LLM4EHR and Medical AI Data Alignment: Engineering Efficiency Questions in a Puzzle Game

It's like assembling scattered puzzle pieces on the floor into a complete picture; each piece has a different shape and color, but they must fit together seamlessly. The relationship between clinical time series (heart rate, blood pressure, oxygen saturation) and medical event sequences (diagnoses, medications, surgeries) is exactly this kind of heterogeneous data alignment problem. The work LLM4EHR attempts to use Large Language Models as glue to bond these two types of data together. My judgment is: this direction is worth investing in, but the ROI depends on whether the team can bring data engineering costs down to a manageable range while simultaneously solving the fatal risk of model hallucinations in medical scenarios.