Improving Diagnostic Accuracy Using Multiparameter Patient Monitoring Based on Data Fusion in the Cloud Zhanpeng Jin∗† , Xiaoliang Wang∗ , Qiong Gui∗ , and Bingwei Liu∗ ∗ Department
of Electrical and Computer Engineering † Department of Bioengineering Binghamton University, State University of New York Binghamton, NY 13902-6000 Email: {zjin, xwang90, qgui1, bliu11}@binghamton.edu
Abstract—Accurate clinical decision making in medical monitoring relies on the strategical fusion of multiparameter physiological signals and thus usually demands a wide variety of complex machine learning approaches and a large set of knowledge database. However, those requirements impose great challenges on computing and storage capabilities, which make it impossible to execute on a single portable computing platform. Leveraging emerging cloud computing technologies, we propose to strategically manage the workloads on the mobile medical monitoring device and to migrate the highly intricate multiparameter data fusion and training procedure to the cloud. The mobile device transmits all sensing data acquired from wearable body sensors to the cloud, which now provides a large pool of easily accessible dataset for the training procedures. The well-trained configurations will be sent back to the mobile device and update its existing complex machine learning based implementations.
I.
S TATUS AND C HALLENGES OF M EDICAL M ONITORING
The skyrocketing healthcare expenditure and the fast aging population impose serious economic burdens to the entire society, while people has constantly pursued high-quality medical services. All of these challenges highlight the needs for innovative solutions supporting more accurate, affordable, flexible, and reliable medical diagnosis and treatment. A critical and costly part of the existing healthcare systems is the monitoring of patients’ vital signs and other physiological signals, which play significant roles in facilitating physicians’ diagnostic practices and tracking people’s daily health status. Most of diseases are not a single symptom, but rather a grouping of signs reflected from highly intercorrelated physiological measures. Current medical monitoring systems often act in an isolated fashion and trigger an alarm when a specific physiological measurement shows abnormalities, without any dependence on other related signals or an individual’s prior medical information [1]. Consequently, the overall health state of a specific person cannot be accurately represented and an extremely high rate of false alarms are reported in clinical settings. It was reported that up to 90% of alarms are false positives and the vast majority of alarms have no real clinical impact [2]. Thus a main challenge in medical monitoring is to build a new fusion system involving heterogeneous electrophysiological data for improving the detection of patient states. Given the extracted spatial, spectral, and statistical physiological features, the whole society has been investigating novel approaches that allow synergistic combination of a number of diverse medial parameters, according to the personalized and
Sejun Song Department of Engineering Technology and Industrial Distribution Texas A&M University College Station, TX 77843-3367 Email:
[email protected]
context-dependent multi-criteria evaluation. The ultimate goal is to compile a unified Health Index (HI) [3] to produce an accurate overall picture of individual’s health status. In medical decision making, data fusion consists of combining data, reducing its complexity and designing a synthetic representation to be more easily interpreted. This requires the integration of spatially and/or temporally distributed information-gathering systems of very high levels of abstractions. In principle, fusion of multiparameter physiological data provides significant advantages over single-parameter data, especially for highly intricate medical diseases and symptoms, which usually involve the correlated rhythms and changes of a set of physiological behaviors. In addition to the statistical advantage gained by combining same-source data (e.g., obtaining an improved estimate via redundant observations), the use of multiple types of physiological information may increase the accuracy with which a quantity can be characterized. For instance, multi-channel ECG signals accompanied with respiratory rate, blood pressure, and SpO2 can be used to provide a more accurate picture about the health status of a cardiovascular patient, although approximate diagnostic assessment can be performed using only selected ECG channels. Observational physiological data may be fused at, from the feature level to the decision level. Feature-level fusion involves the extraction of representative features. An example is the use of statistical metrics (e.g., mean and correlation coefficient) and spectral metrics (e.g., frequency and power spectral density (PSD)) to represent a certain type of physiological measurement. Such features extracted from multiple medical observations will be combined into a single concatenated feature vector which is input to standard pattern recognition approaches. A higher level fusion is also applicable even after each physiological activity has been used to deduce a preliminary determination of an individual’s medical condition. Diagnostic results based on one physiological signal may be either consistent, irrelevant, or contradictory to the behavior revealed from another medical measurement. Thus a synergistic information fusion is imperative at a decision level. II.
O PPORTUNITIES OF DATA F USION IN THE C LOUD
Multi-parameter monitoring has been widely used in today’s clinical diagnostic processes, including various bedside monitors in hospitals or ambulatory devices used for personalized healthcare. Unfortunately, existing computer-assisted
diagnostic analysis based on monitored signals still remains at a rather simple and intuitive level. Besides being visually screened and evaluated by physicians or nurses, most of patient monitors rely on some certain “calling criteria” to activate the alarms, which can be as simple as a few fixed threshold values, or a systematic mechanism involving all monitored variables. Generally, these criteria can be referred to as “physiological track and trigger warning (TTW) systems” [4]. TTW systems can be further categorised as single-parameter, multiparameter, aggregate weighted scoring or combination systems [4]. The simplest of these – single-parameter systems [5] – raise alarms if the extreme abnormality observed in any one of monitored signal. In contrast, multi-parameter systems only indicate warnings if extreme observational values occur in two or more signals. Aggregate weighted scoring systems [6] allocate points to each vital sign variable and trigger the alarms according to the sum of the allocated points, namely Early Warning Scores (EWS) [7]. The combination TT warning systems integrate the elements of single-/multiparameter systems with aggregate weighted scoring. Recently, sophisticated machine learning techniques have been extensively investigated for higher-level medical data fusion and used in identifying imminent medical problems by automatically recognizing the abnormal behaviors from a huge amount of physiological signal data, such as fuzzy inference systems, artificial neural networks (ANNs), support vector machines (SVMs), and Bayesian Networks. For instance, according to our earlier studies, the ANN-based electrocardiogram (ECG) signal processing algorithm can achieve over 95% accuracy in detecting various cardiac arrhythmia [8], and the SVM-based multiparameter vital sign analysis algorithm can significantly reduce the amount of false alarms [9]. However, to achieve a satisfactory training performance, those machine learning based clinical decision support approaches usually demand a large set of a priori knowledge as the training dataset and iteratively perform the computationintensive training processes, which make it impossible and infeasible to execute on a single portable computing platform in the ambulatory setting that is increasingly demanded from the perspectives of pervasive healthcare or ubiquitous healthcare. For example, recent advances in wearable body sensors and mobile computing technologies have enabled and promoted the use of mobile-based health monitoring and alert systems (usually referred as “mHealth”), aiming at providing real-time feedback about an individuals health condition to either the user or to a medical center, while alerting in case of possibly imminent health-threatening conditions. However, the limited computational power and battery life of existing mobile devices, significantly limit their ability to execute resourceintensive applications. Emerging cloud computing provides an alternative to facilitate the conventional clinical decision support systems and to transform the way how future healthcare will be practiced and delivered in a more effective and efficient manner. Some prior studies have been conducted to explore the possible use of cloud computing in healthcare [10]. To address the increasing demands of more sustainable use of mobile devices and more accurate diagnostic decisionmaking in medical monitoring applications, we propose to strategically manage the workloads on the mobile devices and to migrate the highly intricate multiparameter data fusion and the supervised training procedure to the cloud. The mobile
devices will also transmit all sensing data acquired from wearable body sensors or portable physiological monitors to cloud storage, which now can provide a large pool of easily accessible dataset for the supervised training procedures. However, the operations of the machine learning algorithms deployed on mobile devices will continue their regular classification processing without any halt, based on the latest trained configurations. Once the supervised training on the cloud in finished, the well-trained configurations will be sent back to the mobile devices and update the existing implementations of the machine learning algorithms on the mobile devices. In addition to the substantially reduced power consumption and extended battery life of mobile devices, this bidirectional, dynamic adaptive workload balancing and migration approach can constantly improve the performance of the deployed machine learning techniques by regularly updating them according to the most recent training results. As new sensing data of a subject is continuously processed on the mobile devices and backed up on the cloud, the whole system holds the potential to gradually evolve itself toward an even higher diagnostic accuracy through unrelenting individual-specific training and adaptation. However, though the synergistic combination of cloud computing and data fusion has shown great promise in transform future clinical decision support systems, there still are some research questions regarding how to determine the optimal interval for launching a new training process and accordingly ensure a balanced tradeoff between the diagnostic accuracy and the computation efficiency, as well as how to avoid the over-fitting issue in the iterative training procedures. R EFERENCES [1] G. Clifford, et al., “Robust parameter extraction for decision support using multimodal intensive care data,” Phil. Trans. R. Soc. A, vol. 367, no. 1887, pp. 411-429, 2009. [2] M. Imhoff and S. Kuhls, “Alarm algorithms in critical care monitoring,” Anesthesia & Analgesia, vol. 102, no. 5, pp. 1525-1537, 2006. [3] H. Alemzadeh, et al., “An embedded reconfigurable architecture for patient-specific multiparameter medical monitoring,” in Proc. EMBC, pp. 1896-1900, 2011. [4] DH and Modernisation Agency, “Critical care outreach 2003: Progress in developing services,” Department of Health, United Kingdom, 2003. [5] G. Smith, et al., “A review, and performance evaluation, of single-parameter ‘track and trigger’ systems,” Resuscitation, vol. 79, no. 1, pp. 11-21, 2008. [6] G. Smith, et al., “Review and performance evaluation of aggregate weighted ‘track and trigger’ systems,” Resuscitation, vol. 77, no. 2, pp. 170-179, 2008. [7] U. Kyriacos, et al., “Monitoring vital signs using early warning scoring systems: a review of the literature,” J. Nursing Management, vol. 19, no. 3, pp. 311-330, 2011. [8] Z. Jin, et al., “Predicting cardiovascular disease from real-time ECG monitoring: An adaptive machine learning approach on a cell phone,” in Proc. EMBC, 2009. [9] X. Wang, et al., “Leveraging mobile cloud for telemedicine: a performance study in medical monitoring,” in Proc. NEBEC, 2013. [10] C.-P. Shen, et al., “Bio-signal analysis system design with support vector machine based on cloud computing service architecture,” in Proc. EMBC, pp. 1421-1424, 2010.
