Gleaning knowledge from data in the intensive care unit

Abstract

It is often difficult to accurately predict when, why, and which patients develop shock, because signs of shock often occur late, once organ injury is already present. Three levels of aggregation of information can be used to aid the bedside clinician in this task: analysis of derived parameters of existing measured physiologic variables using simple bedside calculations (functional hemodynamic monitoring); prior physiologic data of similar subjects during periods of stability and disease to define quantitative metrics of level of severity; and libraries of responses across large and comprehensive collections of records of diverse subjects whose diagnosis, therapies, and course is already known to predict not only disease severity, but also the subsequent behavior of the subject if left untreated or treated with one of the many therapeutic options. The problem is in defining the minimal monitoring data set needed to initially identify those patients across all possible processes, and then specifically monitor their responses to targeted therapies known to improve outcome. To address these issues, multivariable models using machine learning data-driven classification techniques can be used to parsimoniously predict cardiorespiratory insufficiency. We briefly describe how these machine learning approaches are presently applied to address earlier identification of cardiorespiratory insufficiency and direct focused, patient-specific management.

Keywords: big data; complexity modeling; functional hemodynamic monitoring; machine learning.

Figure 1.

Flow diagram of a typical operation of the model learning phase of a machine learning–based pattern-recognition system. The available library of clinical data is first prepared for training. Streams of potentially diverse data types received from multiple sources—bedside, clinical records, medical history, patient demographics, etc., represented at varied temporal resolutions and recencies—are featurized using any combination of expert rules, statistical characterization, data mining, and anomaly detection techniques to extract and characterize potentially informative patterns in data. Occurrences of the events of interest (e.g., episodes of instability) are adjudicated by expert clinicians and annotated to serve as training examples. Annotated featurized data are then processed by machine learning algorithms to produce reliable models for particular tasks of clinical relevance (e.g., adverse event detection, forecasting instability, or tracking response to treatment). Resulting models are often empirically validated using set-aside test data sets, and further validated by expert clinicians. Results of validation can be used as feedback (dashed lines in the flow diagram) to tune structures of the models themselves, as well as to improve feature extraction and annotation processes (self-diagnostic algorithms, known as active learning, are often used to identify particularly informative yet unlabeled incidents for expert annotation), and they can also be used to inform improvements in the source data acquisition procedures, and help address data quality issues.

Figure 2.

Flow diagram of a typical operation of the performance phase of a machine learning–based pattern-recognition system. Current observations of the monitored patient are featurized in the same way as the data from the reference library (access to which, depending on the types of machine learning models in use, may be required), and processed by the previously trained models to produce predictions.