AIMC Topic: Machine Learning

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Causal Phenotype Discovery via Deep Networks.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The rapid growth of digital health databases has attracted many researchers interested in using modern computational methods to discover and model patterns of health and illness in a research program known as computational phenotyping. Much of the wo...

Automated mutual exclusion rules discovery for structured observational codes in echocardiography reporting.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Structured reporting in medicine has been argued to support and enhance machine-assisted processing and communication of pertinent information. Retrospective studies showed that structured echocardiography reports, constructed through point-and-click...

SIM-ELM: Connecting the ELM model with similarity-function learning.

Neural networks : the official journal of the International Neural Network Society
This paper moves from the affinities between two well-known learning schemes that apply randomization in the training process, namely, Extreme Learning Machines (ELMs) and the learning framework using similarity functions. These paradigms share a com...

Predicting drug side effects by multi-label learning and ensemble learning.

BMC bioinformatics
BACKGROUND: Predicting drug side effects is an important topic in the drug discovery. Although several machine learning methods have been proposed to predict side effects, there is still space for improvements. Firstly, the side effect prediction is ...

TWSVR: Regression via Twin Support Vector Machine.

Neural networks : the official journal of the International Neural Network Society
Taking motivation from Twin Support Vector Machine (TWSVM) formulation, Peng (2010) attempted to propose Twin Support Vector Regression (TSVR) where the regressor is obtained via solving a pair of quadratic programming problems (QPPs). In this paper ...

Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images.

Proceedings of the National Academy of Sciences of the United States of America
Noninvasive, radiological image-based detection and stratification of Gleason patterns can impact clinical outcomes, treatment selection, and the determination of disease status at diagnosis without subjecting patients to surgical biopsies. We presen...

Multimodal Task-Driven Dictionary Learning for Image Classification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Dictionary learning algorithms have been successfully used for both reconstructive and discriminative tasks, where an input signal is represented with a sparse linear combination of dictionary atoms. While these methods are mostly developed for singl...

Outcome Prediction in Clinical Treatment Processes.

Journal of medical systems
Clinical outcome prediction, as strong implications for health service delivery of clinical treatment processes (CTPs), is important for both patients and healthcare providers. Prior studies typically use a priori knowledge, such as demographics or p...

Neural hypernetwork approach for pulmonary embolism diagnosis.

BMC research notes
BACKGROUND: Hypernetworks are based on topological simplicial complexes and generalize the concept of two-body relation to many-body relation. Furthermore, Hypernetworks provide a significant generalization of network theory, enabling the integration...