Combining classifiers for robust PICO element detection
<p>Abstract</p> <p>Background</p> <p>Formulating a clinical information need in terms of the four atomic parts which are Population/Problem, Intervention, Comparison and Outcome (known as PICO elements) facilitates searching for a precise answer within a large medical c...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2010-05-01
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| Series: | BMC Medical Informatics and Decision Making |
| Online Access: | http://www.biomedcentral.com/1472-6947/10/29 |
| _version_ | 1811314861317029888 |
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| author | Grad Roland Bartlett Joan C Nie Jian-Yun Boudin Florian Pluye Pierre Dawes Martin |
| author_facet | Grad Roland Bartlett Joan C Nie Jian-Yun Boudin Florian Pluye Pierre Dawes Martin |
| author_sort | Grad Roland |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Formulating a clinical information need in terms of the four atomic parts which are Population/Problem, Intervention, Comparison and Outcome (known as PICO elements) facilitates searching for a precise answer within a large medical citation database. However, using PICO defined items in the information retrieval process requires a search engine to be able to detect and index PICO elements in the collection in order for the system to retrieve relevant documents.</p> <p>Methods</p> <p>In this study, we tested multiple supervised classification algorithms and their combinations for detecting PICO elements within medical abstracts. Using the structural descriptors that are embedded in some medical abstracts, we have automatically gathered large training/testing data sets for each PICO element.</p> <p>Results</p> <p>Combining multiple classifiers using a weighted linear combination of their prediction scores achieves promising results with an <it>f</it>-measure score of 86.3% for P, 67% for I and 56.6% for O.</p> <p>Conclusions</p> <p>Our experiments on the identification of PICO elements showed that the task is very challenging. Nevertheless, the performance achieved by our identification method is competitive with previously published results and shows that this task can be achieved with a high accuracy for the P element but lower ones for I and O elements.</p> |
| first_indexed | 2024-04-13T11:18:57Z |
| format | Article |
| id | doaj.art-7ac3d887413c4786a71c6e77c130bd1f |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-04-13T11:18:57Z |
| publishDate | 2010-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-7ac3d887413c4786a71c6e77c130bd1f2022-12-22T02:48:52ZengBMCBMC Medical Informatics and Decision Making1472-69472010-05-011012910.1186/1472-6947-10-29Combining classifiers for robust PICO element detectionGrad RolandBartlett Joan CNie Jian-YunBoudin FlorianPluye PierreDawes Martin<p>Abstract</p> <p>Background</p> <p>Formulating a clinical information need in terms of the four atomic parts which are Population/Problem, Intervention, Comparison and Outcome (known as PICO elements) facilitates searching for a precise answer within a large medical citation database. However, using PICO defined items in the information retrieval process requires a search engine to be able to detect and index PICO elements in the collection in order for the system to retrieve relevant documents.</p> <p>Methods</p> <p>In this study, we tested multiple supervised classification algorithms and their combinations for detecting PICO elements within medical abstracts. Using the structural descriptors that are embedded in some medical abstracts, we have automatically gathered large training/testing data sets for each PICO element.</p> <p>Results</p> <p>Combining multiple classifiers using a weighted linear combination of their prediction scores achieves promising results with an <it>f</it>-measure score of 86.3% for P, 67% for I and 56.6% for O.</p> <p>Conclusions</p> <p>Our experiments on the identification of PICO elements showed that the task is very challenging. Nevertheless, the performance achieved by our identification method is competitive with previously published results and shows that this task can be achieved with a high accuracy for the P element but lower ones for I and O elements.</p>http://www.biomedcentral.com/1472-6947/10/29 |
| spellingShingle | Grad Roland Bartlett Joan C Nie Jian-Yun Boudin Florian Pluye Pierre Dawes Martin Combining classifiers for robust PICO element detection BMC Medical Informatics and Decision Making |
| title | Combining classifiers for robust PICO element detection |
| title_full | Combining classifiers for robust PICO element detection |
| title_fullStr | Combining classifiers for robust PICO element detection |
| title_full_unstemmed | Combining classifiers for robust PICO element detection |
| title_short | Combining classifiers for robust PICO element detection |
| title_sort | combining classifiers for robust pico element detection |
| url | http://www.biomedcentral.com/1472-6947/10/29 |
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