Sepsis Prediction Algorithm Sample Clauses

Sepsis Prediction Algorithm. ‌ In recent years, the increased prevalence of electronic medical records (EMR) has spurned the development of machine learning based surveillance tools for detection or classification [9, 17, 18, 10, 20] and prediction [9, 19, 11, 4] of patients with sepsis or septic shock. However, the real-time implementation of a high-dimensional machine learning model in an ICU environment has not been successfully achieved. In their recent work, Xxxxxx et al. developed the Artificial Intelligence Sepsis Expert (AISE), a modified Weibull-Xxx model that uses an EMR data to predict the onset of sepsis four to six hours in advance with an AUC of 0.85 [19]. The AISE development cohorts contained over 30,000 patients from multiple hospitals in the Emory Healthcare system and was validated using a cohort containing 50,000 patients from the MIMIC-III ICU database [19]. In this work, we developed a software platform, called Artificial Intelligence De- compensation Expert version 1.0 (AIDEx 1.0), to facilitate deployment of the AISE algorithm in a live clinical environment. This novel architecture and the associated user interface (UI) addresses the low clinical tolerance for false-alarms [11] as well as the interpretability and workflow integration requirements necessary for successful clinical decision support (CDS) systems [2, 22, 13]. The AISE algorithm, in con- junction with the AIDEx platform, was designed to make real-time predictions about who is or will become septic at virtually any institution with a modern EMR, afford- ing clinicians the ability to intervene early and drastically reduce the impact of this deadly condition. In this work, we describe our containerize architecture that fetches patients records from a real-time EMR database and displays hourly sepsis risk score for each patient, in addition to the top contributing factors to the risk scores.

Sepsis Prediction Algorithm. The AISE Algorithm Service accepts as its input two input files (a demographics file and a dynamic xxxxx xxxxx and laboratory file) which constitute all the input features to the AISE Algorithm [20]. When deployed the algorithm can alert clinicians 4-6 Figure 2.2: Sepsis risk score prediction platform flow diagram hours before a patient meets the Sepsis-3 criteria. The output of the AISE Algorithm service is a sepsis risk score and the top three factors contributing to the sepsis risk score (see Fig. 3). The data returned by AISE Algorithm service along with all patient features are combined into JSON documents to be stored in the Data Xxxx- house. The Inputs of the AISE model and the machine learning model is explained further with more details. Features used by AISE and the AISE machine learning Model AISE algorithm used high resolution HR and BP time series with 2 second resolution through Bed Master System from all ICU patients 18 years old or older within Emory hospital and public ICU available data sets. Patients were followed through their ICU stay until they get discharged or developing sepsis according to Third International Consensus Definition Sepsis-3. Data orchestrator provides AISE algorithm on hourly basis with two input files. These files keep records of real-time High-resolution and dynamical features, laboratory results updated over the last hour, clinical features, and demographical features. Table2.2 presents the lists of all features used as inputs for predicting sepsis risk score by the AISE algorithm. The AISE algorithm accurately predict the onset of Sepsis based on Sepsis-3 def- inition 4-12 hours prior to clinical recognition. We choose the AISE algorithm for our software pipeline because of its ability to adopt the dynamic of real-time high resolution EMR data such as HR and BP recorded and comes to our pipeline from Bed Master system every hour. The AISE algorithm also outperforms other exist- ing models in terms of predicting accurately the risk of sepsis with AUCROC 85% [20]. In AISE algorithm and based on Sepsis-3 definition the time of suspicious (t suspicious) is defined as earlier time stamp of sampling blood culture within 24 hours after initiation antibiotics or the initiation of antibiotics within 72 hours after xxx- pling blood cultures for an infected patient. The onset time of sepsis (t sepsis) is then defined when there are 2 or more points change in a Sequential Organ Failure Assessment (SOFA score ) up to 24 hours...