Multivariate data-driven decision guidance for clinical scientists
- Burstein, Frada, De Silva, Daswin, Jelinek, Herbert, Stranieri, Andrew
- Authors: Burstein, Frada , De Silva, Daswin , Jelinek, Herbert , Stranieri, Andrew
- Date: 2013
- Type: Text , Conference paper
- Relation: 29th International Conference on Data Engineering Workshops, ICDEW 2013; Proceedings - International Conference on Data Engineering p. 193-199
- Full Text:
- Reviewed:
- Description: Clinical decision-support is gaining widespread attention as medical institutions and governing bodies turn towards utilising better information management for effective and efficient healthcare delivery and quality assured outcomes. Amass of data across all stages, from disease diagnosis to palliative care, is further indication of the opportunities and challenges created for effective data management, analysis, prediction and optimization techniques as parts of knowledge management in clinical environments. A Data-driven Decision Guidance Management System (DD-DGMS) architecture can encompass solutions into a single closed-loop integrated platform to empower clinical scientists to seamlessly explore a multivariate data space in search of novel patterns and correlations to inform their research and practice. The paper describes the components of such an architecture, which includes a robust data warehouse as an infrastructure for comprehensive clinical knowledge management. The proposed DD-DGMS architecture incorporates the dynamic dimensional data model as its elemental core. Given the heterogeneous nature of clinical contexts and corresponding data, the dimensional data model presents itself as an adaptive model that facilitates knowledge discovery, distribution and application, which is essential for clinical decision support. The paper reports on a trial of the DD-DGMS system prototype conducted on diabetes screening data which further establishes the relevance of the proposed architecture to a clinical context.
- Description: E1
- Authors: Burstein, Frada , De Silva, Daswin , Jelinek, Herbert , Stranieri, Andrew
- Date: 2013
- Type: Text , Conference paper
- Relation: 29th International Conference on Data Engineering Workshops, ICDEW 2013; Proceedings - International Conference on Data Engineering p. 193-199
- Full Text:
- Reviewed:
- Description: Clinical decision-support is gaining widespread attention as medical institutions and governing bodies turn towards utilising better information management for effective and efficient healthcare delivery and quality assured outcomes. Amass of data across all stages, from disease diagnosis to palliative care, is further indication of the opportunities and challenges created for effective data management, analysis, prediction and optimization techniques as parts of knowledge management in clinical environments. A Data-driven Decision Guidance Management System (DD-DGMS) architecture can encompass solutions into a single closed-loop integrated platform to empower clinical scientists to seamlessly explore a multivariate data space in search of novel patterns and correlations to inform their research and practice. The paper describes the components of such an architecture, which includes a robust data warehouse as an infrastructure for comprehensive clinical knowledge management. The proposed DD-DGMS architecture incorporates the dynamic dimensional data model as its elemental core. Given the heterogeneous nature of clinical contexts and corresponding data, the dimensional data model presents itself as an adaptive model that facilitates knowledge discovery, distribution and application, which is essential for clinical decision support. The paper reports on a trial of the DD-DGMS system prototype conducted on diabetes screening data which further establishes the relevance of the proposed architecture to a clinical context.
- Description: E1
Exploring novel features and decision rules to identify cardiovascular autonomic neuropathy using a hybrid of wrapper-filter based feature selection
- Huda, Shamsul, Jelinek, Herbert, Ray, Biplob, Stranieri, Andrew, Yearwood, John
- Authors: Huda, Shamsul , Jelinek, Herbert , Ray, Biplob , Stranieri, Andrew , Yearwood, John
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at the 2010 6th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2010 p. 297-302
- Full Text:
- Reviewed:
- Description: Cardiovascular autonomic neuropathy (CAN) is one of the important causes of mortality among diabetes patients. Statistics shows that more than 22% of people with type 2 diabetes mellitus suffer from CAN and which in turn leads to cardiovascular disease (heart attack, stroke). Therefore early detection of CAN could reduce the mortality. Traditional method for detection of CAN uses Ewing's algorithm where five noninvasive cardiovascular tests are used. Often for clinician, it is difficult to collect data from for the Ewing Battery patients due to onerous test conditions. In this paper, we propose a hybrid of wrapper-filter approach to find novel features from patients' ECG records and then generate decision rules for the new features for easier detection of CAN. In the proposed feature selection, a hybrid of filter (Maximum Relevance, MR) and wrapper (Artificial Neural Net Input Gain Measurement Approximation ANNIGMA) approaches (MR-ANNIGMA) would be used. The combined heuristics in the hybrid MRANNIGMA takes the advantages of the complementary properties of the both filter and wrapper heuristics and can find significant features. The selected features set are used to generate a new set of rules for detection of CAN. Experiments on real patient records shows that proposed method finds a smaller set of features for detection of CAN than traditional method which are clinically significant and could lead to an easier way to diagnose CAN. © 2010 IEEE.
- Authors: Huda, Shamsul , Jelinek, Herbert , Ray, Biplob , Stranieri, Andrew , Yearwood, John
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at the 2010 6th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2010 p. 297-302
- Full Text:
- Reviewed:
- Description: Cardiovascular autonomic neuropathy (CAN) is one of the important causes of mortality among diabetes patients. Statistics shows that more than 22% of people with type 2 diabetes mellitus suffer from CAN and which in turn leads to cardiovascular disease (heart attack, stroke). Therefore early detection of CAN could reduce the mortality. Traditional method for detection of CAN uses Ewing's algorithm where five noninvasive cardiovascular tests are used. Often for clinician, it is difficult to collect data from for the Ewing Battery patients due to onerous test conditions. In this paper, we propose a hybrid of wrapper-filter approach to find novel features from patients' ECG records and then generate decision rules for the new features for easier detection of CAN. In the proposed feature selection, a hybrid of filter (Maximum Relevance, MR) and wrapper (Artificial Neural Net Input Gain Measurement Approximation ANNIGMA) approaches (MR-ANNIGMA) would be used. The combined heuristics in the hybrid MRANNIGMA takes the advantages of the complementary properties of the both filter and wrapper heuristics and can find significant features. The selected features set are used to generate a new set of rules for detection of CAN. Experiments on real patient records shows that proposed method finds a smaller set of features for detection of CAN than traditional method which are clinically significant and could lead to an easier way to diagnose CAN. © 2010 IEEE.
AWSum - applying data mining in a health care scenario
- Quinn, Anthony, Jelinek, Herbert, Stranieri, Andrew, Yearwood, John
- Authors: Quinn, Anthony , Jelinek, Herbert , Stranieri, Andrew , Yearwood, John
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2008, Sydney, New South Wales : 15th-18th December 2008 p. 291-296
- Full Text:
- Description: This paper investigates the application of a new data mining algorithm called Automated Weighted Sum, (AWSum), to diabetes screening data to explore its use in providing researchers with new insight into the disease and secondarily to explore the potential the algorithm has for the generation of prognostic models for clinical use. There are many data mining classifiers that produce high levels of predictive accuracy but their application to health research and clinical applications is limited because they are complex, produce results that are difficult to interpret and are difficult to integrate with current knowledge and practises. This is because most focus on accuracy at the expense of informing the user as to the influences that lead to their classification results. By providing this information on influences a researcher can be pointed to new potentially interesting avenues for investigation. AWSum measures influence by calculating a weight for each feature value that represents its influence on a class value relative to other class values. The results produced, although on limited data, indicated the approach has potential uses for research and has some characteristics that may be useful in the future development of prognostic models.
- Description: 2003006660
- Authors: Quinn, Anthony , Jelinek, Herbert , Stranieri, Andrew , Yearwood, John
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2008, Sydney, New South Wales : 15th-18th December 2008 p. 291-296
- Full Text:
- Description: This paper investigates the application of a new data mining algorithm called Automated Weighted Sum, (AWSum), to diabetes screening data to explore its use in providing researchers with new insight into the disease and secondarily to explore the potential the algorithm has for the generation of prognostic models for clinical use. There are many data mining classifiers that produce high levels of predictive accuracy but their application to health research and clinical applications is limited because they are complex, produce results that are difficult to interpret and are difficult to integrate with current knowledge and practises. This is because most focus on accuracy at the expense of informing the user as to the influences that lead to their classification results. By providing this information on influences a researcher can be pointed to new potentially interesting avenues for investigation. AWSum measures influence by calculating a weight for each feature value that represents its influence on a class value relative to other class values. The results produced, although on limited data, indicated the approach has potential uses for research and has some characteristics that may be useful in the future development of prognostic models.
- Description: 2003006660
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