, we implemented a named entity recognition (NER) model using the bidirectional long short-term memory network-conditional random field architecture enhanced with a stacked embedding layer. We applied transfer learning from state-of-the-art pretrained language models to obtain high-quality contextual representations, thus improving the detection of entities. The mentions found using this model were subsequently oded using a search engine tailored to the ICD-O codes. Results Our NER models achieved an F1 score of 0.86 and 0.90 for tumor morphology and topography, respectively. The overall performance of our automatic coding system achieved an accuracy at five suggestions of 0.72 and 0.65 for tumor morphology and topography, respectively. Conclusion These results demonstrate the feasibility of implementing natural language processing tools in the routine of a cancer center to extract and code valuable information from pathology reports. Our recommender system allows reliable and transparent coding at the moment of consultation. This publication shares the annotated corpus in Spanish, annotation guidelines, and source code to reproduce our experiments.
    Scopus© Citations 3  1
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    Estimating the effect of social inequalities on the mitigation of COVID-19 across communities in Santiago de Chile
    (2021)
    Nicolò Gozzi
    ;
    Michele Tizzoni
    ;
    Matteo Chinazzi
    ;
    FERRES, LEONARDO ADRIÁN  
    ;
    Alessandro Vespignani
    We study the spatio-temporal spread of SARS-CoV-2 in Santiago de Chile using anonymized mobile phone data from 1.4 million users, 22% of the whole population in the area, characterizing the effects of non-pharmaceutical interventions (NPIs) on the epidemic dynamics. We integrate these data into a mechanistic epidemic model calibrated on surveillance data. As of August 1, 2020, we estimate a detection rate of 102 cases per 1000 infections (90% CI: [95–112 per 1000]). We show that the introduction of a full lockdown on May 15, 2020, while causing a modest additional decrease in mobility and contacts with respect to previous NPIs, was decisive in bringing the epidemic under control, highlighting the importance of a timely governmental response to COVID-19 outbreaks. We find that the impact of NPIs on individuals’ mobility correlates with the Human Development Index of comunas in the city. Indeed, more developed and wealthier areas became more isolated after government interventions and experienced a significantly lower burden of the pandemic. The heterogeneity of COVID-19 impact raises important issues in the implementation of NPIs and highlights the challenges that communities affected by systemic health and social inequalities face adapting their behaviors during an epidemic.
    Scopus© Citations 124  1
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    Visión crítica de la modelación del efecto de propofol
    (2019)
    Pablo Sepúlveda
    ;
    M.C. Demaría
    Scopus© Citations 6  1
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    Scopus© Citations 50  1