首页|University of Manchester Reports Findings in Machine Learning (Clinicosocial det erminants of hospital stay following cervical decompression: A public healthcare perspective and machine learning model)
University of Manchester Reports Findings in Machine Learning (Clinicosocial det erminants of hospital stay following cervical decompression: A public healthcare perspective and machine learning model)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news originating from Manchester, United Kin gdom, by NewsRx correspondents, research stated, "Post-operative length of hospi tal stay (LOS) is a valuable measure for monitoring quality of care provision, p atient recovery, and guiding hospital resource management. But the impact of pat ient ethnicity, socio-economic deprivation as measured by the indices of multipl e deprivation (IMD), and pre-existing health conditions on LOS post-anterior cer vical decompression and fusion (ACDF) is under-researched in public healthcare s ettings."
ManchesterUnited KingdomEuropeCybo rgsEmerging TechnologiesHealth and MedicineHospitalsMachine LearningPu blic Health
NETL
NSTL
