Stephanie Lanius,Erina Ghosh,Emma Holdrich Schwager,Larry James Eshelman
申请号：
US16840856
公开号：
US20200320391A1
申请日:
2020.04.06
申请国别(地区):
US
年份:
2020
代理人:
摘要：
A method for training a baseline risk model, including: pre-processing input data by normalizing continuous variable inputs and producing one-hot input features for categorical variables； providing definitions for clean input data and dirty input data based upon various input data related to a patient condition； segmenting the input data into clean input data and dirty input data, wherein the clean input data includes a first subset and a second subset, where the first subset and the second subset include all of the clean input data and are disjoint； training a machine learning model using the first subset of the clean data； and evaluating the performance of the trained machine learning model using the second subset of the clean input data and the dirty input data.
