Multi-Stage Classification Scheme to Optimize Medical Treatments


  • Karen M. Gishyan Institute for Informatics and Automation Problems of NAS RA



Multi-stage classification, Treatment-optimization, Model-ensembling, Machine learning


In most existing machine learning and deep learning settings, classification and regression prediction problems may be described as a process where the model output is based on a single-stage input. In most real-life scenarios achieving the desired medical state for the patient may involve dynamically solving drug prescription problems based on the input data at different stages, where each stage is a logical grouping such as timestep division, ICU stay, etc. Data at a given stage represents a recovery progression and can be fundamentally different from the datasets from the previous and future stages. Although A single model may solve the task, a multi-stage learning procedure may be more suitable. To solve this task, we propose an FNN-driven ensemble-based approach for predicting the medications that the patient should receive at each stage of the recovery process. The final medical discharge location is predicted as a result of sequential predictions of drugs and features. In this work, we combine model ensembling and multi-stage iterative learning for solving an optimal drug prescription generation task as a contribution to the existing literature.


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How to Cite

Gishyan, K. M. (2023). Multi-Stage Classification Scheme to Optimize Medical Treatments. Mathematical Problems of Computer Science, 60, 40–51.