How is it unique?
AutoPrognosis sets itself apart with its focus on clinical prognosis, offering an easy-to-extend, plugin-able architecture that can automatically learn ensembles of pipelines for classification, regression, or survival analysis tasks on tabular data. It also provides interpretability and uncertainty quantification tools, data imputation using HyperImpute, and the ability to build demonstrators using Streamlit.
How is it useful?
AutoPrognosis can tackle a range of use-cases:
1. Streamline the development of diagnostic and prognostic models by automating the design of predictive modelling pipelines tailored for clinical prognosis.
2. Enhance the decision-making process by offering built-in interpretability and uncertainty quantification tools.
3. Speed up deployment of clinical demonstrators without requiring extensive technical knowledge, promoting adoption of ML techniques in the clinical settings.
4. Provide a flexible and extensible architecture that can be easily customised to address various prognosis tasks and data types.
With AutoPrognosis, decision-makers can harness the power of automated machine learning to develop optimised predictive models for clinical prognosis, driving innovation and improving patient outcomes, while data scientist and researchers can minimise the need for boiler plate code and automate routine model development tasks.