ArticleBrain informatics2016
Interactive knowledge discovery with the doctor-in-the-loop: a practical example of cerebral aneurysms research.
Article in Brain informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Explainability and causability in digital pathology.The journal of pathology. Clinical research · 2023Review
- [Application of deep learning in cancer prognosis prediction model].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2020Article
- Diagnostic accuracy of content-based dermatoscopic image retrieval with deep classification features.The British journal of dermatology · 2019Article
- Artificial intelligence for precision oncology: beyond patient stratification.NPJ precision oncology · 2019Review
- Temporal bone radiology report classification using open source machine learning and natural langue processing libraries.BMC medical informatics and decision making · 2016Article
- Virtual autopsy: Machine Learning and AI provide new opportunities for investigating minimal tumor burden and therapy resistance by cancer patients.Autopsy & case reportsArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Established process models for knowledge discovery find the domain-expert in a customer-like and supervising role. In the field of biomedical research, it is necessary to move the domain-experts into the center of this process with far-reaching consequences for both their research output and the process itself. In this paper, we revise the established process models for knowledge discovery and propose a new process model for domain-expert-driven interactive knowledge discovery. Furthermore, we present a research infrastructure which is adapted to this new process model and demonstrate how the domain-expert can be deeply integrated even into the highly complex data-mining process and data-exploration tasks. We evaluated this approach in the medical domain for the case of cerebral aneurysms research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.