Related Experiment Video
Updated: Mar 8, 2026
A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
Published on: September 20, 2018
Knowledge graphs based on meta-analysis papers improve the quality of case formulation: a mixed methods design
Kenji Yokotani1,2, Yasumitsu Jikihara3, Kohei Koiwa4
1Minamijosanjimacho 1-1, Tokushima University, 1-1, Minamijosanjima-cho,, Tokushima, JP.
Background:
Case formulation (CF) is a core skill for therapists; however, creating high-quality CF requires considerable time.
Objective:
This study demonstrates that providing a knowledge graph based on the meta-analytic literature can enhance CF quality.
Methods:
Five groups were established, including four large language model (LLM) groups and one human expert group, each generating 25 CFs based on 25 vignettes. The Control group with Claude Sonnet 3.7 produced 25 CFs. The Personalization group served as the control group with additional personalization prompts. The Knowledge Graph group employed an LLM that generated 25 CFs, which was provided with a meta-analysis Knowledge Graph. Further incorporation of additional personalization prompts then comprised the Knowledge Graph with Personalization group. Finally, the Expert Group consisted of 25 CFs generated by a human expert. These 125 CFs in total were evaluated for general quality (i.e., correctness, completeness, feasibility, and consistency) using a 7-point scale and 18 essential elements with binary scores (0 or 1) by another human expert. The CFs were also qualitatively analyzed.
Results:
The Knowledge Graph and Knowledge Graph with Personalization groups scored significantly higher than the control group in terms of correctness, completeness, and feasibility. The Expert group scored significantly higher on consistency than the machine-generated groups. Additionally, there was no significant difference in the feasibility scores between the Knowledge Graph, Knowledge Graph with Personalization, and expert groups. The qualitative evaluation suggested that human CFs narrow the text to content that is easy for the client to read, whereas machine CFs are more likely to include expressions that are unnatural to the client.
Conclusions:
These results indicate that providing knowledge graphs to novice therapists increases the correctness, completeness, and feasibility of CF. Providing experienced therapists with knowledge graphs is suggested to improve the quality of their CF and mental health services.
Clinicaltrial:
None.
More Related Videos
07:35A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
Published on: October 13, 2023
05:47Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
Related Concept Videos
01:15Methods of Documentation VI: Case Management Model
For example, a patient with a chronic...
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
01:13Patient-centered Care
01:10Documentation of Nursing Diagnosis
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters...
01:24Kaplan-Meier Approach
01:25Strategies for Assessing and Addressing Confounding
Confounding can be addressed at both the design phase of a study and through analytical methods after data...