Machine learning (ML) is transforming the healthcare industry by enhancing the evaluation of treatments through the prediction of treatment impacts on patient outcomes. This methodology, known as causal ML, uses data from various sources including randomized controlled trials, clinical registries, and electronic health records to measure treatment effects. By providing personalized outcome predictions under different treatment scenarios, causal ML facilitates greater precision in patient care.
However, it’s vital to apply causal ML carefully as it is dependent on underlying notions that are not directly verifiable. Researchers from LMU Munich, University of Cambridge, and Harvard Medical School elucidate how this method deviates from traditional medical statistical and ML methodologies. Using these advanced tools can estimate personalized treatment effects from diverse data sources and predict how treatment affects different patients, while addressing factors like genetic data and drug metabolism.
Traditional ML is adept at predicting outcomes without taking treatment effects into account. Causal ML, however, excels at estimating how these treatments can impact outcomes. For instance, it not only predicts the risk of diabetes but also evaluates how certain treatments can alter this risk. This is achieved by exploring ‘what if’ scenarios including predicting survival rates under varying cancer treatments. Unlike traditional statistics that largely rely on known relationships, causal ML handles complex high-dimensional data.
An integral part of applying causal ML is understanding how to distinguish between treatments’ impacts on outcomes rather than simply predicting them. Unlike traditional ML which primarily focuses on risk predictions, causal ML examines changes in outcomes due to diverse treatments. It goes beyond assessing average treatment effects through calculating conditional average treatment effects for specific patient subgroups.
It’s beneficial to select causal ML methods based on the causal question and treatment effect. These methods, such as model-agnostic meta-learners, S-learners, and T-learners, are flexible with any ML model and applicable to both binary and continuous treatment scenarios. Robust checks and careful validation of assumptions are fundamental to reliable results.
In summary, causal ML holds promise to advance personalized medical treatments and patient outcomes by evaluating treatment effects from assorted medical data. It can pinpoint patient subgroups that are likely to benefit the most from specific treatments and scrutinize real-world data. Extensive datasets, reliable software tools, and robust regulatory frameworks are crucial requirements. Bridging the gap between ML advancements and clinical application is the next step. The integration of causal ML into clinical practice can be facilitated through cross-disciplinary cooperation, lending support to decision-making via personalized predictability.
The full research paper can be accessed here.
