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?
System Architecture Learning Tier 1 Clinical Database Trigg WSN Learning Tier 2 - Feedback Warning .Tier 1: EWs (early warning system) Clinical data, lab tests, manually collected, low frequency Tier 2: RDs (real-time data sensing) Body sensor data, automatically collected, wirelessly transmitted, high frequency
System Architecture •Tier 1: EWS (early warning system) • Clinical data, lab tests, manually collected, low frequency •Tier 2: RDS (real-time data sensing) • Body sensor data, automatically collected, wirelessly transmitted, high frequency
Agenda Background and overview Early warning system(EWS) Real-time data sensing(RDS) Future work
Agenda 1 Background and overview 3 Real-time data sensing (RDS) 5 Future work Early warning system (EWS) 2
Medical Record(34 vital signs: pulse, temperature, oXygen saturation, shock index, respirations, age, blood pressure..) -H- Respiration -HI-Temperature 160-吾-Bp.syso Oxygen Satuarion 阜 口--口 都普一---
Medical Record (34 vital signs: pulse, temperature, oxygen saturation, shock index, respirations, age, blood pressure …) Time/second Time/second
Related work Medical data mIning medica machine knowledge learning methods Acute Physiology Score, Chronic Health Score, and Modified Early decision neural SCAP and PSI APACHE score are Warning SVM used to predict Score(MEWS) trees networks renal failures Main problems: Most previous general work uses a snapshot method that takes all the features at a given time as input to a model, discarding the temporal evolving of data
Related Work Main problems : Most previous general work uses a snapshot method that takes all the features at a given time as input to a model, discarding the temporal evolving of data Medical data mining medical knowledge machine learning methods SCAP and PSI Acute Physiology Score, Chronic Health Score , and APACHE score are used to predict renal failures Modified Early Warning Score (MEWS) decision trees neural networks SVM
Overview of ews Goal: Design an data mining algorithm that can automatically identify patients at risk of clinical deterioration based on their existing electronic medical records time-series Challenges Classification of high in different scale dimensional time series data Irregular data gaps 25000 o measurement errors 20000 class imbalance 口Non-cU 15000 口IcU
Overview of EWS Goal: Design an data mining algorithm that can automatically identify patients at risk of clinical deterioration based on their existing electronic medical records time-series. 0 5000 10000 15000 20000 25000 30000 Non-ICU ICU Challenges: • Classification of highdimensional time series data • Irregular data gaps • measurement errors • class imbalance