A Decision-Support System for Expecting Mothers and Obstetricians

Martirosyan, Hasmik; Frize, Monique; Ong, Daphne E.; Gilchrist, Jeff; Bariciak, Erika

doi:10.1007/978-3-319-11128-5_175

Cited by 4 publications

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“…This hybrid machine learning classifier was then further developed by Yu, who used the decision tree classifier to eliminate variables and then applied an artificial neural network with weightelimination, with improved sensitivity and specificity results [12]. Finally, Ong's work introduced a new neural network classifier using the Fast-Artificial Neural Network Library [13].…”

Section: This Thesis Represents Continuation Of Work Done By Previousmentioning

confidence: 99%

“…Options include centering the data to have a mean of 0 or scaling the data by the standard deviation [64]. Past work has shown that the neural network works best when normalized between the range of [-1,1], [13]. The normalization process will be expanded on in greater detail in Chapter 3.…”

Section: Normalizationmentioning

confidence: 99%

“…Another script was created: The Performance Measures Calculation Tool [13] for outputting the performance metrics of the best network. These metrics include the Positive Predictive Value The ANN Model Selection Tool [13] is a script which was used to select the best performing network based on the highest sensitivity. This selection script is applied to 10 result files (after 5-by-2 cross validation) and sorts through to find the final network with the highest sensitivity, within the verification and test sets.…”

Section: Step 8: Execution Of the Ann Buildermentioning

confidence: 99%

“…The results from applying the BASH file ./create_5_by_2.sh are displayed in the below Figure 4.6. Once these files have been created, then the training of the neural networks using the ANN builder [13] can commence.…”

Section: Step 7: Divide Into Test Train and Verification Setsmentioning

confidence: 99%

“…Therefore, two options needed to be investigated: Balance the data (using the ubBalance package in R), impute the missing values (using the missForest package in R), and then apply the C5.0 DT classifier for feature reduction Apply the C5.0 DT classifier for feature reduction with the missing values present in BORN and PRAMS, and then carry out the preprocessing steps (balance the data and fill in missing values)The results are summarized in the next chapter. The first step in creating the C5.0 DT classifiers was to modify a file called "mortality.names" this file contained information about the features and classes (files are labeled as mortality/nonmortality throughout this research, due to past work done by Hasmik on the ANN Builder[13], her work was focused on neonatal mortality risk estimation models using Artificial Neural Networks) In the mortality.names file the OUTCOME feature represented the target attribute, the CASE ID was the label attribute and the rest of the features in: PRAMS_Parous, PRAMS_Nulliparous, BORN_Parous or BORN_Nulliparous were defined to be continuous (numeric) or discrete (nominal). The next step was to create two csv files, one labeled mortality.csv and the other labeled nonmortality.csv.…”

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confidence: 99%

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Applying Data Preparation Methods to Optimize Preterm Birth Prediction

Esty¹

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The purpose of this work was to develop an accurate prediction model which can process information contained in antenatal databases to determine whether a baby will be born prematurely. The focus was on improved data preprocessing to add to methods developed by previous students in the Carleton MIRG (Medical Information technology Research Group) lab.The machine learning classifiers used included Decision Tree (DT) classifiers (for feature reduction) and the Artificial Neural Network (ANN) classifier (for model evaluation).Missing values and class imbalance was dealt with by applying software packages in the R statistical programming language.This research has shown a marked improvement in the accuracy of predicting preterm births.The final sensitivity and specificity results for the BORN (Better Outcomes Registry and Network) database were: Parous 89.2%, and 67.8%, Nulliparous 89.0% and 71.5%, and for PRAMS (Pregnancy Risk Assessment Monitoring System) database: Parous 84.1% and 71.4%, Nulliparous 83.8% and 76.0%. These improved results are promising. An accurate predictive tool will allow caregivers to implement preventative treatment strategies or to ensure delivery occurs in a tertiary health care Centre.iii

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Section: This Thesis Represents Continuation Of Work Done By Previousmentioning

confidence: 99%

Section: Normalizationmentioning

confidence: 99%

Section: Step 8: Execution Of the Ann Buildermentioning

confidence: 99%

Section: Step 7: Divide Into Test Train and Verification Setsmentioning

confidence: 99%

mentioning

confidence: 99%

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Applying Data Preparation Methods to Optimize Preterm Birth Prediction

Esty¹

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On Code Refactoring for Decision Making Component Combined with the Open-Source Medical Information System

Martsenyuk

Semenets

2018

Advances in Soft and Hard Computing

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РЕФАКТОРІНГ ПРОГРАМНОГО КОДУ ДІАЛОГОВОГО КОМПОНЕНТУ ПЛАТФОРМИ СИСТЕМИ ПІДТРИМКИ ПРИЙНЯТТЯ РІШЕННЯ ДЛЯ МЕДИЧНОЇ ІНФОРМАЦІЙНОЇ СИСТЕМИ З ВІДКРИТИМ КОДОМ OpenMRS

Semenets

Martsenyuk

2016

MIE

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The importance of Medical Information Systems (MIS) for medical practice is emphasized. The wide usage of the Electronic Medical Records (EMR) software is displayed. The importance and alternative approaches to implementation of the MIS in the Ukraine healthcare system are discussed. The benefits of the open-source MIS usage are shown. Effectiveness of the Clinical Decision Support System (CDSS) application in the medical decision making process is emphasized.<p>The open-source MIS OpenMRS developer tools and software API are reviewed. The results of code refactoring of the dialog subsystem of the CDSS platform which is made as module for the open-source MIS OpenMRS are presented. The structure of information model of database of the CDSS dialog subsystem was updated according with MIS OpenMRS requirements. The Model-View-Controller (MVC) based approach to the CDSS dialog subsystem architecture was re-implemented with Java programming language using Spring and Hibernate frameworks. The MIS OpenMRS Encounter portlet form for the CDSS dialog subsystem integration is developed as an extension. The administrative module of the CDSS platform is recreated. The data exchanging formats and methods for interaction of OpenMRS CDSS dialog subsystem module and DecisionTree GAE service are re-implemented with help of AJAX technology via jQuery library</p>

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A Decision-Support System for Expecting Mothers and Obstetricians

Cited by 4 publications

References 0 publications

Applying Data Preparation Methods to Optimize Preterm Birth Prediction

Applying Data Preparation Methods to Optimize Preterm Birth Prediction

On Code Refactoring for Decision Making Component Combined with the Open-Source Medical Information System

РЕФАКТОРІНГ ПРОГРАМНОГО КОДУ ДІАЛОГОВОГО КОМПОНЕНТУ ПЛАТФОРМИ СИСТЕМИ ПІДТРИМКИ ПРИЙНЯТТЯ РІШЕННЯ ДЛЯ МЕДИЧНОЇ ІНФОРМАЦІЙНОЇ СИСТЕМИ З ВІДКРИТИМ КОДОМ OpenMRS

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