Semi-supervised K-means Clustering Research Articles

Mortality prediction on unsupervised and semi-supervised clusters of medical intensive care unit patients based on MIMIC-II database

IntroductionThe study aimed to propose a framework for identifying patient clusters in medical intensive care units (MICUs) based on the Medical Information Mart for Intensive Care II (MIMIC-II) database. The suggested framework makes use of the survival outcomes and physiological information available in the dataset and is hence called a semi-supervised approach. Five neural networks were trained on the clusters identified using the proposed approach to determine whether the proposed framework could improve the predictive accuracy of the deep learning models. MethodsThis study utilized data from the MIMIC-II database, which is a publicly available database that contains information on patients admitted to intensive care units. The clusters underlying the MICU patient population were identified using unsupervised and semi-supervised K-means clustering. Mortality in the resulting clusters was predicted using five deep learning-based survival models and the performance of these models was compared using two metrics. ResultsThree clusters (cluster 1, n = 1304; cluster 2, n = 474; cluster 3, n = 1079) were identified using unsupervised K-means, and another three clusters (cluster 1, n = 479; cluster 2, n = 1492; cluster 3, n = 886) were identified using semi-supervised K-means clustering. Experimental results demonstrate that, in general, the performance of deep learning models was better on semi-supervised clusters obtained by combining Cox proportional hazards (Cox-PH) model-based feature selection and K-means compared to unsupervised clusters. ConclusionsIn the present study, it was observed that deep learning-based survival models tend to perform better on clusters that are identified in a semi-supervised fashion. This approach helps to extract more meaningful patterns and associations between different clinical features and patient outcomes.

The Research on Clustering Ensembles Selection Algorithm based on Semi-supervised K-means Clustering

Selective clustering ensemble algorithm can eliminate the inferior quality clustering member’s influence and can achieve a better clustering solution relative to the clustering ensemble algorithm. For high dimensional data clustering, in this paper, a novel selective ensemble algorithm based on semi-supervised K-means clustering is proposed. In this paper, through a large number of experiments to verify the validity of the proposed algorithm for dealing with high dimensional data clustering. The new algorithm can achieve statistically significant performance improvement over other clustering algorithms.

Adaptive virtual referencing for the extraction of extracellularly recorded action potentials in noisy environments

Objective. Removal of common mode noise and artifacts from extracellularly measured action potentials, herein referred to as spikes, recorded with multi-electrode arrays (MEAs) which included severe noise and artifacts generated by an ultrahigh field (UHF) 16.4 Tesla magnetic resonance imaging (MRI) scanner. Approach. An adaptive virtual referencing (AVR) algorithm is used to remove artifacts and thus enable extraction of neural spike signals from extracellular recordings in anesthetized rat brains. A 16-channel MEA with 150-micron inter-site spacing is used, and a virtual reference is created by spatially averaging the 16 signal channels which results in a reference signal without extracellular spiking activity while preserving common mode noise and artifacts. This virtual reference signal is then used as the input to an adaptive FIR filter which optimally scales and time-shifts the reference to each specific electrode site to remove the artifacts and noise. Main results. By removing artifacts and reducing noise, the neural spikes at each electrode site can be well extracted, even from data originally recorded with a high noise floor due to electromagnetic interference and artifacts generated by a 16.4T MRI scanner. The AVR method enables many more spikes to be detected than would otherwise be possible. Further, the filtered spike waveforms can be well separated from each other using PCA feature extraction and semi-supervised k-means clustering. While data in a 16.4T MRI scanner contains significantly more noise and artifacts, the developed AVR method enables similar data quality to be extracted as recorded on benchtop experiments outside the MRI scanner. Significance. AVR of extracellular spike signals recorded with MEAs has not been previously reported and fills a technical need by enabling low-noise extracellular spike extraction in noisy and challenging environments such as UHF MRI that will enable further study of neuro-vascular coupling at UHF.

Probabilistic framework of visual anomaly detection for unbalanced data

This paper proposes a novel probabilistic detection framework of weighted combining semi-supervised k-means clustering and Posterior Probability SVM (PPSVM) for unbalanced data based on robot vision. Within the framework, an algorithm for learning synchronously the k in k-means and features is introduced based on hybrid wrapper and filter criterion. Then the optimal hierarchical probabilistic model by combining k-means and PPSVM is used to anomaly detection so as to alleviate the problems of imbalanced data with small samples, improve the detection accuracy, and deal with the difficult problem of defining the anomaly classes. The other contributions of our approach include the following three aspects: (1) it classifies anomaly candidates by using their class probability distributions rather than the direct extracted features; (2) the relevant classes are automatically built by learning the samples׳ multimodal Gaussian distribution; and (3) the cost-sensitive idea and filter criterion are integrated in learning k and features via cost function of Tabu search. Experimental results on real-world data sets show the proposed approach obtains a satisfactory detection performance within limited time in inspecting the condition of Heating, and Ventilation and Air-Conditioning (HVAC) ductwork.