Abstract:

The clinical professors and their students use the chatbot for the purpose of understanding preeclampsia and its complications. Preeclampsia is a disorder caused by hypertension, along with the damage of internal organs, namely the liver and kidneys. The factors which influence the occurrence of preeclampsia include age, obesity, gestational diabetes, in vitro fertilization, previous history of preeclampsia, multiple gestation, family history of preeclampsia, history of chronic high blood pressure. Because of all these factors which come from clinical professors, the chatbot updates its knowledge base and allows the other clinical professors, medical students and the pregnant women to benefit from using it. Chatbots provide the user, which benefits from them, the ability of receiving replies within minutes, no matter their geographic location, or the device they are using to communicate. Data about preeclampsia is gathered from medical experts, as well as from blood pressure recordings which come from monitored patients, and all of them are stored on the server side of the system. The software provided by the system offers answers and solutions for monitoring and treating pregnant women after combining descriptions of the illness and the symptoms, diagnostics, predictions and prescriptions. The ontology of the system contains information about preeclampsia, other illnesses and the complications which pregnant women may undergo while being pregnant. These notions are stored as RDF triples, containing a subject, an object and a predicate. All the triples are placed inside the RDF type ontology. The triples represent the medical validated knowledge which is checked by clinical professors in order to find out if the notions have been well linked. Recommendations are constructed based on the triples. The learning analytics tool which stands behind the chatbot queries the data using SPARQL based on defined rules and suggests what the chatbot should answer based on the user's input. The user is provided with a set of actions which should be taken to better understand the pregnancy complications and treatments. After applying the set of actions, the feedback of the user is stored and further used for counseling.

Keywords:

Preeclampsia, Collaborative learning, Learning analytics, Ontology.