Version 2.0 (10/06/16)
The self-defined font is used, because 'Calibri' is
not supported in the latex font packages. 'LuaLatex'
should be used.
This template has been generated according to
the Power Point template of LUMC in 2016.
This is generated purely with images as the
The bullet point color was used purely for personal
Any more adding to the template are welcome.
In order to use the navigation bar, the title
for each section should not be to long.
Adding animation is possible. I prefer to add another
pdf file with:
This is my first template, the files might be not
well organized, sorry for that.
Division Medical imaging processing,
Leiden University Medical Center
Generated by Shengnan Liu on 21-01-2016
Cleaned up for further usage on 10-06-2016
Template criado por Alexandre do Nascimento Silva (firstname.lastname@example.org)a partir da classe ABNT para uso das monografias de conclusão de curso da Faculdade ÁREA1 e modificado pelo professor Lázaro Silva (email@example.com) para uso na internet sem a necessidade de instalação.
This article proposes to obtain a statistical model of the daily peak electricity load of a household located in Austin-TX,USA. The Box-Jenkins methodology was followed to obtain the best fit for the time-series. Four models provided a good fit: ARIMA(0,1,2), ARIMA(1,1,2), SARIMA(0,1,2)(0,1,1) and SARIMA(1,1,2)(0,1,1). The model with the highest Akaike Information Criteria was the ARIMA(1,2,2). However, the model with the highest forecast accuracy was the SARIMA(1,1,2)(0,1,1), which obtained an RMSE of 0.296 and a MAPE Of 15.00.
Accurate prognosis and prediction of a patient's current disease state is critical in an ICU. The use of vast amounts of digital medical information can help in predicting the best course of action for the diagnosis and treatment of patients. The proposed technique investigates the strength of using a combination of latent variable models (latent dirichlet allocation) and structured data to transform the information streams into potentially actionable knowledge. In this project, I use Apache Spark to predict mortality among ICU patients so that it can be used as an acuity surrogate to help physicians identify the patients in need of immediate care.