AIMC Topic: Machine Learning

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Combining Human Computing and Machine Learning to Make Sense of Big (Aerial) Data for Disaster Response.

Big data
Aerial imagery captured via unmanned aerial vehicles (UAVs) is playing an increasingly important role in disaster response. Unlike satellite imagery, aerial imagery can be captured and processed within hours rather than days. In addition, the spatial...

Modeling Disease Progression via Multisource Multitask Learners: A Case Study With Alzheimer's Disease.

IEEE transactions on neural networks and learning systems
Understanding the progression of chronic diseases can empower the sufferers in taking proactive care. To predict the disease status in the future time points, various machine learning approaches have been proposed. However, a few of them jointly cons...

Detecting false positive sequence homology: a machine learning approach.

BMC bioinformatics
BACKGROUND: Accurate detection of homologous relationships of biological sequences (DNA or amino acid) amongst organisms is an important and often difficult task that is essential to various evolutionary studies, ranging from building phylogenies to ...

Minimum Entropy Rate Simplification of Stochastic Processes.

IEEE transactions on pattern analysis and machine intelligence
We propose minimum entropy rate simplification (MERS), an information-theoretic, parameterization-independent framework for simplifying generative models of stochastic processes. Applications include improving model quality for sampling tasks by conc...

A Novel Sparse Dictionary Learning Separation (SDLS) Model With Adaptive Dictionary Mutual Incoherence Constraint for fMRI Data Analysis.

IEEE transactions on bio-medical engineering
OBJECTIVE: Many studies have shown that the independence assumption in the widely-used ICAs is not adaptive enough for brain functional networks (BFN) detection due to the complex brain hemodynamics, functional integration, artifacts embedded in func...

Temporal data representation, normalization, extraction, and reasoning: A review from clinical domain.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: We live our lives by the calendar and the clock, but time is also an abstraction, even an illusion. The sense of time can be both domain-specific and complex, and is often left implicit, requiring significant domain knowledg...

User Adaptive Text Predictor for Mentally Disabled Huntington's Patients.

Computational intelligence and neuroscience
This paper describes in detail the design of the specialized text predictor for patients with Huntington's disease. The main aim of the specialized text predictor is to improve the text input rate by limiting the phrases that the user can type in. We...

Mixed Integer Linear Programming based machine learning approach identifies regulators of telomerase in yeast.

Nucleic acids research
Understanding telomere length maintenance mechanisms is central in cancer biology as their dysregulation is one of the hallmarks for immortalization of cancer cells. Important for this well-balanced control is the transcriptional regulation of the te...

Evaluation of a machine learning capability for a clinical decision support system to enhance antimicrobial stewardship programs.

Artificial intelligence in medicine
OBJECTIVE: Antimicrobial stewardship programs have been shown to limit the inappropriate use of antimicrobials. Hospitals are increasingly relying on clinical decision support systems to assist in the demanding prescription reviewing process. In prev...

A Machine Learning Framework for Plan Payment Risk Adjustment.

Health services research
OBJECTIVE: To introduce cross-validation and a nonparametric machine learning framework for plan payment risk adjustment and then assess whether they have the potential to improve risk adjustment.