Algorithm 1 Embedding-based K-nearest Neighborhood Sampling Contrastive Learning

$$\begin{array}{cc}\hfill & \mathrm{Input}:\text{Longitutinal EHR features}\hfill \\ \hfill & \mathrm{Output}:\text{Pretrained systemm with parameters tuned}\hfill \\ \hfill & 1:\text{Initialize system parameters}\theta \hfill \\ \hfill & 2:\mathbf{for}\text{each epoch}\mathbf{do}\hfill \\ \hfill & 3:\text{For}\text{each}\text{class}\text{group}\text{(label}1\text{or}0),\text{Compute}\text{similarities}\hfill \\ \hfill & \text{between all pairs of embedding feature representations based}\hfill \\ \hfill & \text{on their inner product},\text{and build KNN graph from it}.\hfill \\ \hfill & 4:\mathbf{while}\text{not converged}\mathbf{do}\hfill \\ \hfill & 5:\text{Sample a mini-batch training patients}P\in P_{all}\hfill \\ \hfill & 6:\mathbf{for}\text{each}p\in P\mathbf{do}\hfill \\ \hfill & 7:\text{Sample}1‘‘\text{positive}"\text{sample data}{p}_k^{+}\in P_{all}\text{that have}\hfill \\ \hfill & \text{the same label as}p,\text{and are connected to node}p\text{in the}\hfill \\ \hfill & \text{KNN graph}.\hfill \\ \hfill & 8:\mathbf{end}\mathbf{for}\hfill \\ \hfill & 9:\text{Optimize}L\text{in equation}3\hfill \\ \hfill & 10:\mathbf{end}\mathbf{while}\hfill \\ \hfill & 11:\mathbf{end}\mathbf{for}\hfill \\ \hfill & 12:\mathbf{return}\text{Pre-trained deep learning system}\hfill \end{array}$$