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Long-term Prognosis of Antiepilpetic Drug Therapy in People with Newly Diagnosed and Treated Epilepsy

Description 
Antiepileptic drug is the mainstay of treatment modality for epilepsy. People with epilepsy often require lifelong antiepileptic drug treatment. Previous Glasgow study in 2000 demonstrated a-third of the epilepsy patient did not response well to antiepileptic drug therapy. Despite the introduction of more than a dozen new antiepileptic drugs in the past two decades, there remain no robust data to suggest improvement in treatment outcomes in the recent expanded Glasgow study. To valid the prognosis and antiepileptic drug response patterns observed in the Glasgow studies. We will assess treatment outcomes of newly treated epilepsy patients who were seen at a First Seizure Clinic and were prospectively followed up in Australia. We will extract seizure, diagnostic and treatment information from baseline and follow-up clinical documents and construct a digital database. Various methods will be used in modelling treatment outcomes including traditional statistical methods and advanced machine learning approaches. This project is suitable for students with biostatistics and/or computer science background.
Essential criteria: 
Minimum entry requirements can be found here: https://www.monash.edu/admissions/entry-requirements/minimum
Keywords 
Statistics, computer science, machine learning, artificial intelligence, Antiepilpetic Drug, Newly Diagnosed, Newly Treated, Seizure-free, Epilepsyl, physiology, pharmacology, microbiology, anatomy, developmental biology, molecular biology, biochemistry, immunology, human pathology, clinical
School 
Central Clinical School » Neuroscience
Available options 
Masters by research
Honours
BMedSc(Hons)
Time commitment 
Full-time
Physical location 
Alfred Research Alliance
Co-supervisors 
Dr 
Zhibin Chen

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