Article · PubMed · January 1, 2001
Knowledge discovering from clinical data based on classification tasks solving.
Н. А. Игнатьев, Fatima Аdilova, Gayrat R. Matlatipov, P. P. Chernysh
Abstract
After the abundant literature on normative decision analysis centered on Bayes' rule, the artificial intelligence approach left aside the proposed formalism in favor of symbolic approach. Thus, diagnosis was identified as a dynamic cognitive process, characterized by the search for evidence to test a given hypothesis where heuristic thinking plays a significant role. Approach presented in this study is knowledge discovering system to test a given (manually or automatically) hypothesis, based on three mathematical models of classification: model of classes stability, model of linear envelope, model of multilayer neural network. All the models characterizes as quantitative methods, and works as cognitive tools for new laws searching.
More by Fatima Adilova
- Graph-based density-variation outlier removal for chronic disease prediction: A leakage-free benchmark and component-wise ablationResults in Engineering · 2026
- Monotonic System Framework for Globally Optimal Structuring of Complex Data2026
- Boosting LLM Adaptation: Superior Stability and Convergence via OLoRA+ Across Diverse NLP BenchmarksInternational Siberian workshops and tutorials on electron devices and materials · 2026
- Генерации графов заданной структуры: от глубоких нейронных сетей к квантовым моделям (на примере создания новых лекарств)Problems of Computational and Applied Mathematics · 2026