Remote Patient Monitoring System with Decision Support

Lähteenmäki, Jaakko; Leppänen, Juha; Orsama, Anna-Leena; Salaspuro, Ville; Pirinen, Jukka; Sormunen, Marko; Kaijanranta, Hannu; Ermes, Miikka

doi:10.2316/p.2011.723-143

Cited by 3 publications

(1 citation statement)

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“…Much existing work has focused on the development of communications infrastructures, platforms and protocols for data transfer, and decision support frameworks, extended reviews of which may be found in [2], [3], [8], and [9]. The application of machine learning techniques to the predictive monitoring of patient physiological data at large scale is limited; reviews may be found in [10] and [11].…”

Section: A Existing Workmentioning

confidence: 99%

Predictive Monitoring of Mobile Patients by Combining Clinical Observations With Data From Wearable Sensors

Clifton

Pimentel

et al. 2014

IEEE J. Biomed. Health Inform.

158

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The majority of patients in the hospital are ambulatory and would benefit significantly from predictive and personalized monitoring systems. Such patients are well suited to having their physiological condition monitored using low-power, minimally intrusive wearable sensors. Despite data-collection systems now being manufactured commercially, allowing physiological data to be acquired from mobile patients, little work has been undertaken on the use of the resultant data in a principled manner for robust patient care, including predictive monitoring. Most current devices generate so many false-positive alerts that devices cannot be used for routine clinical practice. This paper explores principled machine learning approaches to interpreting large quantities of continuously acquired, multivariate physiological data, using wearable patient monitors, where the goal is to provide early warning of serious physiological determination, such that a degree of predictive care may be provided. We adopt a one-class support vector machine formulation, proposing a formulation for determining the free parameters of the model using partial area under the ROC curve, a method arising from the unique requirements of performing online analysis with data from patient-worn sensors. There are few clinical evaluations of machine learning techniques in the literature, so we present results from a study at the Oxford University Hospitals NHS Trust devised to investigate the large-scale clinical use of patient-worn sensors for predictive monitoring in a ward with a high incidence of patient mortality. We show that our system can combine routine manual observations made by clinical staff with the continuous data acquired from wearable sensors. Practical considerations and recommendations based on our experiences of this clinical study are discussed, in the context of a framework for personalized monitoring.

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Section: A Existing Workmentioning

confidence: 99%

Predictive Monitoring of Mobile Patients by Combining Clinical Observations With Data From Wearable Sensors

Clifton

Pimentel

et al. 2014

IEEE J. Biomed. Health Inform.

158

View full text Add to dashboard Cite

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Quality Analysis of Sensors Data for Personal Health Records on Mobile Devices

Puentes

Montagner

Lecornu

et al. 2012

Pervasive Health Knowledge Management

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Knowledge-Based Personal Health System to Empower Outpatients of Diabetes Mellitus by Means of P4 Medicine

Bresó

Sáez

Vicente³

et al. 2014

Methods in Molecular Biology

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Diabetes Mellitus (DM) affects hundreds of millions of people worldwide and it imposes a large economic burden on healthcare systems. We present a web patient empowering system (PHSP4) that ensures continuous monitoring and assessment of the health state of patients with DM (type I and II). PHSP4 is a Knowledge-Based Personal Health System (PHS) which follows the trend of P4 Medicine (Personalized, Predictive, Preventive, and Participative). It provides messages to outpatients and clinicians about the achievement of objectives, follow-up, and treatments adjusted to the patient condition. Additionally, it calculates a four-component risk vector of the associated pathologies with DM: Nephropathy, Diabetic retinopathy, Diabetic foot, and Cardiovascular event. The core of the system is a Rule-Based System which Knowledge Base is composed by a set of rules implementing the recommendations of the American Diabetes Association (ADA) (American Diabetes Association: http://www.diabetes.org/ ) clinical guideline. The PHSP4 is designed to be standardized and to facilitate its interoperability by means of terminologies (SNOMED-CT [The International Health Terminology Standards Development Organization: http://www.ihtsdo.org/snomed-ct/ ] and UCUM [The Unified Code for Units of Measure: http://unitsofmeasure.org/ ]), standardized clinical documents (HL7 CDA R2 [Health Level Seven International: http://www.hl7.org/index.cfm ]) for managing Electronic Health Record (EHR). We have evaluated the functionality of the system and its users' acceptance of the system using simulated and real data, and a questionnaire based in the Technology Acceptance Model methodology (TAM). Finally results show the reliability of the system and the high acceptance of clinicians.

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Remote Patient Monitoring System with Decision Support

Cited by 3 publications

References 0 publications

Predictive Monitoring of Mobile Patients by Combining Clinical Observations With Data From Wearable Sensors

Predictive Monitoring of Mobile Patients by Combining Clinical Observations With Data From Wearable Sensors

Quality Analysis of Sensors Data for Personal Health Records on Mobile Devices

Knowledge-Based Personal Health System to Empower Outpatients of Diabetes Mellitus by Means of P4 Medicine

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