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Interactive visualization and navigation of complex terminology systems, exemplified by SNOMED CT.

https://arctichealth.org/en/permalink/ahliterature79795
Source
Stud Health Technol Inform. 2006;124:851-6
Publication Type
Article
Date
2006
Author
Sundvall Erik
Nyström Mikael
Petersson Håkan
Ahlfeldt Hans
Author Affiliation
Department of Biomedical Engineering, Linköpings Universitet, Sweden. erisu@imt.liu.se
Source
Stud Health Technol Inform. 2006;124:851-6
Date
2006
Language
English
Publication Type
Article
Keywords
Data Display
Medical Informatics
Sweden
Systematized Nomenclature of Medicine
Terminology
User-Computer Interface
Abstract
Free-text queries are natural entries into the exploration of complex terminology systems. The way search results are presented has impact on the user's ability to grasp the overall structure of the system. Complex hierarchies like the one used in SNOMED CT, where nodes have multiple parents (IS-A) and several other relationship types, makes visualization challenging. This paper presents a prototype, TermViz, applying well known methods like "focus+context" and self-organizing layouts from the fields of Information Visualization and Graph Drawing to terminologies like SNOMED CT and ICD-10. The user can simultaneously focus on several nodes in the terminologies and then use interactive animated graph navigation and semantic zooming to further explore the terminology systems without loosing context. The prototype, based on Open Source Java components, demonstrates how a number of Information Visualisation methods can aid the exploration of medical terminologies with millions of elements and can serve as a base for further development.
PubMed ID
17108619 View in PubMed
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Non-compliance with a postmastectomy radiotherapy guideline: decision tree and cause analysis.

https://arctichealth.org/en/permalink/ahliterature91961
Source
BMC Med Inform Decis Mak. 2008;8:41
Publication Type
Article
Date
2008
Author
Razavi Amir R
Gill Hans
Ahlfeldt Hans
Shahsavar Nosrat
Author Affiliation
Department of Biomedical Engineering, Division of Medical Informatics, Linköping University, Sweden. amira@imt.liu.se
Source
BMC Med Inform Decis Mak. 2008;8:41
Date
2008
Language
English
Publication Type
Article
Keywords
Age Factors
Breast Neoplasms - radiotherapy - surgery
Decision Trees
Female
Guideline Adherence - statistics & numerical data
Humans
Mastectomy
Neoplasm Recurrence, Local - prevention & control
Practice Guidelines as Topic
Radiotherapy, Adjuvant - utilization
Registries
Sweden
Treatment Refusal - statistics & numerical data
Abstract
BACKGROUND: The guideline for postmastectomy radiotherapy (PMRT), which is prescribed to reduce recurrence of breast cancer in the chest wall and improve overall survival, is not always followed. Identifying and extracting important patterns of non-compliance are crucial in maintaining the quality of care in Oncology. METHODS: Analysis of 759 patients with malignant breast cancer using decision tree induction (DTI) found patterns of non-compliance with the guideline. The PMRT guideline was used to separate cases according to the recommendation to receive or not receive PMRT. The two groups of patients were analyzed separately. Resulting patterns were transformed into rules that were then compared with the reasons that were extracted by manual inspection of records for the non-compliant cases. RESULTS: Analyzing patients in the group who should receive PMRT according to the guideline did not result in a robust decision tree. However, classification of the other group, patients who should not receive PMRT treatment according to the guideline, resulted in a tree with nine leaves and three of them were representing non-compliance with the guideline. In a comparison between rules resulting from these three non-compliant patterns and manual inspection of patient records, the following was found: In the decision tree, presence of perigland growth is the most important variable followed by number of malignantly invaded lymph nodes and level of Progesterone receptor. DNA index, age, size of the tumor and level of Estrogen receptor are also involved but with less importance. From manual inspection of the cases, the most frequent pattern for non-compliance is age above the threshold followed by near cut-off values for risk factors and unknown reasons. CONCLUSION: Comparison of patterns of non-compliance acquired from data mining and manual inspection of patient records demonstrates that not all of the non-compliances are repetitive or important. There are some overlaps between important variables acquired from manual inspection of patient records and data mining but they are not identical. Data mining can highlight non-compliance patterns valuable for guideline authors and for medical audit. Improving guidelines by using feedback from data mining can improve the quality of care in oncology.
PubMed ID
18803875 View in PubMed
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Predicting metastasis in breast cancer: comparing a decision tree with domain experts.

https://arctichealth.org/en/permalink/ahliterature83746
Source
J Med Syst. 2007 Aug;31(4):263-73
Publication Type
Article
Date
Aug-2007
Author
Razavi Amir R
Gill Hans
Ahlfeldt Hans
Shahsavar Nosrat
Author Affiliation
Department of Biomedical Engineering, Division of Medical Informatics, Linköping University, University Hospital, S-58185 Linköping, Sweden. amirreza.razavi@imt.liu.se
Source
J Med Syst. 2007 Aug;31(4):263-73
Date
Aug-2007
Language
English
Publication Type
Article
Keywords
Breast Neoplasms - pathology
Data Interpretation, Statistical
Decision Trees
Female
Humans
Models, Statistical
Neoplasm Metastasis
Neoplasm Recurrence, Local
Prognosis
Registries
Sweden
Abstract
Breast malignancy is the second most common cause of cancer death among women in Western countries. Identifying high-risk patients is vital in order to provide them with specialized treatment. In some situations, such as when access to experienced oncologists is not possible, decision support methods can be helpful in predicting the recurrence of cancer. Three thousand six hundred ninety-nine breast cancer patients admitted in south-east Sweden from 1986 to 1995 were studied. A decision tree was trained with all patients except for 100 cases and tested with those 100 cases. Two domain experts were asked for their opinions about the probability of recurrence of a certain outcome for these 100 patients. ROC curves, area under the ROC curves, and calibration for predictions were computed and compared. After comparing the predictions from a model built by data mining with predictions made by two domain experts, no significant differences were noted. In situations where experienced oncologists are not available, predictive models created with data mining techniques can be used to support physicians in decision making with acceptable accuracy.
PubMed ID
17685150 View in PubMed
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