phat 19(19): e3

Research Article

Towards early and automatic detection of Urinary Infection during pregnancy

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  • @ARTICLE{10.4108/eai.13-7-2018.162810,
        author={Lizbeth Escobedo and Ad\^{a}n Hirales-Carbajal},
        title={Towards early and automatic detection of Urinary Infection during pregnancy},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        keywords={Pregnancy health, pervasive health, digitization of healthcare, data analysis, fetal-maternal morbimortality, natural language processing, urinary tract infections},
  • Lizbeth Escobedo
    Adán Hirales-Carbajal
    Year: 2019
    Towards early and automatic detection of Urinary Infection during pregnancy
    DOI: 10.4108/eai.13-7-2018.162810
Lizbeth Escobedo1,*, Adán Hirales-Carbajal1
  • 1: CETYS Universidad, Calz Cetys 813, Lago Sur, Tijuana, B.C. Mexico
*Contact email:


INTRODUCTION: Worldwide Fetal-Maternal morbidity and mortality is frightfully high. Most of these diseases occur in developing countries. One of the main reasons for this problem, after gestational hypertension and complications in childbirth, is infections. Urinary Tract Infections (UTI) during pregnancy is one of the main causes for fetal-maternal morbidity and mortality in Mexico. Among others, the pervasiveness and heterogeneity of data in Electronic Medical Records (EMR) complicates early diagnosis and treatment of UTI.

OBJECTIVES: Our goal is extract empirical knowledge, in the form of association rules, that generalize symptomatology and treatment of UTI patients with positive and nagative diagnosis.

METHODS: In this study, we developed a criterion to extract words and expressions that uniquely characterize each patient class. We extracted association rules from EMRs and evaluated its level of correspondence between the rules and the extracted word sets.

RESULTS: By defining a bound on word frequency usage and evaluating the positive to negative word ratio we were able to identify word sets that uniquely characterize each patient class. A bound of 47 enabled extraction of 25 unique words and expressions for each patient class. Further, approximately 17% and 27% of association rules drew terms from each word set correspondingly.

CONCLUSION: This work seeks to promote the creation of more effective criterions to extract features, from EMRs, that improve characterization of patients and that ultimately lead to a more accurate diagnosis of UTIs.