Online pattern recognition in intensive care medicine (original) (raw)
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Pattern recognition in intensive care online monitoring
STUDIES IN FUZZINESS AND SOFT …, 2002
Clinical information systems can record numerous variables describing the patient's state at high sampling frequencies. Intelligent alarm systems and suitable bedside decision support are needed to cope with this flood of information. A basic task here is the fast and correct detection of ...
Statistical pattern detection in univariate time series of intensive care on-line monitoring data
Intensive Care Medicine, 1998
Today most of our bedside decisions are based on subjective judgment and experience, rather than on hard data analysis. Most of the changes of a variable over time are more important than one pathological value at the time of observation. Over the past three decades mathematical methods have been developed that allow the assessment of single or multiple variables over time.
Computational and Mathematical Methods in Medicine, 2011
Online-monitoring systems in intensive care are affected by a high rate of false threshold alarms. These are caused by irrelevant noise and outliers in the measured time series data. The high false alarm rates can be lowered by separating relevant signals from noise and outliers online, in such a way that signal estimations, instead of raw measurements, are compared to the alarm limits. This paper presents a clinical validation study for two recently developed online signal filters. The filters are based on robust repeated median regression in moving windows of varying width. Validation is done offline using a large annotated reference database. The performance criteria are sensitivity and the proportion of false alarms suppressed by the signal filters.
2017
ABSTRACTObjectivesClinicians in the intensive care unit (ICU) are presented with a large number of physiological data consisting of periodic and frequently sampled measurements, such as heart rate and blood pressure, as well as aperiodic measurements, such as noninvasive blood pressure and laboratory studies. Because this data can be overwhelming, there is considerable interest in designing algorithms that help integrate and interpret this data and assist ICU clinicians in detecting or predicting in advance patients who may be deteriorating. In order to decide whether to deploy such algorithms in a clinical trial, it is important to evaluate these algorithms using retrospective data. However, the fact that these algorithms will be running continuously, i.e., repeatedly sampling incoming patient data, presents some novel challenges for algorithm evaluation. Commonly used measures of performance such as sensitivity and positive predictive value (PPV) are easily applied to static “snap...
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
A massive amount of multimodal data are continuously collected in the intensive care unit (ICU) along each patient stay, offering a great opportunity for the development of smart monitoring devices based on artificial intelligence (AI). The two main sources of relevant information collected in the ICU are the electronic health records (EHRs) and vital sign waveforms continuously recorded at the bedside. While EHRs are already widely processed by AI algorithms for prompt diagnosis and prognosis, AI-based assessments of the patients’ pathophysiological state using waveforms are less developed, and their use is still limited to real-time monitoring for basic visual vital sign feedback at the bedside. This study uses data from the MIMIC-III database (PhysioNet) to propose a novel AI approach in ICU patient monitoring that incorporates features estimated by a closed-loop cardiovascular model, with the specific goal of identifying sepsis within the first hour of admission. Our top benchma...
Making ICU alarms meaningful: a comparison of traditional vs. trend-based algorithms
Proceedings / AMIA ... Annual Symposium. AMIA Symposium, 1999
Much of the work in the ICU revolves around information that is recorded by electronic devices. Such devices typically incorporate simple alarm functions that trigger when a value exceeds predefined limits. Depending on the parameter followed, these "boundary based" alarms tend to produce vast numbers of false alarms. Some are the result of false reading and some the result of true but clinically insignificant readings. We present a computerized module that analyzes real-time data from multiple monitoring devices using a customizable logic engine. The module was tested on 6 intensive care unit patients over 5 days, running alarm algorithms for heart rate, systolic and diastolic blood pressure as well as arterial oxygen saturation. Results show a ten-fold increase in positive predictive value of alarms from 3% using monitor alarms to 32% using the module. The module's overall sensitivity was 82%, failing to detect 18% of significant alarms as defined by the ICU staff. T...
The present state of trend detection and prediction in patient monitoring
Intensive Care Medicine, 1977
An investigation has been carried out into the suitability of the following techniques for trend detection and forecasting in patient monitoring: Cusum; Trigg's Tracking Signal; The Patient Condition Factor; The Patient Alarm Warning System; Box-Jenkins models and the Harrison-Stevens Bayesian approach. The latter holds considerable promise since it is flexible and can be implemented on a microprocessor. Consideration has also been given to the need for a better knowledge of the statistical properties of the variables to be monitored and the problems of combining trends detected in severable variables.
Toward a two-tier clinical warning system for hospitalized patients
AMIA ... Annual Symposium proceedings. AMIA Symposium, 2011
Clinical study has found early detection and intervention to be essential for preventing clinical deterioration in patients at general hospital units. In this paper, we envision a two-tiered early warning system designed to identify the signs of clinical deterioration and provide early warning of serious clinical events. The first tier of the system automatically identifies patients at risk of clinical deterioration from existing electronic medical record databases. The second tier performs real-time clinical event detection based on real-time vital sign data collected from on-body wireless sensors attached to those high-risk patients. We employ machine-learning techniques to analyze data from both tiers, assigning scores to patients in real time. The assigned scores can then be used to trigger early-intervention alerts. Preliminary study of an early warning system component and a wireless clinical monitoring system component demonstrate the feasibility of this two-tiered approach.
Biometrical Journal, 2002
The detection of patterns in monitoring data of vital signs is of great importance for adequate bedside decision support in critical care. Currently used alarm systems, which are based on fixed thresholds and independency assumptions, are not satisfactory in clinical practice. Time series techniques such as AR-models consider autocorrelations within the series, which can be used for pattern recognition in the data. For practical applications in intensive care the data analysis has to be automated. An important issue is the suitable choice of the model order which is difficult to accomplish online. In a comparative case-study we analyzed 34564 univariate time series of hemodynamic variables in critically ill patients by autoregressive models of different orders and compared the results of pattern detection. AR(2)-models seem to be most suitable for the detection of clinically relevant patterns, thus affirming that treating the data as independent leads to false alarms. Moreover, using AR(2)-models affords only short estimation periods. These findings for pattern detection in intensive care data are of medical importance as they justify a preselection of a model order, easing further automated statistical online analysis.
Patient-specific learning in real time for adaptive monitoring in critical care
Journal of Biomedical Informatics, 2008
Intensive care monitoring systems are typically developed from population data, but do not take into account the variability among individual patients' characteristics. This study develops patient-specific alarm algorithms in real time. Classification tree and neural network learning were carried out in batch mode on individual patients' vital sign numerics in successive intervals of incremental duration to generate binary classifiers of patient state and thus to determine when to issue an alarm. Results suggest that the performance of these classifiers follows the course of a learning curve.