title: Multi-label classification of chronically ill patients with bag of words and supervised dimensionality reduction algorithms.
abstract: OBJECTIVE
  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.
authors.affiliation: University of Applied Sciences Western Switzerland, Institute of Business Information Systems, TechnoArk 3, CH-3960 Sierre, Switzerland. Electronic address: stefano.bromuri@hevs.ch.
authors.forename: Stefano
authors.initials: S
authors.lastname: Bromuri
authors.affiliation: University of Applied Sciences Western Switzerland, Institute of Business Information Systems, TechnoArk 3, CH-3960 Sierre, Switzerland.
authors.forename: Damien
authors.initials: D
authors.lastname: Zufferey
authors.affiliation: University of Applied Sciences Western Switzerland, Institute of Information and Communication Technologies, Bd de Pérolles 80, CH-1705 Fribourg, Switzerland.
authors.forename: Jean
authors.initials: J
authors.lastname: Hennebert
authors.affiliation: University of Applied Sciences Western Switzerland, Institute of Business Information Systems, TechnoArk 3, CH-3960 Sierre, Switzerland.
authors.forename: Michael
authors.initials: M
authors.lastname: Schumacher
mesh_terms: D001185:Artificial Intelligence; D002908:Chronic Disease / Q000145:classification*; D044962:Data Compression / Q000379:methods*; D057225:Data Mining / Q000379:methods; D003924:Diabetes Mellitus, Type 2 / Q000145:classification*; D057286:Electronic Health Records / Q000145:classification*; D006801:Humans; D009323:Natural Language Processing; D018875:Vocabulary, Controlled
keywords: Clinical data; Complex patient; Diabetes type 2; Dimensionality reduction; Kernel methods; Multi-label classification