Endocrinology in Crisis?

Ghably, Jack G.; Paterson, Barbara J.; Peiris, Alan N.

doi:10.1097/smj.0b013e31828d96f3

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…On the other side, such aggregation will become (or are already) unmanageable for humans. In addition, physicians are frequently in charge of hundreds of patients, as reported in [8]. Therefore, there is a need for stateof-the-art data-mining and machine learning tools to assist physicians by providing aggregated information about their patients.…”

Section: Introductionmentioning

confidence: 98%

Performance comparison of multi-label learning algorithms on clinical data for chronic diseases

Zufferey

Hofer

Hennebert

et al. 2015

Computers in Biology and Medicine

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We are motivated by the issue of classifying diseases of chronically ill patients to assist physicians in their everyday work. Our goal is to provide a performance comparison of state-of-the-art multi-label learning algorithms for the analysis of multivariate sequential clinical data from medical records of patients affected by chronic diseases. As a matter of fact, the multi-label learning approach appears to be a good candidate for modeling overlapped medical conditions, specific to chronically ill patients. With the availability of such comparison study, the evaluation of new algorithms should be enhanced. According to the method, we choose a summary statistics approach for the processing of the sequential clinical data, so that the extracted features maintain an interpretable link to their corresponding medical records. The publicly available MIMIC-II dataset, which contains more than 19,000 patients with chronic diseases, is used in this study. For the comparison we selected the following multi-label algorithms: ML-kNN, AdaBoostMH, binary relevance, classifier chains, HOMER and RAkEL. Regarding the results, binary relevance approaches, despite their elementary design and their independence assumption concerning the chronic illnesses, perform optimally in most scenarios, in particular for the detection of relevant diseases. In addition, binary relevance approaches scale up to large dataset and are easy to learn. However, the RAkEL algorithm, despite its scalability problems when it is confronted to large dataset, performs well in the scenario which consists of the ranking of the labels according to the dominant disease of the patient.

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Section: Introductionmentioning

confidence: 98%

Performance comparison of multi-label learning algorithms on clinical data for chronic diseases

Zufferey

Hofer

Hennebert

et al. 2015

Computers in Biology and Medicine

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Multi-label classification of chronically ill patients with bag of words and supervised dimensionality reduction algorithms

Bromuri

Zufferey

Hennebert

et al. 2014

Journal of Biomedical Informatics

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The evaluation highlights the feasibility of representing medical health records using the BoW for multi-label classification tasks. The study also highlights that dimensionality reduction algorithms based on kernel methods, locality preserving projections or both are good candidates to deal with multi-label classification tasks in medical time series with many missing values and high label density.

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Endocrinology in Crisis?

Cited by 2 publications

References 2 publications

Performance comparison of multi-label learning algorithms on clinical data for chronic diseases

Performance comparison of multi-label learning algorithms on clinical data for chronic diseases

Multi-label classification of chronically ill patients with bag of words and supervised dimensionality reduction algorithms

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