Objective The purpose of our research was to develop efficient utilization of Deep Learning to the list-type data in which medical characteristics are arranged. Materials and Methods We conducted a survey of blood donors, focusing on the rare adverse reaction of falling. In addition to all cases of fainting, we randomly selected a control group of donors who did not fall. This data was then converted into a two-dimensional format suitable for analysis with a convolutional neural network (CNN). We used an under sampling method to randomly divide the dataset into training and testing sets. Finally, we used the CNN and a logistic regression model to predict the probability of fainting, calculate anomaly scores, and rank the risk of falling. Results The convolutional neural network (CNN) identified 3 out of 10 falls among the group with the top 1% of anomaly scores. In contrast, the logistic regression model failed to identify any falls within the same top 1% anomaly score group. Conclusion Applying information converted into two-dimensional data to Deep Learning by using anomaly detection together was useful to narrow down with high-risk group. Although these findings require validation in a larger and more diverse population, the success of this approach in predicting falls after blood donation suggests its potential for predicting other rare adverse events in healthcare, such as adverse drug reactions, complications from medical procedures, or even disease outbreaks.

The authors have declared no competing interest.

This study did not receive any funding

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The ethics committee of the Japanese Red Cross Society, chaired by Shuichi Kino, approved this study (Ethical review number: 2024-016), and the consent to study participation was obtained in the form of opt-out on the webpage

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