Pathogenicity determination device, pathogenicity determination method, machine learning method, and learned model generation method

A pathogenicity determination method executed by a processor of a pathogenicity determination device, comprising:

• receiving, from an input device of the pathogenicity determination device, inputs of genetic mutation information indicating a genetic mutation, and genetic mutation-related information related to the genetic mutation information;
• estimating a first score related to presence or absence of a pathological significance of the genetic mutation and a second score related to strength or sufficiency of evidence related to the genetic mutation, based on the genetic mutation information and the genetic mutation-related information;
• and
• outputting the estimated first score and the estimated second score to an output device.

A machine learning method of a learning model applied to a pathogenicity determination device that determines a pathological significance of a genetic mutation,

• the machine learning method comprising:
• causing a processor to acquire genetic mutation information indicating the genetic mutation and genetic mutation-related information related to the genetic mutation information;
• causing the processor to acquire correct answer data of a first score related to presence or absence of a pathological significance of the genetic mutation and a second score related to strength or sufficiency of evidence related to the genetic mutation;
• and
• causing the processor to learn the learning model using the genetic mutation information, the genetic mutation-related information, and the correct answer data as learning data.

A learned model generation method to be applied to a pathogenicity determination device that determines a pathological significance of a genetic mutation,

• the learned model generation method comprising:
• causing a processor to acquire genetic mutation information indicating the genetic mutation and genetic mutation-related information related to the genetic mutation information;
• causing the processor to acquire correct answer data of a first score related to presence or absence of a pathological significance of the genetic mutation and a second score related to strength or sufficiency of evidence related to the genetic mutation;
• and
• causing the processor to construct the learned model such that the first score and the second score are estimated using the genetic mutation information, the genetic mutation-related information, and the correct answer data as learning data, and using the genetic mutation information and the genetic mutation-related information as inputs.