Data-driven phenotype discoveries on Electronic Health Records (EHR) data have recently
drawn benefits across many aspects of clinical practice. In the method described in this …

Abstract Learning the sparse Gaussian Markov Random Field, or conversely, estimating the
sparse inverse covariance matrix is an approach to uncover the underlying dependency …

Sepsis is a serious, life-threatening condition that presents a growing problem in medicine
and health-care. It is characterized by quick progression and high variability in the disease …

In wireless sensor networks, the value of transmission range has a direct impact on energy
utilization. A sensor node makes use of the maximum transmission power and transmission …

The primary goal of Machine learning (ML) models in the prediction of medical conditions is
to accurately predict (classify) the occurrence of a disease, or therapy. Many ML models …

Predictive modeling is an ever-increasingly important part of decision making. The advances
in Machine Learning predictive modeling have spread across many domains bringing …

Data-driven phenotype discoveries on Electronic Health Records (EHR) data have recently
drawn benefits across many aspects of clinical practice. In the method described in this …

Sepse é uma resposta inflamatória sistêmica a uma infecção grave, podendo levar à
falência de múltiplos órgãos e à morte. Sua fisiopatologia é caracterizada pelas respostas …

Structured learning is an area that deals with the learning of complex objects from complex
(structured) data. Structured data, in this context, represent data that consists of several …

Quantifying the properties of interest is an important problem in many domains, eg,
assessing the condition of a patient, estimating the risk of an investment or relevance of the …