A prediction model generation device comprising: a learning data input unit that inputs a plurality of texts representing contents of free conversations conducted by a plurality of patients whose severity of dementia is known, respectively, as learning data; an element extraction unit that analyzes morphemes of the plurality of texts input by the learning data input unit as the learning data, and extracts at least one of a plurality of words and a plurality of parts of speech as a plurality of decomposition elements from the plurality of texts, each in association with an appearance frequency; a text vector computation unit that converts each of the plurality of texts into a q-dimensional vector, wherein q is an arbitrary integer of 2 or more, according to a predetermined rule, thereby computing a plurality of text vectors including q axis components; an element vector computation unit that converts each of the plurality of decomposition elements into a q-dimensional vector according to a predetermined rule, thereby computing a plurality of element vectors including q axis components; an index value computation unit that obtains each of inner products of the plurality of text vectors and the plurality of element vectors, thereby computing relationship index values reflecting a relationship between the plurality of texts and the plurality of decomposition elements; and a prediction model generation unit that generates a prediction model which is machine-learned so that a value indicating the severity of the dementia is output when a text index value group including a plurality of relationship index values for one text is input using the relationship index values computed by the index value computation unit , wherein the value indicating the severity of the dementia is a value of a score of a mini-mental state examination or a value indicating a category classified by a number larger than 2 and less than a maximum value of the score of the mini-mental state examination. a learning data input unit that inputs a plurality of texts representing contents of free conversations conducted by a plurality of patients whose severity of dementia is known, respectively, as learning data; an element extraction unit that analyzes morphemes of the plurality of texts input by the learning data input unit as the learning data, and extracts at least one of a plurality of words and a plurality of parts of speech as a plurality of decomposition elements from the plurality of texts, each in association with an appearance frequency; a text vector computation unit that converts each of the plurality of texts into a q-dimensional vector, wherein q is an arbitrary integer of 2 or more, according to a predetermined rule, thereby computing a plurality of text vectors including q axis components; an element vector computation unit that converts each of the plurality of decomposition elements into a q-dimensional vector according to a predetermined rule, thereby computing a plurality of element vectors including q axis components; an index value computation unit that obtains each of inner products of the plurality of text vectors and the plurality of element vectors, thereby computing relationship index values reflecting a relationship between the plurality of texts and the plurality of decomposition elements; and a prediction model generation unit that generates a prediction model which is machine-learned so that a value indicating the severity of the dementia is output when a text index value group including a plurality of relationship index values for one text is input using the relationship index values computed by the index value computation unit , wherein the value indicating the severity of the dementia is a value of a score of a mini-mental state examination or a value indicating a category classified by a number larger than 2 and less than a maximum value of the score of the mini-mental state examination. <EOS>