Random feature masking makes medical AI more robust and interpretable

What is it about?

Early detection of dementia is vital for effective treatment, but it requires analyzing complex patient history over time. Real medical data is often messy, with missing values and irregular visit times. Many current Artificial Intelligence models are fragile because they learn to rely too heavily on just one or two specific test scores. If those specific scores are missing, then the model fails. We use a training strategy called random feature masking to address this fragility. We train the AI by deliberately obscuring different parts of the input data during the learning process. This forces the model to look for patterns across a wider variety of health indicators instead of fixating on a single feature. We applied this method to the WHIMS dataset using advanced neural network architectures. We found that this approach significantly improves the model's resilience to missing data and helps doctors understand which features drive the diagnosis.

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Why is it important?

This work addresses a major barrier to deploying AI in healthcare which is the variable quality of real-world clinical data. While many models perform well on perfect datasets, they often fail when faced with the gaps found in actual electronic health records. Our findings show that forcing a model to learn with incomplete data actually makes it stronger. The unique contribution of this study is demonstrating that random masking does more than just improve accuracy. It also enhances clinical trust. By preventing the AI from over-relying on dominant features, we ensure the model uses a balanced set of evidence to make a prediction. This is a critical step toward creating diagnostic tools that are robust enough for doctors to use safely in everyday practice.

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