Abstract: An information processing apparatus includes at least one processor, in which the processor is configured to: for a machine learning model that uses a document data group including a plurality of document data as input and outputs output data, derive an evaluation value in the machine learning model for each document data included in the document data group.

Abstract: An information processing apparatus including: plural models including at least a first model that uses first data as input to carry out a first prediction task and a second model that uses second data as input to carry out a second prediction task different from the first prediction task, and at least one processor, in which the processor is configured to: acquire information data representing information related to a specific target; select a selection model to be used for prediction from among the plural models based on the information data; and derive a degree of importance to the specific target for each item included in the information data by using the selection model.

Abstract: An aspect of the present invention allows for more accurately identifying a possible lesion in a human lung field. The aspect of the present invention includes an image obtaining section configured to obtain a chest cross-sectional image of a subject, a segmentation section configured to classify, into a plurality of segments, unit elements of the chest cross-sectional image, and an image dividing section configured to divide the chest cross-sectional image into a plurality of regions. A data deriving section is configured to derive data associated with the possible lesion, the data being derived on the basis of a segment of unit elements in the each region among the plurality of segments. An identifying section is configured to output an identification result, which is a result of identification of the possible lesion in the lung field of the subject.

Abstract: An aspect of the present invention allows for more accurately identifying a possible lesion in a human lung field. The aspect of the present invention includes: an image obtaining section configured to obtain a chest cross-sectional image of a subject; a segmentation section configured to classify, into a plurality of segments, unit elements of the chest cross-sectional image; an image dividing section configured to divide the chest cross-sectional image into a plurality of regions; a data deriving section configured to derive data associated with the possible lesion, the data being derived on the basis of a segment of unit elements in the each region among the plurality of segments; and an identifying section configured to output an identification result, which is a result of identification of the possible lesion in the lung field of the subject.