@PittSTREAM 1 year ago
Study in @PLOSONE by @jennyciganic, PittSTREAM's @walidgellad, et al developed and validated a #machinelearning algorithm to improve prediction of incident opioid use disorder diagnosis among Medicare beneficiaries with ≥1 opioid prescriptions. #opioids https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0235981 https://t.co/LexHO7Nt1Z
@PittCP3 2 years ago
2019 was a big year for #AI in health care. Our team developed #MachineLearning algorithms to predict risk of #opioid overdose among #medicare beneficiaries. Look for more from this project in 2020! @jennyciganic @AJ_Gordon @walidgellad @JAMANetworkOpen https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2728625
@UFCoDES 2 years ago
Presentation by @jennyciganic at #AHSR2019 titled “Using #MachineLearning to Predict Risk of Incident #OpioidUseDisorder among Fee-for- Service #Medicare Beneficiaries" @UFPharmacy @UFCoDES https://t.co/m8WMmBrIAb
@9to5mac.com 2 years ago
Apple to test new digital health API to offer integrated access to patient claims
@theverge.com 4 years ago
What happens when an algorithm cuts your health care
@pmarca 7 years ago
RT @porszag: Note (a) Medicare $126 billion < expected; (b) Med beneficiaries HIGHER than expected; (c) Part D not huge part of explanation (2/2)