Infectious disease threat events (IDTEs) are increasing in frequency worldwide. We analyzed underlying drivers of 116 IDTEs detected in Europe during 2008-2013 by epidemic intelligence at the ...European Centre of Disease Prevention and Control. Seventeen drivers were identified and categorized into 3 groups: globalization and environment, sociodemographic, and public health systems. A combination of >= 2 drivers was responsible for most IDTEs. The driver category globalization and environment contributed to 61% of individual IDTEs, and the top 5 individual drivers of all IDTEs were travel and tourism, food and water quality, natural environment, global trade, and climate. Hierarchical cluster analysis of all drivers identified travel and tourism as a distinctly separate driver. Monitoring and modeling such disease drivers can help anticipate future IDTEs and strengthen control measures. More important, intervening directly on these underlying drivers can diminish the likelihood of the occurrence of an IDTE and reduce the associated human and economic costs.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, ODKLJ, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Artificial intelligence (AI) has attracted much attention because of its enormous potential in healthcare, but uptake has been slow. There are substantial barriers that challenge health technology ...assessment (HTA) professionals to use AI-generated evidence for decision-making from large real-world databases (e.g., based on claims data). As part of the European Commission-funded HTx H2020 (Next Generation Health Technology Assessment) project, we aimed to put forward recommendations to support healthcare decision-makers in integrating AI into the HTA processes. The barriers, addressed by the paper, are particularly focusing on Central and Eastern European (CEE) countries, where the implementation of HTA and access to health databases lag behind Western European countries.
We constructed a survey to rank the barriers to using AI for HTA purposes, completed by respondents from CEE jurisdictions with expertise in HTA. Using the results, two members of the HTx consortium from CEE developed recommendations on the most critical barriers. Then these recommendations were discussed in a workshop by a wider group of experts, including HTA and reimbursement decision-makers from both CEE countries and Western European countries, and summarized in a consensus report.
Recommendations have been developed to address the top 15 barriers in areas of (1) human factor-related barriers, focusing on educating HTA doers and users, establishing collaborations and best practice sharing; (2) regulatory and policy-related barriers, proposing increasing awareness and political commitment and improving the management of sensitive information for AI use; (3) data-related barriers, suggesting enhancing standardization and collaboration with data networks, managing missing and unstructured data, using analytical and statistical approaches to address bias, using quality assessment tools and quality standards, improving reporting, and developing better conditions for the use of data; and (4) technological barriers, suggesting sustainable development of AI infrastructure.
In the field of HTA, the great potential of AI to support evidence generation and evaluation has not yet been sufficiently explored and realized. Raising awareness of the intended and unintended consequences of AI-based methods and encouraging political commitment from policymakers is necessary to upgrade the regulatory and infrastructural environment and knowledge base required to integrate AI into HTA-based decision-making processes better.
This work aims at uncovering challenges in biomedical knowledge representation research by providing an understanding of what was historically called "medical concept representation" and used as the ...name for a working group of the International Medical Informatics Association.
Bibliometrics, text mining, and a social media survey compare the research done in this area between two periods, before and after 2000.
Both the opinion of socially active groups of researchers and the interpretation of bibliometric data since 1988 suggest that the focus of research has moved from "medical concept representation" to "medical ontologies".
It remains debatable whether the observed change amounts to a paradigm shift or whether it simply reflects changes in naming, following the natural evolution of ontology research and engineering activities in the 1990s. The availability of powerful tools to handle ontologies devoted to certain areas of biomedicine has not resulted in a large-scale breakthrough beyond advances in basic research.
Summary
Introduction
: Artificial intelligence (AI) is widespread in many areas, including medicine. However, it is unclear what exactly AI encompasses. This paper aims to provide an improved ...understanding of medical AI and its constituent fields, and their interplay with knowledge representation (KR).
Methods
: We followed a Wittgensteinian approach (“meaning by usage”) applied to content metadata labels, using the Medical Subject Headings (MeSH) thesaurus to classify the field. To understand and characterize medical AI and the role of KR, we analyzed: (1) the proportion of papers in MEDLINE related to KR and various AI fields; (2) the interplay among KR and AI fields and overlaps among the AI fields; (3) interconnectedness of fields; and (4) phrase frequency and collocation based on a corpus of abstracts.
Results
: Data from over eighty thousand papers showed a steep, six-fold surge in the last 30 years. This growth happened in an escalating and cascading way. A corpus of 246,308 total words containing 21,842 unique words showed several hundred occurrences of notions such as robotics, fuzzy logic, neural networks, machine learning and expert systems in the phrase frequency analysis. Collocation analysis shows that fuzzy logic seems to be the most often collocated notion. Neural networks and machine learning are also used in the conceptual neighborhood of KR. Robotics is more isolated.
Conclusions
: Authors note an escalation of published AI studies in medicine. Knowledge representation is one of the smaller areas, but also the most interconnected, and provides a common cognitive layer for other areas.
Introduction: Artificial intelligence (AI) is widespread in many areas, including medicine. However, it is unclear what exactly AI encompasses. This paper aims to provide an improved understanding of ...medical AI and its constituent fields, and their interplay with knowledge representation (KR).
Methods: We followed a Wittgensteinian approach (“meaning by usage”) applied to content metadata labels, using the Medical Subject Headings (MeSH) thesaurus to classify the field. To understand and characterize medical AI and the role of KR, we analyzed: (1) the proportion of papers in MEDLINE related to KR and various AI fields; (2) the interplay among KR and AI fields and overlaps among the AI fields; (3) interconnectedness of fields; and (4) phrase frequency and collocation based on a corpus of abstracts.
Results: Data from over eighty thousand papers showed a steep, six-fold surge in the last 30 years. This growth happened in an escalating and cascading way. A corpus of 246,308 total words containing 21,842 unique words showed several hundred occurrences of notions such as robotics, fuzzy logic, neural networks, machine learning and expert systems in the phrase frequency analysis. Collocation analysis shows that fuzzy logic seems to be the most often collocated notion. Neural networks and machine learning are also used in the conceptual neighborhood of KR. Robotics is more isolated.
Conclusions: Authors note an escalation of published AI studies in medicine. Knowledge representation is one of the smaller areas, but also the most interconnected, and provides a common cognitive layer for other areas.
Economic evaluations (EEs) are commonly used by decision makers to understand the value of health interventions. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS 2022) provide ...reporting guidelines for EEs. Healthcare systems will increasingly see new interventions that use artificial intelligence (AI) to perform their function. We developed Consolidated Health Economic Evaluation Reporting Standards for Interventions that use AI (CHEERS-AI) to ensure EEs of AI-based health interventions are reported in a transparent and reproducible manner.
Potential CHEERS-AI reporting items were informed by 2 published systematic literature reviews of EEs and a contemporary update. A Delphi study was conducted using 3 survey rounds to elicit multidisciplinary expert views on 26 potential items, through a 9-point Likert rating scale and qualitative comments. An online consensus meeting was held to finalize outstanding reporting items. A digital health patient group reviewed the final checklist from a patient perspective.
A total of 58 participants responded to survey round 1, 42, and 31 of whom responded to rounds 2 and 3, respectively. Nine participants joined the consensus meeting. Ultimately, 38 reporting items were included in CHEERS-AI. They comprised the 28 original CHEERS 2022 items, plus 10 new AI-specific reporting items. Additionally, 8 of the original CHEERS 2022 items were elaborated on to ensure AI-specific nuance is reported.
CHEERS-AI should be used when reporting an EE of an intervention that uses AI to perform its function. CHEERS-AI will help decision makers and reviewers to understand important AI-specific details of an intervention, and any implications for the EE methods used and cost-effectiveness conclusions.
•The use of artificial intelligence (AI) in healthcare is expanding rapidly. New health interventions that use AI to perform their functions are increasingly expected to be developed. To date, the reporting of economic evaluations (EEs) of AI-based health interventions appears to lack important details regarding the AI nature of the intervention and potential implications for cost-effectiveness results.•The Consolidated Health Economic Evaluation Reporting Standards for Interventions that use AI (CHEERS-AI) checklist is intended to standardize reporting of EEs of health technologies that use AI. Developed using a Delphi study, it contains 38 reporting items in total. It comprises the original 28 CHEERS-2022 checklist items with 8 elaborations to draw out potential AI-related nuances, plus 10 new AI-specific items that extend upon CHEERS-2022.•The CHEERS-AI checklist will ensure that important details relating to the AI nature of the intervention and implications for the analysis are reported in a transparent and reproducible way. CHEERS-AI will also support the interpretation and comparison of such studies by reviewers and decision makers. It will raise the standard of EEs reporting for AI technologies as their presence in healthcare proliferates.
Extracting scientifically accurate terminology from an EU public health regulation is part of the knowledge engineering work at the European Centre for Disease Prevention and Control (ECDC). ECDC ...operates information systems at the crossroads of many areas - posing a challenge for transparency and consistency. Semantic interoperability is based on the Terminology Server (TS). TS value sets (structured vocabularies) describe shared domains as "diseases", "organisms", "public health terms", "geo-entities" "organizations" and "administrative terms" and others. We extracted information from the relevant EC Implementing Decision on case definitions for reporting communicable diseases, listing 53 notifiable infectious diseases, containing clinical, diagnostic, laboratory and epidemiological criteria. We performed a consistency check; a simplification - abstraction; we represented lab criteria in triplets: as 'y' procedural result /of 'x' organism-substance/on 'z' specimen and identified negations. The resulting new case definition value set represents the various formalized criteria, meanwhile the existing disease value set has been extended, new signs and symptoms were added. New organisms enriched the organism value set. Other new categories have been added to the public health value set, as transmission modes; substances; specimens and procedures. We identified problem areas, as (a) some classification error(s); (b) inconsistent granularity of conditions; (c) seemingly nonsense criteria, medical trivialities; (d) possible logical errors, (e) seemingly factual errors that might be phrasing errors. We think our hypothesis regarding room for possible improvements is valid: there are some open issues and a further improved legal text might lead to more precise epidemiologic data collection. It has to be noted that formal representation for automatic classification of cases was out of scope, such a task would require other formalism, as e.g. those used by rule-based decision support systems.
Infectious disease threat events (IDTEs) are increasing in frequency worldwide. We analyzed underlying drivers of 116 IDTEs detected in Europe during 2008-2013 by epidemic intelligence at the ...European Centre of Disease Prevention and Control. Seventeen drivers were identified and categorized into 3 groups: globalization and environment, sociodemographic, and public health systems. A combination of >2 drivers was responsible for most IDTEs. The driver category globalization and environment contributed to 61% of individual IDTEs, and the top 5 individual drivers of all IDTEs were travel and tourism, food and water quality, natural environment, global trade, and climate. Hierarchical cluster analysis of all drivers identified travel and tourism as a distinctly separate driver. Monitoring and modeling such disease drivers can help anticipate future IDTEs and strengthen control measures. More important, intervening directly on these underlying drivers can diminish the likelihood of the occurrence of an IDTE and reduce the associated human and economic costs.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, ODKLJ, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Real-world data and real-world evidence (RWE) are becoming more important for healthcare decision making and health technology assessment. We aimed to propose solutions to overcome barriers ...preventing Central and Eastern European (CEE) countries from using RWE generated in Western Europe.
To achieve this, following a scoping review and a webinar, the most important barriers were selected through a survey. A workshop was held with CEE experts to discuss proposed solutions.
Based on survey results, we selected the nine most important barriers. Multiple solutions were proposed, for example, the need for a European consensus, and building trust in using RWE.
Through collaboration with regional stakeholders, we proposed a list of solutions to overcome barriers on transferring RWE from Western Europe to CEE countries.
Medical concept representation: the years beyond 2000 Balkanyi, Laszlo; Schulz, Stefan; Cornet, Ronald ...
Proceedings of Studies in Health Technology & Informatics, vol. 192,
2013, Letnik:
192
Journal Article, Conference Proceeding
Recenzirano
This work aims at understanding the state of the art in the broad contextual research area of "medical concept representation". Our data support the general understanding that the focus of research ...has moved toward medical ontologies, which we interpret as a paradigm shift. Both the opinion of socially active groups of researchers and changes in bibliometric data since 1988 support this opinion. Socially active researchers mention the OBO foundry, SNOMED CT, and the UMLS as anchor activities.
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