Background
Acute pancreatitis is the most common pancreatic disease, with a global incidence of 34 cases per 100,000 individuals. This disease
causes more than 1.5 million new patients per year worldwide, with a mortality that approaches 1%. Biliary acute pancreatitis (BAP)
patients, when admitted to the hospital, can be treated with index cholecystectomy or conservatively. While conservative treatment
can be resolutive, up to 35% of BAP patients have a relapse (RAP) within 30 days, and require emergency surgery in a significantly
worse overall patient condition, reducing the chances of success. Other than that, RAP dramatically increases the chances of chronic
pancreatitis, pancreatic cancer, postoperative complications and overall mortality. RAP episodes have also an economic impact on
healthcare facilities, in that a second and longer hospital admission per patient increases the overall medical cost per patient by at
least 100%. So far, however, there are no standardised methods to predict RAP.

Scope
The MINERVA (Machine learnINg for the rElapse Risk eValuation in Acute biliary pancreatitis) project stems from the need in the
clinical practice of taking an operational decision in patients that are admitted to the hospital with a diagnosis of acute biliary
pancreatitis (BAP).
In particular, the MINERVA project aims to develop a predictive score that allows to assess the risk of hospital readmission for patients diagnosed with mild biliary acute pancreatitis using machine learning and artificial intelligence. The aim of the MINERVA
score is to provide the clinicians a validated and standardized assessment of relapse risk that takes into account the personal
history, demographic data and hematological characteristics of each patient. The MINERVA score will be free and easy to compute
instantly for all medical staff; it will be accessible at any time on the MINERVA website and will provide an immediate and reliable
result that can be a clear indication for the best treatment pathway for the clinician and for the patient.
The MINERVA project aims to reach the following objectives and results:
1) Propose a novel methodology for the assessment of the risk of relapse in patients with Biliary Acute Pancreatitis;
2) Propose a machine learning predictive model using a deep learning architecture applied to data easy to collect from patients;
3) Validate the MINERVA score on an extensive, multicentric, prospective cohort;
4) Allow national and international clinicians, medical staff, researchers and the general audience to freely and easily access the
MINERVA score computation and use it in their daily clinical practice.
The MINERVA score model will be developed on a retrospective cohort of patients and will be validated on a novel prospective
multicentric cohort. After validation, the scoring system and the algorithm will be published and will be freely and easily accessible
for researchers, clinicians and the general public.