Method for assessing degree of risk of subject and classifying same subject according to same degree of risk, and device using same

A method used by a computing device operating based on a machine learning method, the method comprising:

• acquiring integrated data of the subject, wherein the integrated data is converted from non-numerical patient data of the subject and numerical patient data of the subject;
• applying the integrated data to the machine learning model for criticality assessment of the subject to generate a classification result associated with a criticality of a subject;
• and
• providing information for providing the generated classification result to an external entity,
• wherein the integrated data for the non-numerical patient data is expressed as a first vector of a size N (>1), and each element of the first vector corresponds to a respective one of N first categories related to the non-numerical patient data, and wherein an element of the first vector, corresponding to a first category to which the non-numerical patient data belongs, is set to a positive value, and the other element(s) of the first vector is set to zero,
• wherein the integrated data for the numerical patient data is expressed as a second vector of a size M (>1), and each element of the second vector corresponds to a respective one of M second categories related to the numerical patient data, and wherein an element of the second vector, corresponding to a second category to which the numerical patient data belongs, is set to a positive value, and the other element(s) of the second vector is set to zero, and
• wherein the size M is set to be same as the size N.

A computing device configured to operate based on a machine learning method, the computing device comprising:

• a communication unit;
• and
• a processor configured to control the communication unit and to perform:
• acquiring integrated data of the subject, wherein the integrated data is converted from non-numerical patient data of the subject and numerical patient data of the subject;
• applying the integrated data to the machine learning model for criticality assessment of the subject to generate a classification result associated with a criticality of a subject;
• and
• generating for providing the generated classification result to an external entity,
• wherein the integrated data for the non-numerical patient data is expressed as a first vector of a size N (>1), and each element of the first vector corresponds to a respective one of N first categories related to the non-numerical patient data, and wherein an element of the first vector, corresponding to a first category to which the non-numerical patient data belongs, is set to a positive value, and the other element(s) of the first vector is set to zero,
• wherein the integrated data for the numerical patient data is expressed as a second vector of a size M (>1), and each element of the second vector corresponds to a respective one of M second categories related to the numerical patient data, and wherein an element of the second vector, corresponding to a second category to which the numerical patient data belongs, is set to a positive value, and the other element(s) of the second vector is set to zero, and
• wherein the size M is set to be same as the size N.