Probabilistic modeling of sensor artifacts in critical care

2008

We describe an application of probabilistic modeling and inference technology to the problem of analyzing sensor data in the setting of an intensive care unit (ICU). In particular, we consider the arterial-line blood pressure sensor, which is subject to frequent data artifacts that cause false alarms in the ICU and make the raw data almost useless for automated decision making.

2011 IEEE Statistical Signal Processing Workshop (SSP), 2011

Assessing the global situation of a person from physiological data is a well-known difficult problem. In previous work, we propose a system that does not produce a diagnosis but instead follows a set of hypotheses and decides of an alarming situation with this information. In this paper we focus on data processing part of the system taking into account the complexity and the ambiguity of the data. We propose a statistical approach with a global model based on Hidden Markov Model and we present data models that rely on classical physiological parameters and expert's knowledge. We then learn a model that depends on the person and its environment, and we define and compute confidence values to assess the plausibility of hypotheses.

Journal of Clinical Monitoring and Computing, 2019

Studies reveal that the false alarm rate (FAR) demonstrated by intensive care unit (ICU) vital signs monitors ranges from 0.72 to 0.99. We applied machine learning (ML) to ICU multi-sensor information to imitate a medical specialist in diagnosing patient condition. We hypothesized that applying this data-driven approach to medical monitors will help reduce the FAR even when data from sensors are missing. An expert-based rules algorithm identified and tagged in our dataset seven clinical alarm scenarios. We compared a random forest (RF) ML model trained using the tagged data, where parameters (e.g., heart rate or blood pressure) were (deliberately) removed, in detecting ICU signals with the full expert-based rules (FER), our ground truth, and partial expert-based rules (PER), missing these parameters. When all alarm scenarios were examined, RF and FER were almost identical. However, in the absence of one to three parameters, RF maintained its values of the Youden index (0.94-0.97) and positive predictive value (PPV) (0.98-0.99), whereas PER lost its value (0.54-0.8 and 0.76-0.88, respectively). While the FAR for PER with missing parameters was 0.17-0.39, it was only 0.01-0.02 for RF. When scenarios were examined separately, RF showed clear superiority in almost all combinations of scenarios and numbers of missing parameters. When sensor data are missing, specialist performance worsens with the number of missing parameters, whereas the RF model attains high accuracy and low FAR due to its ability to fuse information from available sensors, compensating for missing parameters.

Lecture Notes in Computer Science , 2017

The use-case described in this paper covers data acquisition and real-time analysis of the gathered medical data from wearable sensor system. Accumulated data is essential for monitoring vital signs and tracking the dynamics of the treatment process of disabled patients or patients undergoing the recovery after traumatic knee joint injury (e.g. post-operative rehabilitation). The main goal of employing the wearable sensor system is to conduct rehabilitation process more effectively and increase the rate of successful rehabilitation. The results of data analysis of patient’s vital signs and feedback allow a physiotherapist to adjust the rehabilitation scenario on the fly. In this paper, we focus on the methodology for data modelling with a purpose to design a computer-aided rehabilitation system that would support agility of changing information requirements by being flexible and augmentable.

BioMedical Engineering OnLine, 2009

Background: Within the intensive care unit (ICU), arterial blood pressure (ABP) is typically recorded at different (and sometimes uneven) sampling frequencies, and from different sensors, and is often corrupted by different artifacts and noise which are often non-Gaussian, nonlinear and nonstationary. Extracting robust parameters from such signals, and providing confidences in the estimates is therefore difficult and requires an adaptive filtering approach which accounts for artifact types.

2021

ilustracionesResumen En el campo de la monitorización continua de los signos vitales en entornos de cuidados intensivos se ha observado que los signos de alerta temprana "de un deterioro fisiológico inminente” pueden no ser detectados a tiempo, hecho que se agrava no solo por la limitación de los recursos médicos, sino también por el "diluvio de datos" causado por la adquisición de información en pacientes cada vez más complejos durante la atención de rutina. El objetivo de este estudio es desarrollar un modelo probabilístico para predecir los episodios clínicos futuros de un paciente utilizando valores de signos vitales observados antes de un evento clínico. Los signos vitales (por ejemplo, frecuencia cardíaca, presión arterial) se utilizan para controlar las funciones fisiológicas de un paciente y sus cambios simultáneos indican las transiciones entre los estados de salud del paciente. Si tales cambios son anormales, puede conducir a un deterioro fisiológico grave. ...

2012

This paper describes the authors' observations about the uncertainties which are associated with monitoring based on sensors. It presents the results of an experiment which is part of an ongoing research about dealing with uncertain contextual information in the human health monitoring system based on sensors. The experiment employs evidence theory on reasoning over context. Recommendations to improve the systems to monitor the human health within a framework that addresses uncertainty are also provided.

Studies in fuzziness and soft computing, 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 important patterns of change such as level shifts and trends in the data. We present approaches for automated pattern detection in online-monitoring data. Several methods based on curve fitting and statistical time series analysis are described. Median filtering can be used as a preliminary step to reduce the noise and to remove clinically irrelevant short term fluctuations. Our special focus is the potential of these methods for online-monitoring in intensive care. The strengths and weaknesses of the methods are discussed in this special context. The best approach may well be a suitable combination of the methods for achieving reliable results. Further investigations are needed to further improve the methods and their performance should be compared extensively in simulation studies and applications to real data.

2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE), 2012

Vital-sign monitoring of patients within a hospital setting is a big component in the recognition and treatment of early signs of deterioration. Current vital-sign monitoring systems, including both manual early warning systems, and more sophisticated data fusion systems, typically make use of the most recently recorded data, and are unable to deal with missing data in a principled manner. The latter is particularly pertinent in the field of ambulatory monitoring, in which patient movement can result in sensor disconnections and other artefact. This paper presents a Gaussian process regression technique for estimating missing data and how it can be incorporated within an automated data fusion monitoring system. The technique is then demonstrated using vital-sign data from a recent clinical study conducted at the John Radcliffe Hospital, Oxford, showing an improvement over an existing data fusion algorithm by providing both an estimate of missing vital sign data and the uncertainty in the estimated value.

2018

Modern clinical databases collect a large amount of time series data of vital signs. In this work, we first extract the general representative signal patterns from physiological signals, such as blood pressure, respiration rate and heart rate, referred to as atomic patterns. By assuming the same disease may share the same styles of atomic patterns and their temporal dependencies, we present a probabilistic framework to recognize diseases from physiological data in the presence of uncertainty. To handle the temporal relationships among atomic patterns, Allen’s interval relations and latent variables originated from Chinese restaurant process are utilized to characterize the unique sets of interval configurations of a disease. We evaluate the proposed framework using MIMIC-III database, and the experimental results show that our approach outperforms other competitive models.