Real-Time Clinical Warning for Hospitalized Patients via Data Mining(数据挖掘实现的住院病人的实时预警) Department of Computer Science and Engineering Yixin Chen(陈一昕) Yi Mao, Minmin Chen, Rahay dor,Greg ackermann, Zhicheng Yang chengyang lu School of medicine Kelly Faulkner, Kevin Heard, Marin Kollef, Thomas Bailey s Washington University in St Louis
Department of Computer Science and Engineering Yixin Chen （陈一昕）, Yi Mao, Minmin Chen, Rahav Dor, Greg Hackermann, Zhicheng Yang, Chengyang Lu School of Medicine Kelly Faulkner, Kevin Heard, Marin Kollef, Thomas Bailey Real-Time Clinical Warning for Hospitalized Patients via Data Mining （数据挖掘实现的住院病人的实时预警）
Background The icu direct costs per day for survivors is between six and seven times those for non -CU care Unlike patients at ICUs, general hospital wards(GHW) patients are not under extensive electronic monitoring and nurse care Clinical study has found that 4-17% of patients will undergo cardiopulmonary or respiratory arrest while in the GHW of hospital
Background • The ICU direct costs per day for survivors is between six and seven times those for non-ICU care. • Unlike patients at ICUs, general hospital wards (GHW) patients are not under extensive electronic monitoring and nurse care. • Clinical study has found that 4–17% of patients will undergo cardiopulmonary or respiratory arrest while in the GHW of hospital
Project mission Sudden deteriorations(e.g. septic shock, cardiopulmonary or respiratory arrest)of ghw patients can often be severe and life threatening Goal: Provide early detection and intervention based on data mining to prevent these serious, often life threatening events Using both clinical data and wireless body sensor data A NIH-ICTS funded project: currently under clinical trials at Barnes-Jewish Hospital, St Louis, MO
Project mission • Sudden deteriorations (e.g. septic shock, cardiopulmonary or respiratory arrest) of GHW patients can often be severe and life threatening. • Goal: Provide early detection and intervention based on data mining – to prevent these serious, often lifethreatening events. – Using both clinical data and wireless body sensor data • A NIH-ICTS funded project: currently under clinical trials at Barnes-Jewish Hospital, St. Louis, MO
What exactly do we predict Is he going to die?
What exactly do we predict Is he going to die?
What exactly do we predict Is he going to CU?
What exactly do we predict Is he going to ICU?