mtslearn 0.1.1

The Medical Time-Series Analysis Toolkit mtslearn is designed to process and analyze complex, irregularly sampled medical data.

Medical Time-Series Analysis Toolkit (mtslearn)

The Medical Time-Series Analysis Toolkit mtslearn is designed to process and analyze complex, irregularly sampled medical data. It provides a streamlined pipeline from raw data cleaning and resampling to advanced predictive modeling using both Machine Learning (XGBoost) and Deep Learning (T-LSTM).

🌟 Key Features

• Multi-Format Data Support: Handles both Static and Time-Series data processing workflows.
• Flexible Data Ingestion: Supports both Wide and Long data formats commonly found in clinical electronic health records (EHR).
• Advanced Temporal Modeling: Features T-LSTM (Time-Aware LSTM) to specifically handle irregular time intervals between patient visits.
• Diverse Model Integration: Supports a variety of architectures, from XGBoost and CoxPH for static features to LSTM, T-LSTM, and Transformer for time series features.
• End-to-End Pipeline: Integrated modules for data cleaning, outlier detection, resampling, standardization, and performance evaluation (ROC/Confusion Matrix for static outputs, Error Distributions for temporal outputs).

🛠 Installation

You can now install the toolkit and all its dependencies directly via pip:

pip install mtslearn

🚀 Quick Start

1. Data Loading & Feature Engineering

from mtslearn import StaticProcessor, Static_Static_Classifier

static_processor = StaticProcessor()
static_processor.load_dataset("COVID-19")  # Built-in dataset
static_processor.extract_features(agg_funcs=['mean', 'std', 'max', 'min', 'median'], include_duration=True)

2. Data Preprocessing & Cleaning

X_train_static, X_test_static, y_train_static, y_test_static = static_processor.train_test_split(
    test_size=0.3, shuffle=True, random_state=42, stratify=True
) # data Splitting
X_train_static, X_test_static = static_processor.data_cleaning(
    X_train_static, X_test_static, fill_missing='mean', outlier_method='iqr'
) # data cleaning
# standardization
X_train_static, X_test_static = static_processor.scale_features(X_train_static, X_test_static, method='standardize')

3. Model Training & Evaluation

model = Static_Static_Classifier(model_type='XGB') 
model.fit(X_train_static, y_train_static)
model.evaluate(X_test_static, y_test_static)

For more in-depth examples, refer to 🔗 test.ipynb, which demonstrates the complete workflow for both static and time-series processes.

Documentation

For detailed documentation, including advanced usage, customization options, and examples, refer to the User Guide .

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For questions or issues, please open an issue on GitHub or contact us as [e-mail].