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BEGIN:VEVENT
DTSTAMP:20260513T050459Z
DTSTART;VALUE=DATE-TIME:20190121T130000
DTEND;VALUE=DATE-TIME:20190121T140000
SUMMARY:Sebastian Vollmer (Turing / ĢĒŠÄTV)
TZID:Europe/London
UID:20190121-8a1785d86850d8d901689fbed18d4632@warwick.ac.uk
CREATED:20190117T182453Z
DESCRIPTION:Predicting risk of hospital admission and readmission of Scot
 tish population Scottish Patients at Risk of Readmission and Admission (
 SPARRA) is a risk prediction tool initially developed by NHS National Se
 rvices Scotland in 2006 to predict an individualā€™s risk of emergency hos
 pital inpatient admission over the next twelve months. The current versi
 on of SPARRA (released in 2012) uses hospital inpatient admissions data 
 to calculate a risk score for those patients who have had an emergency a
 dmission in the previous three years. Scores are calculated quarterly an
 d disseminated to NHS Goards\, Community Health Partnerships (CHPs)\, an
 d GP Practices. Currently SPARRA is mainly used for ā€œcase findingā€ i.e.h
 elping to identify patients with complex care needs who are likely to be
 nefit most from anticipatory health care. Risk scores are calculated for
  around 4.2m patients monthly and accessed by authorised healthcare prof
 essionals. This talk looks at updating the underlying machine learning m
 odel. Measuring Sample Quality with diffusions Standard Markov chain Mon
 te Carlo diagnostics\, like effective sample size\, are ineffective for 
 biased sampling procedures that sacrifice asymptotic correctness for com
 putational speed. Recent work addresses this issue for a class of strong
 ly log-concave target distributions by constructing a computable discrep
 ancy measure based on Steinā€™s method that provably determines convergenc
 e to the target. We generalize this approach to cover any target with a 
 fast-coupling Ito diffusion by bounding the derivatives of Stein equatio
 n solutions in terms of Markov process coupling times [Gorham et al.\, 2
 016]. As example applications\, we develop computable and convergence-de
 termining diffusion Stein discrepancies for log-concave\, heavy-tailed\,
  and multimodal targets and use these quality measures to select the hyp
 erparameters of biased samplers\, compare random and deterministic quadr
 ature rules\, and quantify bias-variance tradeoffs in approximate Markov
  chain Monte Carlo. Our explicitmultivariate Stein factor bounds may be 
 of independent interest.
LOCATION:PS0.17 Physical Sciences
CATEGORIES:
LAST-MODIFIED:20190117T182453Z
ORGANIZER;CN="":
END:VEVENT
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