UNDERSTANDING MACHINE LEARNING–DRIVEN PREDICTIVE MODELS IN HEALTHCARE

Abstract

In high-stakes fields like healthcare, it's essential for machine learning (ML) models to be interpretable, meaning their predictions are easily understood and explained to end-users. This transparency allows healthcare professionals to make informed, data-driven decisions, leading to more personalized care and a higher quality of service. Interpretability methods can be broadly categorized into two groups. The first group provides local interpretability, focusing on explaining individual predictions, while the second group offers global interpretability, summarizing the model's behavior across an entire population. Another way to classify these approaches is by their dependency on the model itself. Model-specific techniques are tailored for a particular type of model, like a neural network, whereas model-agnostic methods can be used to interpret predictions from any ML model. This overview explores various interpretability approaches for structured data and provides practical examples of their application in healthcare. We'll discuss how these methods can be used to predict health outcomes, optimize treatment plans, and improve the efficiency of screening for specific conditions. Finally, we'll outline future directions for interpretable ML, emphasizing the need for new algorithmic solutions that can facilitate reliable, ML-driven decision-making in critical healthcare scenarios.