Unbiased Representation of Clinical Data for Precise Patient Outcome Prediction
Precise and unbiased representation of patient data is essential for capturing clinical data signatures that are critical for accurate patient outcome prediction. However, clinical data are often biased due to factors such as patient demographics, socioeconomic status, and healthcare provider biases. To tackle this problem, researchers are developing machine learning algorithms and statistical models that can identify and mitigate data bias. In today's talk, we will explore several end-to-end approaches focused on data embedding and encoding that can be used to achieve precise and unbiased representation of patient data. These approaches may improve healthcare outcomes by enabling more accurate patient outcome prediction and personalized care.
