| 11. |
- Hedin, K, et al.
(författare)
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Liver guide for monitoring of chronic hepatitis C
- 2000
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Ingår i: JAMIA Journal of the American Medical Informatics Association. - 1067-5027 .- 1527-974X. ; , s. 340-343
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Tidskriftsartikel (refereegranskat)abstract
- The severity of chronic hepatitis C infection in the Individual patient is monitored using blood laboratory findings and liver biopsy. Lf blood test results could be shown to provide sufficient information concerning the disease, the invasive procedure of liver biopsy could perhaps be avoided in some instances. This study assessed the clinical relevance of blood laboratory tests for detecting disease-related changes. in the liver. Histopathological classification was used ta assign class membership of the patients and data mining operations were performed in an elaborate way on 19 different data sets. Disease activity could be detected by a small set of blood tests. Extended sets could identify more severe changes, but failed to distinguish them. The extracted rules are implemented as a part of the knowledge base of a corresponding decision support system aimed at specialists and general practitioners.
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| 12. |
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| 13. |
- Hägglund, Maria, et al.
(författare)
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Modeling shared care plans using CONTsys and openEHR to support shared homecare of the elderly.
- 2011
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Ingår i: JAMIA Journal of the American Medical Informatics Association. - : Oxford University Press (OUP). - 1067-5027 .- 1527-974X. ; 18:1, s. 66-9
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Tidskriftsartikel (refereegranskat)abstract
- This case report describes how two complementary standards, CONTsys (European Standard EN 13940-1 for continuity of care) and the reference model of openEHR, were applied in modeling a shared care plan for shared homecare based on requirements from the OLD@HOME project. Our study shows that these requirements are matched by CONTsys on a general level. However, certain attributes are not explicit in CONTsys, for example agents responsible for performing planned interventions, and support for monitoring outcome of interventions. We further studied how the care plan conceptual model can be implemented using the openEHR reference model. The study demonstrates the feasibility of developing shared care plans combining a standard concept model, for example CONTsys with an electronic health records (EHR) interoperability specification, that is the openEHR, while highlighting areas that need further exploration. It also explores the reusability of existing clinical archetypes as building blocks of care plans and the modeling of new shared care plan archetypes.
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| 14. |
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| 15. |
- King, Henry, et al.
(författare)
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How, for whom, and in what contexts will artificial intelligence be adopted in pathology? A realist interview study
- 2023
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Ingår i: JAMIA Journal of the American Medical Informatics Association. - : OXFORD UNIV PRESS. - 1067-5027 .- 1527-974X. ; 30:3, s. 529-538
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Tidskriftsartikel (refereegranskat)abstract
- Objective There is increasing interest in using artificial intelligence (AI) in pathology to improve accuracy and efficiency. Studies of clinicians perceptions of AI have found only moderate acceptability, suggesting further research is needed regarding integration into clinical practice. This study aimed to explore stakeholders theories concerning how and in what contexts AI is likely to become integrated into pathology. Materials and Methods A literature review provided tentative theories that were revised through a realist interview study with 20 pathologists and 5 pathology trainees. Questions sought to elicit whether, and in what ways, the tentative theories fitted with interviewees perceptions and experiences. Analysis focused on identifying the contextual factors that may support or constrain uptake of AI in pathology. Results Interviews highlighted the importance of trust in AI, with interviewees emphasizing evaluation and the opportunity for pathologists to become familiar with AI as means for establishing trust. Interviewees expressed a desire to be involved in design and implementation of AI tools, to ensure such tools address pressing needs, but needs vary by subspecialty. Workflow integration is desired but whether AI tools should work automatically will vary according to the task and the context. Conclusions It must not be assumed that AI tools that provide benefit in one subspecialty will provide benefit in others. Pathologists should be involved in the decision to introduce AI, with opportunity to assess strengths and weaknesses. Further research is needed concerning the evidence required to satisfy pathologists regarding the benefits of AI.
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| 16. |
- Kougia, Vasiliki, et al.
(författare)
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RTEX : A novel framework for ranking, tagging, and explanatory diagnostic captioning of radiography exams
- 2021
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Ingår i: JAMIA Journal of the American Medical Informatics Association. - : Oxford University Press (OUP). - 1067-5027 .- 1527-974X. ; 28:8, s. 1651-1659
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Tidskriftsartikel (refereegranskat)abstract
- Objective: The study sought to assist practitioners in identifying and prioritizing radiography exams that are more likely to contain abnormalities, and provide them with a diagnosis in order to manage heavy workload more efficiently (eg, during a pandemic) or avoid mistakes due to tiredness.Materials and MethodsThis article introduces RTEx, a novel framework for (1) ranking radiography exams based on their probability to be abnormal, (2) generating abnormality tags for abnormal exams, and (3) providing a diagnostic explanation in natural language for each abnormal exam. Our framework consists of deep learning and retrieval methods and is assessed on 2 publicly available datasets.Results: For ranking, RTEx outperforms its competitors in terms of nDCG@k. The tagging component outperforms 2 strong competitor methods in terms of F1. Moreover, the diagnostic captioning component, which exploits the predicted tags to constrain the captioning process, outperforms 4 captioning competitors with respect to clinical precision and recall.Discussion: RTEx prioritizes abnormal exams toward the improvement of the healthcare workflow by introducing a ranking method. Also, for each abnormal radiography exam RTEx generates a set of abnormality tags alongside a diagnostic text to explain the tags and guide the medical expert. Human evaluation of the produced text shows that employing the generated tags offers consistency to the clinical correctness and that the sentences of each text have high clinical accuracy.Conclusions: This is the first framework that successfully combines 3 tasks: ranking, tagging, and diagnostic captioning with focus on radiography exams that contain abnormalities.
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| 17. |
- Lee, Dennis, et al.
(författare)
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Literature review of SNOMED CT use
- 2014
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Ingår i: JAMIA Journal of the American Medical Informatics Association. - : BMJ Group. - 1067-5027 .- 1527-974X. ; 21:E1, s. E11-E19
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Forskningsöversikt (refereegranskat)abstract
- OBJECTIVE: The aim of this paper is to report on the use of the systematised nomenclature of medicine clinical terms (SNOMED CT) by providing an overview of published papers.METHODS: Published papers on SNOMED CT between 2001 and 2012 were identified using PubMed and Embase databases using the keywords 'systematised nomenclature of medicine' and 'SNOMED CT'. For each paper the following characteristics were retrieved: SNOMED CT focus category (ie, indeterminate, theoretical, pre-development/design, implementation and evaluation/commodity), usage category (eg, prospective content coverage, used to classify or code in a study), medical domain and country.RESULTS: Our search strategy identified 488 papers. A comparison between the papers published between 2001-6 and 2007-12 showed an increase in every SNOMED CT focus category. The number of papers classified as 'theoretical' increased from 46 to 78, 'pre-development/design' increased from 61 to 173 and 'implementation' increased from 10 to 34. Papers classified as 'evaluation/commodity' only started to appear from 2010.CONCLUSIONS: The majority of studies focused on 'theoretical' and 'pre-development/design'. This is still encouraging as SNOMED CT is being harmonized with other standardized terminologies and is being evaluated to determine the content coverage of local terms, which is usually one of the first steps towards adoption. Most implementations are not published in the scientific literature, requiring a look beyond the scientific literature to gain insights into SNOMED CT implementations.
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| 18. |
- Low, Yen S., et al.
(författare)
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Cheminformatics-aided pharmacovigilance : application to Stevens-Johnson Syndrome
- 2016
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Ingår i: JAMIA Journal of the American Medical Informatics Association. - : Oxford University Press (OUP). - 1067-5027 .- 1527-974X. ; 23:5, s. 968-978
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Tidskriftsartikel (refereegranskat)abstract
- Objective Quantitative Structure-Activity Relationship (QSAR) models can predict adverse drug reactions (ADRs), and thus provide early warnings of potential hazards. Timely identification of potential safety concerns could protect patients and aid early diagnosis of ADRs among the exposed. Our objective was to determine whether global spontaneous reporting patterns might allow chemical substructures associated with Stevens-Johnson Syndrome (SJS) to be identified and utilized for ADR prediction by QSAR models. Materials and Methods Using a reference set of 364 drugs having positive or negative reporting correlations with SJS in the VigiBase global repository of individual case safety reports (Uppsala Monitoring Center, Uppsala, Sweden), chemical descriptors were computed from drug molecular structures. Random Forest and Support Vector Machines methods were used to develop QSAR models, which were validated by external 5-fold cross validation. Models were employed for virtual screening of DrugBank to predict SJS actives and inactives, which were corroborated using knowledge bases like VigiBase, ChemoText, and MicroMedex (Truven Health Analytics Inc, Ann Arbor, Michigan). Results We developed QSAR models that could accurately predict if drugs were associated with SJS (area under the curve of 75%-81%). Our 10 most active and inactive predictions were substantiated by SJS reports (or lack thereof) in the literature. Discussion Interpretation of QSAR models in terms of significant chemical descriptors suggested novel SJS structural alerts. Conclusions We have demonstrated that QSAR models can accurately identify SJS active and inactive drugs. Requiring chemical structures only, QSAR models provide effective computational means to flag potentially harmful drugs for subsequent targeted surveillance and pharmacoepidemiologic investigations.
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| 19. |
- Ludvigsson, Jonas F., et al.
(författare)
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Use of computerized algorithm to identify individuals in need of testing for celiac disease
- 2013
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Ingår i: JAMIA Journal of the American Medical Informatics Association. - : Oxford University Press (OUP). - 1067-5027 .- 1527-974X. ; 20:E2, s. E306-E310
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Tidskriftsartikel (refereegranskat)abstract
- Background and aim Celiac disease (CD) is a lifelong immune-mediated disease with excess mortality. Early diagnosis is important to minimize disease symptoms, complications, and consumption of healthcare resources. Most patients remain undiagnosed. We developed two electronic medical record (EMR)-based algorithms to identify patients at high risk of CD and in need of CD screening. Methods (I) Using natural language processing (NLP), we searched EMRs for 16 free text (and related) terms in 216 CD patients and 280 controls. (II) EMRs were also searched for ICD9 (International Classification of Disease) codes suggesting an increased risk of CD in 202 patients with CD and 524 controls. For each approach, we determined the optimal number of hits to be assigned as CD cases. To assess performance of these algorithms, sensitivity and specificity were calculated. Results Using two hits as the cut-off, the NLP algorithm identified 72.9% of all celiac patients (sensitivity), and ruled out CD in 89.9% of the controls (specificity). In a representative US population of individuals without a prior celiac diagnosis (assuming that 0.6% had undiagnosed CD), this NLP algorithm could identify a group of individuals where 4.2% would have CD (positive predictive value). ICD9 code search using three hits as the cut-off had a sensitivity of 17.1% and a specificity of 88.5% (positive predictive value was 0.9%). Discussion and conclusions This study shows that computerized EMR-based algorithms can help identify patients at high risk of CD. NLP-based techniques demonstrate higher sensitivity and positive predictive values than algorithms based on ICD9 code searches.
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| 20. |
- Martinez-Costa, Catalina, et al.
(författare)
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Semantic enrichment of clinical models towards semantic interoperability. The heart failure summary use case
- 2015
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Ingår i: JAMIA Journal of the American Medical Informatics Association. - : Oxford University Press (OUP): Policy B - Oxford Open Option B - CC-BY / Elsevier. - 1067-5027 .- 1527-974X. ; 22:3, s. 565-576
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Tidskriftsartikel (refereegranskat)abstract
- Objective To improve semantic interoperability of electronic health records (EHRs) by ontology-based mediation across syntactically heterogeneous representations of the same or similar clinical information. Materials and Methods Our approach is based on a semantic layer that consists of: (1) a set of ontologies supported by (2) a set of semantic patterns. The first aspect of the semantic layer helps standardize the clinical information modeling task and the second shields modelers from the complexity of ontology modeling. We applied this approach to heterogeneous representations of an excerpt of a heart failure summary. Results Using a set of finite top-level patterns to derive semantic patterns, we demonstrate that those patterns, or compositions thereof, can be used to represent information from clinical models. Homogeneous querying of the same or similar information, when represented according to heterogeneous clinical models, is feasible. Discussion Our approach focuses on the meaning embedded in EHRs, regardless of their structure. This complex task requires a clear ontological commitment (ie, agreement to consistently use the shared vocabulary within some context), together with formalization rules. These requirements are supported by semantic patterns. Other potential uses of this approach, such as clinical models validation, require further investigation. Conclusion We show how an ontology-based representation of a clinical summary, guided by semantic patterns, allows homogeneous querying of heterogeneous information structures. Whether there are a finite number of top-level patterns is an open question.
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