An aberration detection-based approach for sentinel syndromic surveillance of COVID-19 and other novel influenza-like illnesses.

Autor: Wen A; Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA., Wang L; Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA., He H; Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA., Liu S; Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA., Fu S; Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA., Sohn S; Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA., Kugel JA; Advanced Analytics Service Unit, Department of Information Technology, Mayo Clinic, Rochester, MN, USA., Kaggal VC; Advanced Analytics Service Unit, Department of Information Technology, Mayo Clinic, Rochester, MN, USA., Huang M; Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA., Wang Y; Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA., Shen F; Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA., Fan J; Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA. Electronic address: Fan.Jung-Wei@mayo.edu., Liu H; Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA. Electronic address: Liu.Hongfang@mayo.edu.

Publikováno v: Journal of biomedical informatics [J Biomed Inform] 2021 Jan; Vol. 113, pp. 103660. Date of Electronic Publication: 2020 Dec 13.