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Related Experiment Video

Updated: Aug 24, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

521

Empowering digital pathology applications through explainable knowledge extraction tools.

Stefano Marchesin1, Fabio Giachelle1, Niccolò Marini2

  • 1Department of Information Engineering, University of Padua, Padua, Italy.

Journal of Pathology Informatics
|October 21, 2022
PubMed
Summary
This summary is machine-generated.

We developed an unsupervised system, the Semantic Knowledge Extractor Tool (SKET), to unlock valuable medical knowledge from free-text cancer reports. This approach enhances data mining and precision medicine by extracting critical information accurately.

Keywords:
Clinical practiceDigital pathologyExpert systemsExplainable AIKnowledge extractionMachine learning

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Area of Science:

  • Computational linguistics
  • Medical informatics
  • Artificial intelligence in healthcare

Background:

  • Vast amounts of medical data, particularly cancer reports, exist primarily as unstructured free text.
  • This free text contains significant, yet largely unexploited, medical knowledge.
  • Existing methods struggle to efficiently extract actionable insights from this data.

Purpose of the Study:

  • To develop an unsupervised system for extracting medical knowledge from free-text pathology reports.
  • To demonstrate the effectiveness of combining rule-based systems with pre-trained Machine Learning (ML) models for knowledge extraction.
  • To introduce a visually explanatory tool (SKET X) for understanding the system's predictions.

Main Methods:

  • Developed the Semantic Knowledge Extractor Tool (SKET), an unsupervised system.
  • Integrated rule-based expert systems with pre-trained Machine Learning (ML) models.
  • Created SKET eXplained (SKET X), a web-based system for visual explanation of SKET's algorithmic decisions.

Main Results:

  • Achieved high accuracy in knowledge extraction by combining rule-based techniques and pre-trained ML models.
  • Demonstrated the viability of unsupervised Natural Language Processing (NLP) for extracting critical information from cancer reports.
  • SKET provides a practical approach to exploiting textual and multimodal medical information.

Conclusions:

  • Unsupervised NLP techniques, like SKET, can effectively decode medical knowledge embedded in pathology reports.
  • This technology opens new avenues for data mining, precision medicine, structured reporting, and multimodal learning.
  • SKET X aids domain experts in understanding and validating extracted knowledge, fostering trust and further development.