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Artificial Intelligence-powered tiered early warning framework addressing high false alarm rates for in-hospital mortality prediction
15 hours ago NONTHABURI, Thailand (AP) — Thailand’s “Death Fest” invites people to confront mortality while also offering practical services and spiritual advice for the inevitable. The unusual fair, now in its second year, has broached the subject that’s uncomfortable for many. It’s also one of the central …
Artificial Intelligence-powered tiered early warning framework addressing high false alarm rates for in-hospital mortality prediction
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Abstracts Alert fatigue remains a major barrier to the effective deployment of predictive models in emergency care, particularly in the context of rare but critical outcomes such as in-hospital mortality (IHM), which often occurs in less than 5.0% of patients admitted from the emergency department (ED). Severe class imbalance leads to low positive predictive value (PPV), undermining the clinical utility of even high-performance predictive models.
By Lijuan Wu, Jinxin Huang, Xinrong He, Anna Khalemsky, Vijaya Kumar, James Paxton, Shan Liu, Gabriele Savioli, Karim Tazarourte, Anna Slagman, Michael Christ, Muhammad Qureshi, Shamai Grossman, Bei Hu Verified, Huajun Wang, Binbin He, Phillip D. Levy, Brian O’Neil, Seth R. Gemme, Lisa Kurland, Eddy Lang, Jinle Lin, Huiying Liang, Xin Li Verified, Abdelouahab Bellou, Liyi Mai, Hongnian Wang, Xueyun Zhan, Dionyssios Tsilimingras, Niels K. Rathlev, Hani Hariri
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Nature
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Negative Conversion Rate of SARS-CoV-2 Infection
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