Abstract: A method for providing an explanation for patient state prediction and an electronic apparatus therefor are disclosed. The method includes acquiring time-series data on a patient, acquiring, from an artificial intelligence-based prediction model, a prediction result that is output by the prediction model based on the time-series data, acquiring, from an explainable artificial intelligence-based explanation model, explanation data that is output by the explanation model based on the time-series data and the prediction result, inputting input data including the time-series data, the prediction result, and the explanation data to an artificial intelligence-based language model, and acquiring, from the language model, natural language text data that is output by the language model based on the input data and includes a reasoning basis of the prediction model for the prediction result.

Abstract: Provided are a method for predicting a mortality risk or a sepsis risk, implemented by a processor, to predict an emergency situation, and a device using the same. The method for predicting a mortality risk or a sepsis risk includes: receiving biological signal data for a subject from a biological signal prediction device; generating a risk sequence for the subject based on the biological signal data, by using a risk sequence generation model configured to generate a risk sequence based on the biological signal data; and predicting a risk for the subject based on the risk sequence.

Abstract: Disclosed herein are an apparatus and method for task-adaptive neural network retrieval based on meta-contrastive learning. The apparatus for task-adaptive neural network retrieval based on meta-contrastive learning includes: memory configured to store a database including a learning model pool consisting of a plurality of datasets and neural networks pre-trained on the datasets and also store a program for task-adaptive neural network retrieval based on meta-contrastive learning; and a controller configured to perform task-adaptive neural network retrieval based on meta-contrastive learning by executing the program. In this case, the controller learns a cross-modal latent space for datasets and neural networks trained on the datasets by calculating the similarity between each dataset and a neural network trained on the dataset while considering constraints included in any one task previously selected from the database, thereby retrieving an optimal neural network.