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Clustering-based Approach for Categorizing Pregnant Women in Obstetrics and Maternity Care

Published: 13 July 2015 Publication History

Abstract

When a pregnant woman is guided to a hospital for obstetrics purposes, many outcomes are possible, depending on her current conditions. An improved understanding of these conditions could provide a more direct medical approach by categorizing the different types of patients, enabling a faster response to risk situations, and therefore increasing the quality of services. In this case study, the characteristics of the patients admitted in the maternity care unit of Centro Hospitalar of Porto are acknowledged, allowing categorizing the patient women through clustering techniques. The main goal is to predict the patients' route through the maternity care, adapting the services according to their conditions, providing the best clinical decisions and a cost-effective treatment to patients. The models developed presented very interesting results, being the best clustering evaluation index: 0.65. The evaluation of the clustering algorithms proved the viability of using clustering based data mining models to characterize pregnant patients, identifying which conditions can be used as an alert to prevent the occurrence of medical complications.

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Cited By

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  • (2023)Solutions for Complications in Pregnant WomenPredicting Pregnancy Complications Through Artificial Intelligence and Machine Learning10.4018/978-1-6684-8974-1.ch017(260-275)Online publication date: 30-Jun-2023
  • (2020)Artificial Intelligence in Pregnancy: A Scoping ReviewIEEE Access10.1109/ACCESS.2020.30283338(181450-181484)Online publication date: 2020
  • (2015)Predicting Pre-triage Waiting Time in a Maternity Emergency Room Through Data MiningRevised Selected Papers of the International Conference on Smart Health - Volume 954510.1007/978-3-319-29175-8_10(105-117)Online publication date: 17-Nov-2015

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      cover image ACM Other conferences
      C3S2E '15: Proceedings of the Eighth International C* Conference on Computer Science & Software Engineering
      July 2015
      166 pages
      ISBN:9781450334198
      DOI:10.1145/2790798
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      • Keio University: Keio University
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      Publication History

      Published: 13 July 2015

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      Author Tags

      1. Clustering
      2. Data Mining
      3. Decision Support Systems
      4. Interoperability
      5. Maternity Care
      6. Obstetrics Care
      7. Real data

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      Overall Acceptance Rate 12 of 42 submissions, 29%

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      View all
      • (2023)Solutions for Complications in Pregnant WomenPredicting Pregnancy Complications Through Artificial Intelligence and Machine Learning10.4018/978-1-6684-8974-1.ch017(260-275)Online publication date: 30-Jun-2023
      • (2020)Artificial Intelligence in Pregnancy: A Scoping ReviewIEEE Access10.1109/ACCESS.2020.30283338(181450-181484)Online publication date: 2020
      • (2015)Predicting Pre-triage Waiting Time in a Maternity Emergency Room Through Data MiningRevised Selected Papers of the International Conference on Smart Health - Volume 954510.1007/978-3-319-29175-8_10(105-117)Online publication date: 17-Nov-2015

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