Development of a case database including atypical cases of rare diseases with emphasis on symptoms
and findings using generative AI
Eisuke Dohi1), Itaru Hayakawa2), Tomoyasu Matsubara3), Toyofumi Fujiwara4), Tomoyasu Yamamoto4)
1) National Center of Neurology and Psychiatry, National Institute of Neuroscience, 2) National Center for Child Health and Development, Department ofNeurology 3) Tokushima University Department of Neurology, 4) Database Center for Life Science

BackgroundResults

In the case of intractable and rare diseases, the sheer number of diseases
and the small number of individual cases prevents medical practitioners
from gaining sufficient case experience. Educational resources are also
limited to textbooks with typical cases and case reports biased towardatypical cases, and there is a lack of educational resources to grasp theoverall picture of the disease. Even if all of the published learning resourceswere collected, case reports are biased toward atypical cases, making it
difficult to create a patient distribution that reflects the image of the disease
seen in actual clinical practice.
Therefore, this study aims to create data resources for learning anddiagnostic support by constructing a case database that is similar to realclinical cases using generative AI.

① PDF → text extraction：Ask your PDF pro works well!
② The extraction of symptoms：LLM works well (+/-, time course)

MethodLLM③ Annotation with LLM did not work with HPO….
④ Annotation with corresponding chart works!
Annotated clinical text

corresponding chart
LLM

1: Case data were extracted from case reports PDF and symptoms and findings werecategorized. Case data were annotated with corresponding charts. All the procedure wereconducted with generative AI (LLM such as ChatGPT) except making the corresponding charts.
2: To compensate typical cases,
typical cases will be generated withgenerative AI with typical cases in realcases and case reports. The additional
generated typical cases will be added
to case reports datasets until basic
statics reach previously reported
cohort studies.
3: Case dataset will be visualizedbased on their symptoms and findings
in individual cases to ease tounderstand the atypical case among
the case datasets.

visualization pf typicaland atypical cases

A 13-year-old boy exhibited cervical kyphosisand deteriorating cognition<"demetia">. He
was born normally and achieved
developmental milestones without delay. The
family history was noncontributory. At the ageof 2, he had 2 simple febrile
seizure<"seizure">s. At the age of 8, he had ageneralized febrile convulsion that lasted
about 10 min. Subsequent
electroencephalography (EEG) revealed
bilateral frontal spikes during sleep.
Valproate was started under the diagnosis ofepilepsy. Since then, his school performanceslowly began to deteriorate<"psychomotor
regression">.

Current issues and solusions(1) It is unclear if the HPO does not have term or if the annotation
function is inadequate.
(2) Streamlining the creation of correspondence tables.
(3) Stability o results when using LLM
Organizing the HPO to make it easier for clinicians to understand.
Automate each procedure and clarify where expert help is needed.

Acknowledgments and Fundings
Special thanks to Yuka Tateishi, Terue Takatsuki and our mentors, advisors,and colleagues for their guidance and support.
Kakenhi 22K12253, 23K11886, Rois-DS Joint 2023