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 (11A, 11B) 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 (122, 123) 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 (13A, 13B, 13C) 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 (14A, 14B, 14C, 14D, 14E) 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 (13A, 13B, 13C), 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 (11A, 11B) 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 (122, 123) 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 (13A, 13B, 13C) 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 (14A, 14B, 14C, 14D, 14E) 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 (13A, 13B, 13C), 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.