Organizations in many domains generate a considerable amount of heterogeneous data every day. Such data can be processed to enhance these organizations’ decisions in real time. However, storing and processing large and varied datasets (known as big data) is challenging to do in real time. In machine learning, streaming feature selection has always been considered a superior technique for selecting the relevant subset features from highly dimensional data and thus reducing learning complexity. In the relevant literature, streaming feature selection refers to the features that arrive consecutively over time; despite a lack of exact figure on the number of features, numbers of instances are well-established. Many scholars in the field have proposed streaming-feature-selection algorithms in attempts to find the proper solution to this problem. This paper presents an exhaustive and methodological introduction of these techniques. This study provides a review of the traditional feature-selection algorithms and then scrutinizes the current algorithms that use streaming feature selection to determine their strengths and weaknesses. The survey also sheds light on the ongoing challenges in big-data research.

There is insufficient evidence on the utility of potassium-binding resins in patients with end-stage renal disease on dialysis. In addition, their poor tolerability raises concerns of patient adherence. We aimed to assess the efficacy of calcium resonium and investigate the impact of counseling on adherence pattern as well as treatment response. Adult patients on hemodialysis receiving calcium resonium were enrolled with a control group not on treatment. Adherence patterns and adverse effects were recorded following patient interviews. Patients were stratified into 28 adherent (A), 42 non-adherent (NA), and 30 controls (C). Patient education was undertaken, and serum potassium levels were evaluated for 3 months pre-and post-counseling with inter-and intra-group comparison. A statistically significant difference was observed between potassium levels at baseline in A and NA groups but not post-education, which was related to worsening control in former and not due to improvement in NA patients. The poor effectiveness of calcium resonium in the control of hyperkalemia was likely related to non-compliance due to gastrointestinal (GI) intolerability. Dietary indiscretions as well as lack of consistent use of cathartics may have also contributed. No difference in dialysis adequacy was noted among groups, although the contribution of residual renal function was not assessed. These findings raise concern regarding cost-efficacy of this medication and lend credence to investing in traditional measures in hyperkalemia management, namely dietary compliance and adequate dialysis. Further long-term trials are awaited to better define the role of calcium resonium in the dialysis setting.

A huge amount of medical data is generated every day, which presents a challenge in analysing these data. The obvious solution to this challenge is to reduce the amount of data without information loss. Dimension reduction is considered the most popular approach for reducing data size and also to reduce noise and redundancies in data. In this paper, we investigate the effect of feature selection in improving the prediction of patient deterioration in ICUs. We consider lab tests as features. Thus, choosing a subset of features would mean choosing the most important lab tests to perform. If the number of tests can be reduced by identifying the most important tests, then we could also identify the redundant tests. By omitting the redundant tests, observation time could be reduced and early treatment could be provided to avoid the risk. Additionally, unnecessary monetary cost would be avoided. Our approach uses state-ofthe-art feature selection for predicting ICU patient deterioration using the medical lab results. We apply our technique on the publicly available MIMIC-II database and show the effectiveness of the feature selection. We also provide a detailed analysis of the best features identified by our approach.

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Large amount of heterogeneous medical data is generated every day in various healthcare organizations. Those data could derive insights for improving monitoring and care delivery in the Intensive Care Unit. Conversely, these data presents a challenge in reducing this amount of data without information loss. Dimension reduction is considered the most popular approach for reducing data size and also to reduce noise and redundancies in data. In this paper, we are investigate the effect of the average laboratory test value and number of total laboratory in predicting patient deterioration in the Intensive Care Unit, where we consider laboratory tests as features. Choosing a subset of features would mean choosing the most important lab tests to perform. Thus, our approach uses state-of-the-art feature selection to identify the most discriminative attributes, where we would have a better understanding of patient deterioration problem. If the number of tests can be reduced by identifying the most important tests, then we could also identify the redundant tests. By omitting the redundant tests, observation time could be reduced and early treatment could be provided to avoid the risk. Additionally, unnecessary monetary cost would be avoided. We apply our technique on the publicly available MIMIC-II database and show the effectiveness of the feature selection. We also provide a detailed analysis of the best features identified by our approach.