A web-based perinatal decision support system framework using a knowledge-based-approach to estimate clinical outcomes : neonatal mortality and preterm birth in twins pregnancies

Gunaratnam, Marry

doi:10.22215/etd/2013-06551

Cited by 3 publications

(10 citation statements)

References 27 publications

(97 reference statements)

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“…All sensitivity and specificity values showed a near perfect performance. To be considered as clinically useful a Sensitivity of 65% and Specificity of 85% must be met as recommended by our clinician partner, reported by Gunaratnam (2013). Collectively these values meet and surpass the performance measures reported.…”

Section: Ann Resultsmentioning

confidence: 80%

“…For the BORN verification sets, the outcomes of results from both methods for each set were similar to one another (with Method 2 performing only slightly better than Method 1 overall). To be considered as clinically useful a Sensitivity of 65% and Specificity of 85% must be met as recommended by our clinician partner, reported by Gunaratnam (2013). The specificity values do meet the requirement, however, collectively these values do not meet the performance measures reported.…”

Section: Hold Out Verification Resultsmentioning

confidence: 96%

“…To be considered as clinically useful a Sensitivity of 65% and Specificity of 85% must be met as recommended by our clinician partner, reported by Gunaratnam (2013).…”

Section: Ann Resultsmentioning

confidence: 99%

“…Analysis strategies to compare the performance obtained with various dataset are also discussed. This thesis introduces a new method of feature selection, not previously applied in the works of Catley (Catley, 2007), Yu, (Yu, 2009) and Gunaratnam (Gunaratnam, 2013).…”

Section: Chapter: Methodologymentioning

confidence: 99%

“…The difficulties with predicting preterm birth are largely due to the difficulties classifying it; preterm birth is a multifactorial problem. Different ways to assess this have been outlined by Yu and Gunaratnam, which show the differences in classification, and thus further the difficulties of assessment (Yu 2009, andGunaratnam, 2013 before the completion of 37-week gestational period. An ideal term birth occurs between 37-41 weeks of gestation and post term birth occurs with more than 41 weeks gestation.…”

Section: Preterm Birth Classificationmentioning

confidence: 99%

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An Integrated Machine Learning Approach to Optimize the Estimation of Preterm Birth

Ong¹

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This thesis describes a new methodology used in conjunction with artificial intelligence tools to create multiple models for prediction of preterm birth in obstetrical environments. The data mining approach integrates: Decision Trees (DTs), Artificial Neural Networks (ANNs) -specifically a Feed Forward Back Propagation ANN, and Case Based Reasoning System (CBRS).This work also introduces a 5by2 cross validation method, assesses two methods of attribute selection, and considers data prevalence (15% and 8.1%) in training and testing networks. Two databases were assessed from two countries: BORN (Canada) and PRAMS (USA). Best BORN results used selection method 2, had sensitivities of 50.53%, 53.96%, specificities of 91.61%, 95.40%, and area under curves (AUC) of 0.7721, 0.7970 for Parous and Nulliparous cases respectively. Best PRAMS results used selection method 1, had sensitivities of 68.15%, 40.35%, specificities of 64.71%, 94.57%, and area under curves (AUC) of 0.8452, 0.7064 for Parous and Nulliparous cases respectively. iii Above all, challenge yourself. You may well surprise yourself at what strengths you have, what you can accomplish. -Cecile Springer iv AcknowledgementsFirst and foremost, I would like to take this opportunity to thank my thesis supervisor, Dr. Monique Frize, for her support, encouragement and guidance. I am very grateful to have had the opportunity to work with you.

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Section: Ann Resultsmentioning

confidence: 80%

Section: Hold Out Verification Resultsmentioning

confidence: 96%

“…To be considered as clinically useful a Sensitivity of 65% and Specificity of 85% must be met as recommended by our clinician partner, reported by Gunaratnam (2013).…”

Section: Ann Resultsmentioning

confidence: 99%

Section: Chapter: Methodologymentioning

confidence: 99%

Section: Preterm Birth Classificationmentioning

confidence: 99%

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An Integrated Machine Learning Approach to Optimize the Estimation of Preterm Birth

Ong¹

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Framework for Knowledge–Based Intelligent Clinical Decisionsupport to Predict Comorbidity

Onuiri¹,

Awodele²,

Idowu³

2014

IJARAI

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Research in medicine has shown that comorbidity is prevalent among chronic diseases. In ophthalmology, it is used to refer to the overlap of two or more ophthalmic disorders. The comorbidity of cataract and glaucoma has continued to gain increasing prominence in ophthalmology within the past few decades and poses a major concern to practitioners. The situation is made worse by the dearth in number of ophthalmologists in Nigeria vis-à-vis Sub-Saharan Africa, making it most inevitable that patients will find themselves more at the mercies of General Practitioners (GPs) who are not experts in this domain of interest. To stem the tide, we designed a framework that adopts a knowledge-based Clinical Decision Support System (CDSS) approach to deal with predicting ophthalmic comorbidity as well as the generation of patient-specific care plans at the point of care. This research which is within the domain of medical/healthcare informatics was carried out through an indepth understanding of the intricacies associated with knowledge representation/preprocessing of relevant domain knowledge. Furthermore, we present the Comorbidity Ontological Framework for Intelligent Prediction (COFIP) in which Artificial Neural Network and Decision Trees, both being mechanisms of Artificial Intelligence (AI) was embedded into the framework togive it an intelligent (predictive and adaptive) capability. This framework provides the platform for a CDSS that is diagnostic, predictive and preventive. This is because the framework was designed to predict with satisfactory accuracy, the tendency of a patient with either of cataract or glaucoma to degenerate into a state comorbidity. Furthermore, because this framework is generic in outlook, it can be adapted for other chronic diseases of interest within the medical informatics research community.

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Physician Knowledge Base: Clinical Decision Support Systems

Kim¹,

Kim

2022

Yonsei Med J

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With the introduction of electronic medical records (EMRs), it has become possible to accumulate massive amounts of qualitative medical data. As such, EMRs have become increasingly used in clinical decision support systems (CDSSs). While CDSSs aim to reduce medical errors normally occurring in the process of treating patients by physicians, technical maturity and the completeness of CDSSs do not meet standards for medical use yet. As data further accumulates, CDSS algorithms must be continuously updated to allow CDSSs to perform their core functions. Doing so, however, requires extensive time and manpower investments. In current practice, computational systems already perform a wide variety of functions in medical settings to allow medical staff to focus on other tasks. However, no prior research has evaluated the potential effectiveness of future CDSSs nor analyzed possibilities for their further development. In this article, we evaluate CDSS technology with the consideration that medical staff also understand the core functions of such systems.

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A web-based perinatal decision support system framework using a knowledge-based-approach to estimate clinical outcomes : neonatal mortality and preterm birth in twins pregnancies

Cited by 3 publications

References 27 publications

An Integrated Machine Learning Approach to Optimize the Estimation of Preterm Birth

An Integrated Machine Learning Approach to Optimize the Estimation of Preterm Birth

Framework for Knowledge–Based Intelligent Clinical Decisionsupport to Predict Comorbidity

Physician Knowledge Base: Clinical Decision Support Systems

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