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Predicción de diagnóstico a partir de datos médicos utilizando algoritmos de PLN
(ITESO, 2024-05) Bhat, Ashwin G.; Muñoz-Elguezábal, Juan F.
In the contemporary medical landscape, there is a pressing need for rapid and accurate solutions to health emergencies, as well as access to expert physician insights. Traditional approaches involve clinical consultations where doctors assess patient histories and recommend specialist interventions. However, with the advent of Natural Language Processing (NLP) – a subset of machine learning – there is potential to revolutionize this process. NLP, when applied to medical findings, offers promising avenues for predicting patient diagnoses and identifying early indicators of chronic diseases. Given the vast repositories of publicly accessible medical data, there is an opportunity to harness advanced models such as Spark NLP, Spacy, and Deep Learning to distill meaningful insights from these findings. Such models can not only aid in patient diagnosis but also provide a broader perspective on population health trends, paving the way for proactive disease prevention strategies. This document delves into the utilization of diverse NLP algorithms for diagnosing conditions based on medical findings, underscoring the transformative potential of machine learning in clinical analysis.