Systems and methods for imputing missing values in data sets

A system comprising:

• a computer readable medium including a data set with data stored in rows and columns,
• wherein each of the rows is associated with one individual patient, and
• wherein each of the columns is associated with one type of data for patients;
• and
• one or more processors configured to:
• (i) determine initial values for missing values, respectively, of a row;
• (ii) generate a mathematical model for each column based on the data in one or more of the other columns;
• (iii) for a column with a missing value in the row, predict the missing value for the column using the mathematical model of that column and values of the other columns in the row;
• (iv) replace the missing value in the row with the prediction of the missing value;
• (v) update the mathematical models after (iv);
• (vi) determine an error metric based on the values of the row including the prediction of the missing value;
• and
• (vii) repeat (iii)-(vi) until the error metric converges;
• and
• (viii) after the error metric converges, fill missing values in the dataset using the mathematical models.

A method comprising:

• obtaining, from a computer readable medium, a data set with data stored in rows and columns,
• wherein each of the rows is associated with one individual patient, and
• wherein each of the columns is associated with one type of data for patients;
• and
• by one or more processors:
• (i) determining initial values for missing values, respectively, of a row;
• (ii) generating a mathematical model for each column based on the data in one or more of the other columns;
• (iii) for a column with a missing value in the row, predicting the missing value for the column using the mathematical model of that column and values of the other columns in the row;
• (iv) replacing the missing value in the row with the prediction of the missing value;
• (v) updating the mathematical models after (iv);
• (vi) determining an error metric based on the values of the row including the prediction of the missing value;
• and
• (vii) repeating (iii)-(vi) until the error metric converges;
• and
• (viii) after the error metric converges, filling missing values in the dataset using the mathematical models.