CLIN-IK-LINKS: A platform for the design and execution of clinical data transformation and reasoning workflows

Maldonado, José Alberto; Marcos, Mar; Fernández‐Breis, Jesualdo Tomás; Giménez-Solano, Vicente Miguel; Legaz-García, María del Carmen; Martínez-Salvador, Begoña

doi:10.1016/j.cmpb.2020.105616

Cited by 18 publications

(16 citation statements)

References 19 publications

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“…Third, Sahu et al [ 90 ] mentioned collecting “demographic information from a subset of existing ecobee users to understand the association between age, sex, and other relevant demographic indicators (p. 8)”, and Arsevska et al [ 79 ] indicated integrating geographical and language factors. Other directions include: (1) integrating additional physiological signal monitoring modules [ 91 ], (2) combining the temporality of messages in clustering [ 61 ], (3) adopting linked open data as complementary answer sources [ 30 ], (4) exploiting health data produced via passive smartphone sensing technologies and linking them with Web-based applications [ 92 ], (5) integrating additional types of mappings or services with the basis of clinical guidelines to allow linking electronic health records with guideline-oriented decision support applications [ 93 ], (6) integrating multiple context information based on deep learning [ 94 ], (7) allowing seamless integration of data from varied sources or repositories [ 54 ], and (8) collecting propagation-related information and time series information to enhance model performance [ 86 ].…”

Section: Resultsmentioning

confidence: 99%

Machine and cognitive intelligence for human health: systematic review

Chen

Cheng

Wang³

et al. 2022

Brain Inf.

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Brain informatics is a novel interdisciplinary area that focuses on scientifically studying the mechanisms of human brain information processing by integrating experimental cognitive neuroscience with advanced Web intelligence-centered information technologies. Web intelligence, which aims to understand the computational, cognitive, physical, and social foundations of the future Web, has attracted increasing attention to facilitate the study of brain informatics to promote human health. A large number of articles created in the recent few years are proof of the investment in Web intelligence-assisted human health. This study systematically reviews academic studies regarding article trends, top journals, subjects, countries/regions, and institutions, study design, artificial intelligence technologies, clinical tasks, and performance evaluation. Results indicate that literature is especially welcomed in subjects such as medical informatics and health care sciences and service. There are several promising topics, for example, random forests, support vector machines, and conventional neural networks for disease detection and diagnosis, semantic Web, ontology mining, and topic modeling for clinical or biomedical text mining, artificial neural networks and logistic regression for prediction, and convolutional neural networks and support vector machines for monitoring and classification. Additionally, future research should focus on algorithm innovations, additional information use, functionality improvement, model and system generalization, scalability, evaluation, and automation, data acquirement and quality improvement, and allowing interaction. The findings of this study help better understand what and how Web intelligence can be applied to promote healthcare procedures and clinical outcomes. This provides important insights into the effective use of Web intelligence to support informatics-enabled brain studies.

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Section: Resultsmentioning

confidence: 99%

Machine and cognitive intelligence for human health: systematic review

Chen

Cheng

Wang³

et al. 2022

Brain Inf.

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“…The literature searches generated 1,235 publications from three sources until Jan 5, 2022. After removing duplicates and examining according to inclusion and exclusion criteria, we included 81 publications (Appendix 3) in the final review and analysis [19-21, 23-25, 43-117]. Figure 2 illustrates each step of the literature search, screening, selection flow, and results.…”

Section: Resultsmentioning

confidence: 99%

A systematic review of ontology-based clinical decision support system rules: usage, management, and interoperability

Jing

Min

Gong

et al. 2022

Preprint

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ObjectiveClinical decision support systems (CDSS) have a critical role in improving the quality and safety of health care delivery. CDSS rules direct the behavior of CDSS. However, the CDSS rules have not been routinely shared and reused, and ontology can promote the reusing of CDSS rules. We systematically screened literature to elaborate on the current status of ontology applied in CDSS rule management.MethodsWe searched PubMed, the Association for Computing Machinery (ACM) Digital Library, and the Nursing & Allied Health Database for publications focusing on ontology, clinical decision support, and rules. Grounded theory and PRISMA 2020 guidelines were followed. One author started the screening and literature analysis, and two authors validated the processes and results. Inclusion and exclusion criteria were developed and refined iteratively.ResultsAmong 81 included publications, the identified CDSS were mainly applied to managing chronic conditions, alerts for medication prescriptions, reminders for immunizations and preventive services, diagnoses, and treatment recommendations. The CDSS rules were presented in Semantic Web Rule Language, Jess, or Jena formats. Despite ontology was used to supply medical knowledge, CDSS rules, and terminologies to CDSS, ontology has not been used in CDSS rule management.ConclusionsAlthough ontology can facilitate the reuse, management, and maintenance of CDSS rules, CDSS ontology remains unavailable indicating that more efforts are needed to improve the reusability and interoperability of CDSS rules.

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“…Several previous works have addressed combining the EHR with other information sources [11][12][13][14]16,[30][31][32], most of which have focused on converting EHR data to Semantic Web formats. These approaches aim at reasoning with the data, and thus infer new facts from the patient's EHR.…”

Section: Discussionmentioning

confidence: 99%

Augmented EHR: Enrichment of EHR with Contents from Semantic Web Sources

et al. 2021

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This work presents methods to combine data from the Semantic Web into existing EHRs, leading to an augmented EHR. An existing EHR extract is augmented by combining it with additional information from external sources, typically linked data sources. The starting point is a standardized EHR extract described by an archetype. The method consists of combining specific data from the original EHR with contents from the external information source by building a semantic representation, which is used to query the external source. The results are converted into a standardized EHR extract according to an archetype. This work sets the foundations to transform Semantic Web contents into normalized EHR extracts. Finally, to exemplify the approach, the work includes a practical use case in which the summarized EHR is augmented with drug–drug interactions and disease-related treatment information.

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CLIN-IK-LINKS: A platform for the design and execution of clinical data transformation and reasoning workflows

Cited by 18 publications

References 19 publications

Machine and cognitive intelligence for human health: systematic review

Machine and cognitive intelligence for human health: systematic review

A systematic review of ontology-based clinical decision support system rules: usage, management, and interoperability

Augmented EHR: Enrichment of EHR with Contents from Semantic Web Sources

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