feat: Implement time-aware GPT-2 for patient event prediction

This commit introduces a complete framework for training a temporal GPT-2 model on sequential patient event data.

Key components include:

- `models.py`:
  - `TimeAwareGPT2`: A custom GPT-2 model that incorporates temporal information through a time-based causal attention mask and a sinusoidal age encoding for positional information.
  - `AgeSinusoidalEncoding`: A module for creating time-based positional embeddings.
  - `CombinedLoss`: A two-part loss function combining cross-entropy for event prediction and a survival loss for event timing.

- `utils.py`:
  - `PatientEventDataset`: A PyTorch Dataset class to process, batch, and load patient event sequences, including imputation of "no event" gaps and padding/truncation.

- `train.py`:
  - A comprehensive training script that initializes the model, data loaders, and loss function.
  - Implements a training loop with a cosine annealing learning rate scheduler, validation, and early stopping based on validation loss.

- `prepare_data.py`:
  - Script for preprocessing raw UK Biobank data into a format suitable for the model.

- `GEMINI.md`:
  - Project documentation outlining the structure, coding style, and framework.

GEMINI.md

@@ -0,0 +1,59 @@
+# DeepHealth Project
+
+This is a deep learning project based on PyTorch. This project adheres to specific code style and file structure conventions to ensure clarity, maintainability, and reproducibility.
+
+## 1. Project Structure
+
+To maintain a clean and modular project, we adopt the following file organization:
+
+DeepHealth/
+    |-tain.py
+    |-models.py
+    |-utils.py
+    |-data/
+    |-requirements.txt
+    |-README.md
+
+
+### File Descriptions
+
+* **`train.py`**:
+    * **Core training script**. It contains the control flow for the entire training process.
+    * Responsible for initializing the model, optimizer, DataLoader, etc.
+    * Executes the training and validation loops.
+    * Handles saving and loading checkpoints, logging, and other related tasks.
+
+* **`models.py`**:
+    * **Model and Loss Function Definitions**. This file stores the architecture for all neural network models.
+    * All subclasses of `torch.nn.Module` should be defined in this file.
+    * Custom loss functions should also be implemented here.
+
+* **`utils.py`**:
+    * **Utility Functions Module**. It contains reusable helper functions for the project.
+    * Primarily responsible for data I/O operations, data preprocessing, performance metric calculations, logger configuration, or other logic that doesn't belong in the core model or training framework.
+
+* **`data/`**:
+    * **Data Storage Directory**. Used to store the datasets required for the project.
+    * `data/raw/` stores the original, unprocessed data.
+    * `data/processed/` stores data after it has been preprocessed.
+
+* **`requirements.txt`**:
+    * **Project Dependencies**. Lists all the Python packages and their versions required to run this project.
+
+* **`README.md`**:
+    * **Project Documentation**. Provides a high-level overview of the project, setup instructions, and usage guidelines.
+
+## 2. Core Framework
+
+* **Deep Learning Framework**: `PyTorch`
+
+## 3. Coding Style
+
+This project uniformly adopts the **Google Python Style Guide**. All submitted code should adhere to this standard to ensure consistency and readability.
+
+Key features include:
+* Using `yapf` or `black` for automatic code formatting.
+* Following detailed naming conventions (`module_name`, `package_name`, `ClassName`, `method_name`, `ExceptionName`, `function_name`, `GLOBAL_CONSTANT_NAME`).
+* Using Google-style docstrings.
+
+Please refer to the official documentation: [Google Python Style Guide](http://google.github.io/styleguide/pyguide.html)