Disease Diagnosis On Ships Using Hierarchical Reinforcement Learning

Every year about 30 million people travel by ships world wide often in extreme weather conditions and also in polluted environment due to ship's fuel combustion and many other factors that impacts the health of both passengers and crew staff so there is a need of medical staff but that's not always available so we introduce an a model based on Reinforcement learning(RL) that is used as the key approach in dialogue system.We incorporates Hierarchical reinforcement learning (HRL) model with the layers of Deep Q-Network for dialogue oriented diagnosis system.policy learning is integrated as policy gradients are already defined.We created two stage hierarchical strategy.We used the hierarchical structure with double layer policies for automatic disease diagnosis.Double layer means it splits the task into sub-tasks named as high-state strategy and low level strategy.It has a component called user simulator that communicates with patient for symptom collection low level agent inquire symptoms.Once its done collecting it sends results to high level agent which activates the D-classifier for last diagnosis.When its done its send back by user simulator to patients to verify diagnosis made.Every single diagnosis made has its own reward that trains the system.

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1. This project has been partially funded by the “Programma Nazionale Ricerca, Innovazione e Competitività per la transizione verde e digitale 2021/2027 destinate all’intervento del FCS “Scoperta imprenditoriale” - Azione 1.1.4 “Ricerca collaborativa” - with the project SIAMO (Servizi Innovativi per l’Assistenza Medica a bOrdo) project number F/360124/01- 02/X75.

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2. Thematic Sessions: Short Papers