An open access medical knowledge base for community driven diagnostic decision support system development
Abstract Introduction While early diagnostic decision support systems were built around knowledge bases, more recent systems employ machine learning to consume large amounts of health data. We argue curated knowledge bases will remain an important component of future diagnostic decision support syst...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2019-04-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12911-019-0804-1 |
| _version_ | 1818243368916877312 |
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| author | Lars Müller Rashmi Gangadharaiah Simone C. Klein James Perry Greg Bernstein David Nurkse Dustin Wailes Rishi Graham Robert El-Kareh Sanjay Mehta Staal A. Vinterbo Eliah Aronoff-Spencer |
| author_facet | Lars Müller Rashmi Gangadharaiah Simone C. Klein James Perry Greg Bernstein David Nurkse Dustin Wailes Rishi Graham Robert El-Kareh Sanjay Mehta Staal A. Vinterbo Eliah Aronoff-Spencer |
| author_sort | Lars Müller |
| collection | DOAJ |
| description | Abstract Introduction While early diagnostic decision support systems were built around knowledge bases, more recent systems employ machine learning to consume large amounts of health data. We argue curated knowledge bases will remain an important component of future diagnostic decision support systems by providing ground truth and facilitating explainable human-computer interaction, but that prototype development is hampered by the lack of freely available computable knowledge bases. Methods We constructed an open access knowledge base and evaluated its potential in the context of a prototype decision support system. We developed a modified set-covering algorithm to benchmark the performance of our knowledge base compared to existing platforms. Testing was based on case reports from selected literature and medical student preparatory material. Results The knowledge base contains over 2000 ICD-10 coded diseases and 450 RX-Norm coded medications, with over 8000 unique observations encoded as SNOMED or LOINC semantic terms. Using 117 medical cases, we found the accuracy of the knowledge base and test algorithm to be comparable to established diagnostic tools such as Isabel and DXplain. Our prototype, as well as DXplain, showed the correct answer as “best suggestion” in 33% of the cases. While we identified shortcomings during development and evaluation, we found the knowledge base to be a promising platform for decision support systems. Conclusion We built and successfully evaluated an open access knowledge base to facilitate the development of new medical diagnostic assistants. This knowledge base can be expanded and curated by users and serve as a starting point to facilitate new technology development and system improvement in many contexts. |
| first_indexed | 2024-12-12T14:00:01Z |
| format | Article |
| id | doaj.art-90af0da5b21b4a0893e82dc3d5a3af5b |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-12-12T14:00:01Z |
| publishDate | 2019-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-90af0da5b21b4a0893e82dc3d5a3af5b2022-12-22T00:22:22ZengBMCBMC Medical Informatics and Decision Making1472-69472019-04-011911710.1186/s12911-019-0804-1An open access medical knowledge base for community driven diagnostic decision support system developmentLars Müller0Rashmi Gangadharaiah1Simone C. Klein2James Perry3Greg Bernstein4David Nurkse5Dustin Wailes6Rishi Graham7Robert El-Kareh8Sanjay Mehta9Staal A. Vinterbo10Eliah Aronoff-Spencer11Design Lab, UCSDAmazon Web ServicesSchool of Medicine, UCSDSchool of Medicine, UCSDSchool of Medicine, UCSDSchool of Medicine, UCSDSchool of Medicine, UCSDDesign Lab, UCSDDivision of Biomedical Informatics, UCSDDivision of Infectious Diseases, UCSDDepartment of Information Security and Communication Technology, Norwegian University of Science and TechnologyDesign Lab, UCSDAbstract Introduction While early diagnostic decision support systems were built around knowledge bases, more recent systems employ machine learning to consume large amounts of health data. We argue curated knowledge bases will remain an important component of future diagnostic decision support systems by providing ground truth and facilitating explainable human-computer interaction, but that prototype development is hampered by the lack of freely available computable knowledge bases. Methods We constructed an open access knowledge base and evaluated its potential in the context of a prototype decision support system. We developed a modified set-covering algorithm to benchmark the performance of our knowledge base compared to existing platforms. Testing was based on case reports from selected literature and medical student preparatory material. Results The knowledge base contains over 2000 ICD-10 coded diseases and 450 RX-Norm coded medications, with over 8000 unique observations encoded as SNOMED or LOINC semantic terms. Using 117 medical cases, we found the accuracy of the knowledge base and test algorithm to be comparable to established diagnostic tools such as Isabel and DXplain. Our prototype, as well as DXplain, showed the correct answer as “best suggestion” in 33% of the cases. While we identified shortcomings during development and evaluation, we found the knowledge base to be a promising platform for decision support systems. Conclusion We built and successfully evaluated an open access knowledge base to facilitate the development of new medical diagnostic assistants. This knowledge base can be expanded and curated by users and serve as a starting point to facilitate new technology development and system improvement in many contexts.http://link.springer.com/article/10.1186/s12911-019-0804-1Decision support systems, clinical (D020000)Diagnosis, differential (D003937)Knowledge bases (D051188) |
| spellingShingle | Lars Müller Rashmi Gangadharaiah Simone C. Klein James Perry Greg Bernstein David Nurkse Dustin Wailes Rishi Graham Robert El-Kareh Sanjay Mehta Staal A. Vinterbo Eliah Aronoff-Spencer An open access medical knowledge base for community driven diagnostic decision support system development BMC Medical Informatics and Decision Making Decision support systems, clinical (D020000) Diagnosis, differential (D003937) Knowledge bases (D051188) |
| title | An open access medical knowledge base for community driven diagnostic decision support system development |
| title_full | An open access medical knowledge base for community driven diagnostic decision support system development |
| title_fullStr | An open access medical knowledge base for community driven diagnostic decision support system development |
| title_full_unstemmed | An open access medical knowledge base for community driven diagnostic decision support system development |
| title_short | An open access medical knowledge base for community driven diagnostic decision support system development |
| title_sort | open access medical knowledge base for community driven diagnostic decision support system development |
| topic | Decision support systems, clinical (D020000) Diagnosis, differential (D003937) Knowledge bases (D051188) |
| url | http://link.springer.com/article/10.1186/s12911-019-0804-1 |
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