Apparatus and method for controlling pharmaceutical mixer based on similar clinical trial data extracted by machine learnings

A computer-implemented method for controlling a pharmaceutical mixer based on similar clinical trial data extracted by machine learnings, the method comprising:

• collecting a set of clinical trial data from a database;
• determining a type of each clinical trial data of the set of clinical trial data;
• preprocessing the set of clinical trial data according to the type of each clinical trial data;
• generating a first vector set using metadata of the set of clinical trial data according to the type of each clinical trial data;
• training a learning model in a first stage using the first vector set;
• generating a second vector set by tokenizing words extracted from the set of clinical trial data;
• training the learning model in a second stage using the second vector set;
• when clinical trial data is received from a user terminal, determining a type of the clinical trial data;
• generating a vector using each piece of metadata of the clinical trial data;
• generating a vector by tokenizing words extracted from the clinical trial data according to the type of the clinical trial data;
• inputting the vector to the pretrained learning model and calculating a distance between a prestored vector in the learning model and the vector;
• measuring a similarity grade according to the distance between the vectors;
• extracting clinical trial data having a similarity grade which is lower than or equal to a predetermined grade;
• and
• transmitting a control signal to the pharmaceutical mixer based on the extracted clinical trial data,
• wherein the generating of the vector by tokenizing the words extracted from the clinical trial data according to the type of the clinical trial data comprises:
• when the type of the clinical trial data is unstructured data, deleting predetermined clinical non-use words from clinical title data and extracting words from the clinical title data from which the predetermined clinical non-use words are deleted on the basis of a blank;
• performing morpheme analysis on each of the words and generating tokens each of which includes a pair of a word and a morpheme value and is assigned a label indicating a frequency;
• and
• generating a documentary word matrix by giving a different weight to each of the tokens according to words and labels of the tokens,
• wherein the generating of the documentary word matrix by giving the different weight to each of the tokens according to the words and labels of the tokens comprises:
• decomposing the documentary word matrix into a first matrix having a size of (the number of pieces of clinical trial data×k which is the number of topics) and a second matrix having a size of (k which is the number of topics×the number of words) through a non-negative matrix factorization machine learning algorithm;
• and
• updating the first matrix and second matrix by clustering the clinical trial data and each of the words into any one of the k topics.