RESUMO
In this paper we report the use of the open source Spatiotemporal Epidemiological Modeler (STEM, www.eclipse.org/stem) to compare three basic models for seasonal influenza transmission. The models are designed to test for possible differences between the seasonal transmission of influenza A and B. Model 1 assumes that the seasonality and magnitude of transmission do not vary between influenza A and B. Model 2 assumes that the magnitude of seasonal forcing (i.e., the maximum transmissibility), but not the background transmission or flu season length, differs between influenza A and B. Model 3 assumes that the magnitude of seasonal forcing, the background transmission, and flu season length all differ between strains. The models are all optimized using 10 years of surveillance data from 49 of 50 administrative divisions in Israel. Using a cross-validation technique, we compare the relative accuracy of the models and discuss the potential for prediction. We find that accounting for variation in transmission amplitude increases the predictive ability compared to the base. However, little improvement is obtained by allowing for further variation in the shape of the seasonal forcing function.
Assuntos
Simulação por Computador , Vírus da Influenza A Subtipo H1N1 , Vírus da Influenza B , Influenza Humana/transmissão , Influenza Humana/virologia , Estações do Ano , Algoritmos , Surtos de Doenças , Previsões , Humanos , Incidência , Vírus da Influenza A Subtipo H1N1/isolamento & purificação , Vírus da Influenza B/isolamento & purificação , Influenza Humana/diagnóstico , Influenza Humana/epidemiologia , Israel/epidemiologia , Modelos Estatísticos , Valor Preditivo dos Testes , Reprodutibilidade dos Testes , Medição de Risco , Vigilância de Evento SentinelaRESUMO
In this paper we describe a novel social-medical discovery solution, based on an idea of social and medical data unification. Built on foundations of exploratory search technologies, the proposed discovery solution is better tailored for the social-medical discovery task. We then describe its implementation within the IBM Medics system and discuss a sample usecase which demonstrates several new social-medical discovery opportunities.
Assuntos
Informática Médica/métodos , Algoritmos , Computadores , Registros Eletrônicos de Saúde , Humanos , Internet , Informática Médica/tendências , Software , Interface Usuário-ComputadorRESUMO
Adverse drug event (ADE) has significant implications on patient safety and is recognized as a major cause of fatalities and hospital expenses. Although some medical systems today can help reduce the number of ADE occurrences, these primarily take into account clinical factors-even though recent studies show the significance of genetic profiles in ADE detection. Incorporating pharmacogenetics knowledge and data from genetic test results into these systems can improve the accuracy of preliminary alerts about potential ADEs. However, pharmacogenetics knowledge is unstructured, making it inappropriate for use in a system that involves automatic processing. We propose a methodology that can help incorporate the pharmacogenetics knowledge. Specifically, we show how pharmacogenetics knowledge can be expressed in a medical system and used together with the patient genetic data to provide alerts about ADEs at the point of care.
