Browsing by Author "Castro Rodríguez, Jose A."

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    Emergent Pneumonia in Children
    (2021) Perret Pérez, Cecilia; Le Corre Pérez, Monique Nicole; Castro Rodríguez, Jose A.
    In recent decades there have been multiple pathogens, viruses and bacteria, which have emerged as causal agents of pneumonia affecting adults, albeit less frequently, to children. For the purposes of this article we have classified emerging pathogens as follows: True emerging, to pathogens identified for the very first time affecting human population (SARS-CoV-1, SARS-CoV-2, MERS-CoV, avian influenza, and hantavirus); Re-emerging, to known pathogens which circulation was controlled once, but they have reappeared (measles, tuberculosis, antimicrobial resistant bacteria such as CA-MRSA, Mycoplasma pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Stenotrophomonas maltophilia, and new serotypes of post-vaccine pneumococcal); and finally, those that we have called old known with new presentations, including common pathogens that, in particular condition, have changed their form of presentation (rhinovirus, and non-SARS coronavirus). We will review for each of them their epidemiology, forms of presentation, therapy, and prognosis in children compared to the adult with the aim of being able to recognize them to establish appropriate therapy, prognostics, and effective control measures.
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    Expert artificial intelligence-based natural language processing characterises childhood asthma
    (2020) Seol, Hee Yun; Rolfes, Mary C.; Chung, Wi; Sohn, Sunghwan; Ryu, Euijung; Park, Miguel A.; Kita, Hirohito; Ono, Junya; Croghan, Ivana; Armasu, Sebastian M.; Castro Rodríguez, Jose A.; Weston, Jill D.; Liu, Hongfang; Juhn, Young
    Introduction: The lack of effective, consistent, reproducible and efficient asthma ascertainment methods results in inconsistent asthma cohorts and study results for clinical trials or other studies. We aimed to assess whether application of expert artificial intelligence (AI)-based natural language processing (NLP) algorithms for two existing asthma criteria to electronic health records of a paediatric population systematically identifies childhood asthma and its subgroups with distinctive characteristics. Methods: Using the 1997-2007 Olmsted County Birth Cohort, we applied validated NLP algorithms for Predetermined Asthma Criteria (NLP-PAC) as well as Asthma Predictive Index (NLP-API). We categorised subjects into four groups (both criteria positive (NLP-PAC+/NLP-API+); PAC positive only (NLP-PAC+ only); API positive only (NLP-API+ only); and both criteria negative (NLP-PAC-/NLP-API-)) and characterised them. Results were replicated in unsupervised cluster analysis for asthmatics and a random sample of 300 children using laboratory and pulmonary function tests (PFTs). Results: Of the 8196 subjects (51% male, 80% white), we identified 1614 (20%), NLP-PAC+/NLP-API+; 954 (12%), NLP-PAC+ only; 105 (1%), NLP-API+ only; and 5523 (67%), NLP-PAC-/NLP-API-. Asthmatic children classified as NLP-PAC+/NLP-API+ showed earlier onset asthma, more Th2-high profile, poorer lung function, higher asthma exacerbation and higher risk of asthma-associated comorbidities compared with other groups. These results were consistent with those based on unsupervised cluster analysis and lab and PFT data of a random sample of study subjects. Conclusion: Expert AI-based NLP algorithms for two asthma criteria systematically identify childhood asthma with distinctive characteristics. This approach may improve precision, reproducibility, consistency and efficiency of large-scale clinical studies for asthma and enable population management.