Data Mining Approach in Preterm Birth Prediction

Data mining refers to the process of discovering patterns in data, typically with the aid of powerful algorithms to automate part of the search. These methods come from the disciplines such as statistics, machine learning, pattern recognition, neural networks and database. In particular this paper reveals out how the problem of preterm birth prediction is approached by a data mining analyst with a background in machine learning. In the health field, data mining applications have been growing considerably as it can be used to directly derive patterns, which are relevant to forecast different risk groups among the patients. Data mining technique such as clustering has not been used to predict preterm birth. Hence this paper made an attempt to identify patterns from the database of the preterm birth patients using clustering

Machine Learning Approach for Preterm Birth Prediction Using Health Records: Systematic Review

Background: Preterm birth (PTB), a common pregnancy complication, is responsible for 35% of the 3.1 million pregnancy-related deaths each year and significantly affects around 15 million children annually worldwide. Conventional approaches to predict PTB lack reliable predictive power, leaving >50% of cases undetected. Recently, machine learning (ML) models have shown potential as an appropriate complementary approach for PTB prediction using health records (HRs).Objective: This study aimed to systematically review the literature concerned with PTB prediction using HR data and the ML approach.Methods: This systematic review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. A comprehensive search was performed in 7 bibliographic databases until May 15, 2021. The quality of the studies was assessed, and descriptive information, including descriptive characteristics of the data, ML modeling processes, and model performance, was extracted and reported.Results: A total of 732 papers were screened through title and abstract. Of these 732 studies, 23 (3.1%) were screened by full text, resulting in 13 (1.8%) papers that met the inclusion criteria. The sample size varied from a minimum value of 274 to a maximum of 1,400,000. The time length for which data were extracted varied from 1 to 11 years, and the oldest and newest data were related to 1988 and 2018, respectively. Population, data set, and ML models’ characteristics were assessed, and the performance of the model was often reported based on metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve.Conclusions: Various ML models used for different HR data indicated potential for PTB prediction. However, evaluation metrics, software and package used, data size and type, selected features, and importantly data management method often remain unjustified, threatening the reliability, performance, and internal or external validity of the model. To understand the usefulness of ML in covering the existing gap, future studies are also suggested to compare it with a conventional method on the same data set.</p

Angariação e retenção de clientes numa maternidade privada

Este trabalho apresenta o caso de um prestador de saúde privado, com maternidade, da zona da grande Lisboa, cujo número de partos tem vindo a decrescer. Trabalhou-se um conjunto de dados da especialidade de Ginecologia/Obstetrícia (GIN/OBS), a partir do qual se construiu uma metodologia de análise inovadora na aplicação de Customer Relationship Management (CRM) a esta especialidade, e que permite extrair conhecimento útil sobre o seu comportamento. A criação de perfis de utente, através da construção de métricas agregadas, permitiu aferir condicionantes do negócio, como a utilização de Entidades Financiadoras de Referência (EFR’s) e o desempenho de médicos em número de partos, a georreferenciação de utentes, e a segmentação de clientes por valor. Este conhecimento, em conjunto com dados da literatura e da análise do mercado das maternidades privadas, permitiu definir diretrizes de atuação de marketing que podem ser aplicáveis a vários níveis da organização, visando o aumento da quota de mercado de partos do prestador. Organizações de saúde que sigam esta metodologia poderão conhecer melhor os seus clientes, criando uma estratégia de CRM, com vista ao aumento do número de partos