Introduction of affinity set and its application in data-mining example of delayed diagnosis
At least 44,000 people die in hospitals each year as a result of medical errors, and these deaths are becoming the eighth-leading cause of death in the United States. Thus, medical providers have the responsibility to pay attention for reducing avoidable medical errors and improve patient safety as...
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| Format: | Article |
| Language: | English |
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Elsevier
2009
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| Online Access: | http://irep.iium.edu.my/1546/ http://irep.iium.edu.my/1546/1/Introduction_of_affinity_set_and_its_application_in_data-mining_example_of_delayed_diagnosis.pdf |
| _version_ | 1848775872145784832 |
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| author | Chen, Yuh-Wen Larbani, Moussa Hsieh, Cheng-Yen Chen, Chao-Wen |
| author_facet | Chen, Yuh-Wen Larbani, Moussa Hsieh, Cheng-Yen Chen, Chao-Wen |
| author_sort | Chen, Yuh-Wen |
| building | IIUM Repository |
| collection | Online Access |
| description | At least 44,000 people die in hospitals each year as a result of medical errors, and these deaths are becoming the eighth-leading cause of death in the United States. Thus, medical providers have the responsibility to pay attention for reducing avoidable medical errors and improve patient safety as best as they can. It requires the rapid evaluation and prioritisation of life threatening injuries in the primary survey followed by a detailed secondary survey in the emergency room. However, time is always valuable and limited such that some important vital signs may be delayed and ignored. This research explores delayed diagnosis problem and uses the affinity set by Topology concept to classify/focus on key attributes causing delayed diagnosis (missed injury) in order to reduce error risk. Results interestingly indicate that when a patient can breathe normally, but his (or her) blood-pressure or pulse is abnormal, a high probability of delayed diagnosis exists. This affinity work also compares the performance with the model of rough set (Rosetta), neural network, support vector machine and logistic regression. And our affinity model shows its advantage by prediction accuracy and explanation power |
| first_indexed | 2025-11-14T14:21:05Z |
| format | Article |
| id | iium-1546 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T14:21:05Z |
| publishDate | 2009 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-15462011-08-22T04:31:05Z http://irep.iium.edu.my/1546/ Introduction of affinity set and its application in data-mining example of delayed diagnosis Chen, Yuh-Wen Larbani, Moussa Hsieh, Cheng-Yen Chen, Chao-Wen QA75 Electronic computers. Computer science At least 44,000 people die in hospitals each year as a result of medical errors, and these deaths are becoming the eighth-leading cause of death in the United States. Thus, medical providers have the responsibility to pay attention for reducing avoidable medical errors and improve patient safety as best as they can. It requires the rapid evaluation and prioritisation of life threatening injuries in the primary survey followed by a detailed secondary survey in the emergency room. However, time is always valuable and limited such that some important vital signs may be delayed and ignored. This research explores delayed diagnosis problem and uses the affinity set by Topology concept to classify/focus on key attributes causing delayed diagnosis (missed injury) in order to reduce error risk. Results interestingly indicate that when a patient can breathe normally, but his (or her) blood-pressure or pulse is abnormal, a high probability of delayed diagnosis exists. This affinity work also compares the performance with the model of rough set (Rosetta), neural network, support vector machine and logistic regression. And our affinity model shows its advantage by prediction accuracy and explanation power Elsevier 2009 Article PeerReviewed application/pdf en http://irep.iium.edu.my/1546/1/Introduction_of_affinity_set_and_its_application_in_data-mining_example_of_delayed_diagnosis.pdf Chen, Yuh-Wen and Larbani, Moussa and Hsieh, Cheng-Yen and Chen, Chao-Wen (2009) Introduction of affinity set and its application in data-mining example of delayed diagnosis. Expert Systems with Application, 36. pp. 10883-10889. ISSN 0957-4174 http://dl.acm.org/citation.cfm?id=1542628 10.1016/j.eswa.2009.02.020 |
| spellingShingle | QA75 Electronic computers. Computer science Chen, Yuh-Wen Larbani, Moussa Hsieh, Cheng-Yen Chen, Chao-Wen Introduction of affinity set and its application in data-mining example of delayed diagnosis |
| title | Introduction of affinity set and its application in data-mining example of delayed diagnosis |
| title_full | Introduction of affinity set and its application in data-mining example of delayed diagnosis |
| title_fullStr | Introduction of affinity set and its application in data-mining example of delayed diagnosis |
| title_full_unstemmed | Introduction of affinity set and its application in data-mining example of delayed diagnosis |
| title_short | Introduction of affinity set and its application in data-mining example of delayed diagnosis |
| title_sort | introduction of affinity set and its application in data-mining example of delayed diagnosis |
| topic | QA75 Electronic computers. Computer science |
| url | http://irep.iium.edu.my/1546/ http://irep.iium.edu.my/1546/ http://irep.iium.edu.my/1546/ http://irep.iium.edu.my/1546/1/Introduction_of_affinity_set_and_its_application_in_data-mining_example_of_delayed_diagnosis.pdf |