Document Difficulty Aspects for Medical Practitioners: Enhancing Information Retrieval in Personalized Search Engines
Timely and relevant information enables clinicians to make informed decisions about patient care outcomes. However, discovering related and understandable information from the vast medical literature is challenging. To address this problem, we aim to enable the development of search engines that mee...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-09-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/19/10612 |
| _version_ | 1827722868509638656 |
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| author | Sameh Frihat Catharina Lena Beckmann Eva Maria Hartmann Norbert Fuhr |
| author_facet | Sameh Frihat Catharina Lena Beckmann Eva Maria Hartmann Norbert Fuhr |
| author_sort | Sameh Frihat |
| collection | DOAJ |
| description | Timely and relevant information enables clinicians to make informed decisions about patient care outcomes. However, discovering related and understandable information from the vast medical literature is challenging. To address this problem, we aim to enable the development of search engines that meet the needs of medical practitioners by incorporating text difficulty features. We collected a dataset of 209 scientific research abstracts from different medical fields, available in both English and German. To determine the difficulty aspects of readability and technical level of each abstract, 216 medical experts annotated the dataset. We used a pre-trained BERT model, fine-tuned to our dataset, to develop a regression model predicting those difficulty features of abstracts. To highlight the strength of this approach, the model was compared to readability formulas currently in use. Analysis of the dataset revealed that German abstracts are more technically complex and less readable than their English counterparts. Our baseline model showed greater efficacy than current readability formulas in predicting domain-specific readability aspects. Conclusion: Incorporating these text difficulty aspects into the search engine will provide healthcare professionals with reliable and efficient information retrieval tools. Additionally, the dataset can serve as a starting point for future research. |
| first_indexed | 2024-03-10T21:49:21Z |
| format | Article |
| id | doaj.art-2f568d62a50940349fe556f5f0a8021f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T21:49:21Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-2f568d62a50940349fe556f5f0a8021f2023-11-19T14:01:47ZengMDPI AGApplied Sciences2076-34172023-09-0113191061210.3390/app131910612Document Difficulty Aspects for Medical Practitioners: Enhancing Information Retrieval in Personalized Search EnginesSameh Frihat0Catharina Lena Beckmann1Eva Maria Hartmann2Norbert Fuhr3Department of Information Engineering, University of Duisburg-Essen, 47057 Duisburg, GermanyDepartment of Computer Science, University of Applied Sciences and Arts Dortmund, 44227 Dortmund, GermanyDepartment of Computer Science, University of Applied Sciences and Arts Dortmund, 44227 Dortmund, GermanyDepartment of Information Engineering, University of Duisburg-Essen, 47057 Duisburg, GermanyTimely and relevant information enables clinicians to make informed decisions about patient care outcomes. However, discovering related and understandable information from the vast medical literature is challenging. To address this problem, we aim to enable the development of search engines that meet the needs of medical practitioners by incorporating text difficulty features. We collected a dataset of 209 scientific research abstracts from different medical fields, available in both English and German. To determine the difficulty aspects of readability and technical level of each abstract, 216 medical experts annotated the dataset. We used a pre-trained BERT model, fine-tuned to our dataset, to develop a regression model predicting those difficulty features of abstracts. To highlight the strength of this approach, the model was compared to readability formulas currently in use. Analysis of the dataset revealed that German abstracts are more technically complex and less readable than their English counterparts. Our baseline model showed greater efficacy than current readability formulas in predicting domain-specific readability aspects. Conclusion: Incorporating these text difficulty aspects into the search engine will provide healthcare professionals with reliable and efficient information retrieval tools. Additionally, the dataset can serve as a starting point for future research.https://www.mdpi.com/2076-3417/13/19/10612personalized information retrievalmedical practitionersreadability assessmentmedical literature aspects |
| spellingShingle | Sameh Frihat Catharina Lena Beckmann Eva Maria Hartmann Norbert Fuhr Document Difficulty Aspects for Medical Practitioners: Enhancing Information Retrieval in Personalized Search Engines Applied Sciences personalized information retrieval medical practitioners readability assessment medical literature aspects |
| title | Document Difficulty Aspects for Medical Practitioners: Enhancing Information Retrieval in Personalized Search Engines |
| title_full | Document Difficulty Aspects for Medical Practitioners: Enhancing Information Retrieval in Personalized Search Engines |
| title_fullStr | Document Difficulty Aspects for Medical Practitioners: Enhancing Information Retrieval in Personalized Search Engines |
| title_full_unstemmed | Document Difficulty Aspects for Medical Practitioners: Enhancing Information Retrieval in Personalized Search Engines |
| title_short | Document Difficulty Aspects for Medical Practitioners: Enhancing Information Retrieval in Personalized Search Engines |
| title_sort | document difficulty aspects for medical practitioners enhancing information retrieval in personalized search engines |
| topic | personalized information retrieval medical practitioners readability assessment medical literature aspects |
| url | https://www.mdpi.com/2076-3417/13/19/10612 |
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