BEGIN:VCALENDAR
VERSION:2.0
PRODID:researchseminars.org
CALSCALE:GREGORIAN
X-WR-CALNAME:researchseminars.org
BEGIN:VEVENT
SUMMARY:Juan Inda (Chalmers University of Technology & University of Gothe
 nburg)
DTSTART:20230516T111500Z
DTEND:20230516T120000Z
DTSTAMP:20260422T155153Z
UID:gbgstats/27
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/gbgstats/27/
 ">Confidence-based Prediction of Antibiotic Resistance at the Patient-leve
 l Using Transformers</a>\nby Juan Inda (Chalmers University of Technology 
 & University of Gothenburg) as part of Gothenburg statistics seminar\n\nLe
 cture held in MVL15.\n\nAbstract\nRapid and accurate diagnostics of bacter
 ial infections are necessary for efficient treatment of antibiotic-resista
 nt pathogens. Cultivation-based methods\, such as antibiotic susceptibilit
 y testing (AST)\, are slow\, resource-demanding\, and can fail to produce 
 results before the treatment needs to start. This increases patient risks 
 and antibiotic overprescription. Here\, we present a deep-learning method 
 that uses transformers to merge patient data with available AST results to
  predict antibiotic susceptibilities that have not been measured. The meth
 od is combined with conformal prediction (CP) to enable the estimation of 
 uncertainty at the patient-level. After training on three million AST resu
 lts from thirty European countries\, the method made accurate predictions 
 for most antibiotics while controlling the error rates\, even when limited
  diagnostic information was available. We conclude that transformers and C
 P enables confidence-based decision support for bacterial infections and\,
  thereby\, offer new means to meet the growing burden of antibiotic resist
 ance.\n
LOCATION:https://researchseminars.org/talk/gbgstats/27/
END:VEVENT
END:VCALENDAR