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

Tópicos

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Resumo

JECTIVE: This research is motivated by the issue of classifying illnesses of chronically ill patients for decision support in clinical settings. Our main objective is to propose multi-label classification of multivariate time series contained in medical records of chronically ill patients, by means of quantization methods, such as bag of words (BoW), and multi-label classification algorithms. Our second objective is to compare supervised dimensionality reduction techniques to state-of-the-art multi-label classification algorithms. The hypothesis is that kernel methods and locality preserving projections make such algorithms good candidates to study multi-label medical time series.METHODS: We combine BoW and supervised dimensionality reduction algorithms to perform multi-label classification on health records of chronically ill patients. The considered algorithms are compared with state-of-the-art multi-label classifiers in two real world datasets. Portavita dataset contains 525 diabetes type 2 (DT2) patients, with co-morbidities of DT2 such as hypertension, dyslipidemia, and microvascular or macrovascular issues. MIMIC II dataset contains 2635 patients affected by thyroid disease, diabetes mellitus, lipoid metabolism disease, fluid electrolyte disease, hypertensive disease, thrombosis, hypotension, chronic obstructive pulmonary disease (COPD), liver disease and kidney disease. The algorithms are evaluated using multi-label evaluation metrics such as hamming loss, one error, coverage, ranking loss, and average precision.RESULTS: Non-linear dimensionality reduction approaches behave well on medical time series quantized using the BoW algorithm, with results comparable to state-of-the-art multi-label classification algorithms. Chaining the projected features has a positive impact on the performance of the algorithm with respect to pure binary relevance approaches.CONCLUSIONS: 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.

Resumo Limpo

jectiv research motiv issu classifi ill chronic ill patient decis support clinic set main object propos multilabel classif multivari time seri contain medic record chronic ill patient mean quantiz method bag word bow multilabel classif algorithm second object compar supervis dimension reduct techniqu stateoftheart multilabel classif algorithm hypothesi kernel method local preserv project make algorithm good candid studi multilabel medic time seriesmethod combin bow supervis dimension reduct algorithm perform multilabel classif health record chronic ill patient consid algorithm compar stateoftheart multilabel classifi two real world dataset portavita dataset contain diabet type dt patient comorbid dt hypertens dyslipidemia microvascular macrovascular issu mimic ii dataset contain patient affect thyroid diseas diabet mellitus lipoid metabol diseas fluid electrolyt diseas hypertens diseas thrombosi hypotens chronic obstruct pulmonari diseas copd liver diseas kidney diseas algorithm evalu use multilabel evalu metric ham loss one error coverag rank loss averag precisionresult nonlinear dimension reduct approach behav well medic time seri quantiz use bow algorithm result compar stateoftheart multilabel classif algorithm chain project featur posit impact perform algorithm respect pure binari relev approachesconclus evalu highlight feasibl repres medic health record use bow multilabel classif task studi also highlight dimension reduct algorithm base kernel method local preserv project good candid deal multilabel classif task medic time seri mani miss valu high label densiti

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