000			04057nam a22004937a 4500
008			240807s20242024 xxu||||| |||| 00| 0 eng d
022			_a1472-6947
024			_a10.1186/s12911-024-02566-4 [pii]
024			_aPMC11170878 [pmc]
040			_aOvid MEDLINE(R)
099			_a38872146
245			_aSurprising and novel multivariate sequential patterns using odds ratio for temporal evolution in healthcare.
251			_aBMC Medical Informatics & Decision Making. 24(1):165, 2024 Jun 13.
252			_aBMC Med Inf Decis Mak. 24(1):165, 2024 Jun 13.
253			_aBMC medical informatics and decision making
260			_c2024
260			_fFY2024
260			_p2024 Jun 13
265			_sepublish
265			_tMEDLINE
266			_d2024-08-07
266			_z2024/06/13 23:35
501			_aAvailable online from MWHC library: 2001 - present
520			_aBACKGROUND: Pattern mining techniques are helpful tools when extracting new knowledge in real practice, but the overwhelming number of patterns is still a limiting factor in the health-care domain. Current efforts concerning the definition of measures of interest for patterns are focused on reducing the number of patterns and quantifying their relevance (utility/usefulness). However, although the temporal dimension plays a key role in medical records, few efforts have been made to extract temporal knowledge about the patient's evolution from multivariate sequential patterns.
520			_aCONCLUSIONS: Our proposed method with which to extract JDORSP generates a set of interpretable multivariate sequential patterns with new knowledge regarding the temporal evolution of the patients. The number of patterns is greatly reduced when compared to those generated by other methods and measures of interest. An additional advantage of this method is that it does not require any parameters or thresholds, and that the reduced number of patterns allows a manual evaluation. Copyright © 2024. The Author(s).
520			_aMETHODS: In this paper, we propose a method to extract a new type of patterns in the clinical domain called Jumping Diagnostic Odds Ratio Sequential Patterns (JDORSP). The aim of this method is to employ the odds ratio to identify a concise set of sequential patterns that represent a patient's state with a statistically significant protection factor (i.e., a pattern associated with patients that survive) and those extensions whose evolution suddenly changes the patient's clinical state, thus making the sequential patterns a statistically significant risk factor (i.e., a pattern associated with patients that do not survive), or vice versa.
520			_aRESULTS: The results of our experiments highlight that our method reduces the number of sequential patterns obtained with state-of-the-art pattern reduction methods by over 95%. Only by achieving this drastic reduction can medical experts carry out a comprehensive clinical evaluation of the patterns that might be considered medical knowledge regarding the temporal evolution of the patients. We have evaluated the surprisingness and relevance of the sequential patterns with clinicians, and the most interesting fact is the high surprisingness of the extensions of the patterns that become a protection factor, that is, the patients that recover after several days of being at high risk of dying.
546			_aEnglish
650			_a*Data Mining
650			_aData Mining/mt [Methods]
650			_aDelivery of Health Care
650			_aElectronic Health Records
650			_aHumans
650			_aOdds Ratio
650			_aPattern Recognition, Automated
650			_aTime Factors
650			_zAutomated
651			_aMedStar Heart & Vascular Institute
657			_aJournal Article
700			_aLorente-Ros, Marta _bMHVI
790			_aCasanova IJ, Campos M, Juarez JM, Gomariz A, Canovas-Segura B, Lorente-Ros M, Lorente JA
856			_uhttps://dx.doi.org/10.1186/s12911-024-02566-4 _zhttps://dx.doi.org/10.1186/s12911-024-02566-4
942			_cART _dArticle
999			_c14586 _d14586