A new scoring system in Cystic Fibrosis : Statistical tools for database analysis - A preliminary report
- Hafen, Gaudenz, Hurst, Cameron, Yearwood, John, Smith, Julie, Dzalilov, Zari, Robinson, P. J.
- Authors: Hafen, Gaudenz , Hurst, Cameron , Yearwood, John , Smith, Julie , Dzalilov, Zari , Robinson, P. J.
- Date: 2008
- Type: Text , Journal article
- Relation: BMC Medical Informatics and Decision Making Vol. 8, no. 44 (2008), p.1-11
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- Description: Background. Cystic fibrosis is the most common fatal genetic disorder in the Caucasian population. Scoring systems for assessment of Cystic fibrosis disease severity have been used for almost 50 years, without being adapted to the milder phenotype of the disease in the 21st century. The aim of this current project is to develop a new scoring system using a database and employing various statistical tools. This study protocol reports the development of the statistical tools in order to create such a scoring system. Methods. The evaluation is based on the Cystic Fibrosis database from the cohort at the Royal Children's Hospital in Melbourne. Initially, unsupervised clustering of the all data records was performed using a range of clustering algorithms. In particular incremental clustering algorithms were used. The clusters obtained were characterised using rules from decision trees and the results examined by clinicians. In order to obtain a clearer definition of classes expert opinion of each individual's clinical severity was sought. After data preparation including expert-opinion of an individual's clinical severity on a 3 point-scale (mild, moderate and severe disease), two multivariate techniques were used throughout the analysis to establish a method that would have a better success in feature selection and model derivation: 'Canonical Analysis of Principal Coordinates' and 'Linear Discriminant Analysis'. A 3-step procedure was performed with (1) selection of features, (2) extracting 5 severity classes out of a 3 severity class as defined per expert-opinion and (3) establishment of calibration datasets. Results. (1) Feature selection: CAP has a more effective "modelling" focus than DA. (2) Extraction of 5 severity classes: after variables were identified as important in discriminating contiguous CF severity groups on the 3-point scale as mild/moderate and moderate/severe, Discriminant Function (DF) was used to determine the new groups mild, intermediate moderate, moderate, intermediate severe and severe disease. (3) Generated confusion tables showed a misclassification rate of 19.1% for males and 16.5% for females, with a majority of misallocations into adjacent severity classes particularly for males. Conclusion. Our preliminary data show that using CAP for detection of selection features and Linear DA to derive the actual model in a CF database might be helpful in developing a scoring system. However, there are several limitations, particularly more data entry points are needed to finalize a score and the statistical tools have further to be refined and validated, with re-running the statistical methods in the larger dataset. © 2008 Hafen et al; licensee BioMed Central Ltd.
- Authors: Hafen, Gaudenz , Hurst, Cameron , Yearwood, John , Smith, Julie , Dzalilov, Zari , Robinson, P. J.
- Date: 2008
- Type: Text , Journal article
- Relation: BMC Medical Informatics and Decision Making Vol. 8, no. 44 (2008), p.1-11
- Full Text:
- Reviewed:
- Description: Background. Cystic fibrosis is the most common fatal genetic disorder in the Caucasian population. Scoring systems for assessment of Cystic fibrosis disease severity have been used for almost 50 years, without being adapted to the milder phenotype of the disease in the 21st century. The aim of this current project is to develop a new scoring system using a database and employing various statistical tools. This study protocol reports the development of the statistical tools in order to create such a scoring system. Methods. The evaluation is based on the Cystic Fibrosis database from the cohort at the Royal Children's Hospital in Melbourne. Initially, unsupervised clustering of the all data records was performed using a range of clustering algorithms. In particular incremental clustering algorithms were used. The clusters obtained were characterised using rules from decision trees and the results examined by clinicians. In order to obtain a clearer definition of classes expert opinion of each individual's clinical severity was sought. After data preparation including expert-opinion of an individual's clinical severity on a 3 point-scale (mild, moderate and severe disease), two multivariate techniques were used throughout the analysis to establish a method that would have a better success in feature selection and model derivation: 'Canonical Analysis of Principal Coordinates' and 'Linear Discriminant Analysis'. A 3-step procedure was performed with (1) selection of features, (2) extracting 5 severity classes out of a 3 severity class as defined per expert-opinion and (3) establishment of calibration datasets. Results. (1) Feature selection: CAP has a more effective "modelling" focus than DA. (2) Extraction of 5 severity classes: after variables were identified as important in discriminating contiguous CF severity groups on the 3-point scale as mild/moderate and moderate/severe, Discriminant Function (DF) was used to determine the new groups mild, intermediate moderate, moderate, intermediate severe and severe disease. (3) Generated confusion tables showed a misclassification rate of 19.1% for males and 16.5% for females, with a majority of misallocations into adjacent severity classes particularly for males. Conclusion. Our preliminary data show that using CAP for detection of selection features and Linear DA to derive the actual model in a CF database might be helpful in developing a scoring system. However, there are several limitations, particularly more data entry points are needed to finalize a score and the statistical tools have further to be refined and validated, with re-running the statistical methods in the larger dataset. © 2008 Hafen et al; licensee BioMed Central Ltd.
Taxonomy based on science is necessary for global conservation
- Authors: Greenslade, Penelope
- Date: 2018
- Type: Text , Journal article
- Relation: PLoS Biology Vol. 16, no. 3 (2018), p. 1-12
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- Description: Taxonomy is a scientific discipline that has provided the universal naming and classification system of biodiversity for centuries and continues effectively to accommodate new knowledge. A recent publication by Garnett and Christidis [1] expressed concerns regarding the difficulty that taxonomic changes represent for conservation efforts and proposed the establishment of a system to govern taxonomic changes. Their proposal to "restrict the freedom of taxonomic action" through governing subcommittees that would "review taxonomic papers for compliance" and their assertion that "the scientific community's failure to govern taxonomy threatens the effectiveness of global efforts to halt biodiversity loss, damages the credibility of science, and is expensive to society" are flawed in many respects. They also assert that the lack of governance of taxonomy damages conservation efforts, harms the credibility of science, and is costly to society. Despite its fairly recent release, Garnett and Christidis' proposition has already been rejected by a number of colleagues [2,3,4,5,6,7,8]. Herein, we contribute to the conversation between taxonomists and conservation biologists aiming to clarify some misunderstandings and issues in the proposition by Garnett and Christidis. **Please note that there are multiple authors for this article therefore only the name of the Federation University Australia affiliate is provided in this record**
- Authors: Greenslade, Penelope
- Date: 2018
- Type: Text , Journal article
- Relation: PLoS Biology Vol. 16, no. 3 (2018), p. 1-12
- Full Text:
- Reviewed:
- Description: Taxonomy is a scientific discipline that has provided the universal naming and classification system of biodiversity for centuries and continues effectively to accommodate new knowledge. A recent publication by Garnett and Christidis [1] expressed concerns regarding the difficulty that taxonomic changes represent for conservation efforts and proposed the establishment of a system to govern taxonomic changes. Their proposal to "restrict the freedom of taxonomic action" through governing subcommittees that would "review taxonomic papers for compliance" and their assertion that "the scientific community's failure to govern taxonomy threatens the effectiveness of global efforts to halt biodiversity loss, damages the credibility of science, and is expensive to society" are flawed in many respects. They also assert that the lack of governance of taxonomy damages conservation efforts, harms the credibility of science, and is costly to society. Despite its fairly recent release, Garnett and Christidis' proposition has already been rejected by a number of colleagues [2,3,4,5,6,7,8]. Herein, we contribute to the conversation between taxonomists and conservation biologists aiming to clarify some misunderstandings and issues in the proposition by Garnett and Christidis. **Please note that there are multiple authors for this article therefore only the name of the Federation University Australia affiliate is provided in this record**
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