Prospects of Machine and Deep Learning in Analysis of Vital Signs for the Improvement of Healthcare Services

2019 ◽  
pp. 113-136 ◽  
Author(s):  
Mohamed Alloghani ◽  
Thar Baker ◽  
Dhiya Al-Jumeily ◽  
Abir Hussain ◽  
Jamila Mustafina ◽  
...  
Keyword(s):  
Deep Learning ◽  
Vital Signs ◽  
Healthcare Services

543 Automated detection of mitral annular plane systolic excursion in transoesophageal echocardiography based on deep learning

2020 ◽  
Vol 21 (Supplement_1) ◽  
Author(s):  
T Nordal ◽  
E A R Berg ◽  
G Kiss
Keyword(s):  
Deep Learning ◽  
Left Ventricle ◽  
Vital Signs ◽  
Transoesophageal Echocardiography ◽  
Vertical Movement ◽  
Left Ventricular ◽  
Cardiac Monitoring ◽  
Major Surgery ◽  
Intraobserver Variability ◽  
Manual Input

Abstract Introduction Major surgery and interventions may impact cardiac function. Perioperative monitoring is currently based on vital signs and clinical observations. However, this does not offer a complete monitoring of left ventricular function throughout the intervention. We hypothesize that functional monitoring of the heart can be performed automatically based on transoesphageal echocardiography (TOE) images. One parameter that has been shown to correlate well with ejection fraction is mitral annular plane systolic excursion (MAPSE). To aid functional monitoring of the left ventricle perioperatively, we propose a technique for detecting MAPSE in TOE images of the left ventricle. Purpose The purpose of this study is to automatically track the movement of the mitral annular plane in TOE sequences of the left ventricle and detect MAPSE via a deep learning approach. Method Recordings from 131 consecutive complete TOE exams from the Echocardiography Unit were anonymized and used for training. Recordings from 23 consecutive TOE exams, also anonymized, were used as test set. All recordings were manually annotated with the location of the landmarks indicated in both 4-chamber (4C) and 2-chamber (2C) views. All recordings were made using state-of-the-art clinical scanners. The captures include 3 to 5 heart cycles of standard 4C and 2C views. An approach based on a fully convolutional neural network was implemented and trained in a supervised manner to predict the location of two landmarks on the mitral annular plane in B-mode TOE images from 4C and 2C views. The model was also trained to account for noise by recognizing when detecting the landmarks is not feasible due to poor image quality. We have implemented all necessary post processing calculations to automatically estimate MAPSE based only on raw TOE B-mode sequences. Results Preliminary results on the test data show that the landmark detector is able to track the vertical movement of landmarks on the mitral annular plane with a mean error of 0.88 mm and a standard deviation of 0.27 mm (Fig. 1: Upper left and lower left: tracked mitral attachment points on a sample case presented upper right. Lower right: all measured Y-axis excursion values versus the reference). The classifier for detecting ultrasound frames where landmark detection is not feasible has a sensitivity of 0.82 and a specificity of 0.91. Conclusion The landmark detector is showing promising results in tracking of the mitral annular plane excursion. This can provide a fast calculation of MAPSE and eliminate intraobserver variability. This may be included in a more extensive cardiac monitoring for any type of surgery without the need of manual input from echocardiographers. Further research is ongoing and a comparison with clinical MAPSE values is underway. Abstract 543 Figure 1

scholarly journals Utilization of Deep Learning for Subphenotype Identification in Sepsis-Associated Acute Kidney Injury

2020 ◽  
Vol 15 (11) ◽  
pp. 1557-1565 ◽  
Author(s):  
Kumardeep Chaudhary ◽  
Akhil Vaid ◽  
Áine Duffy ◽  
Ishan Paranjpe ◽  
Suraj Jaladanki ◽  
...  
Keyword(s):  
Intensive Care ◽  
Deep Learning ◽  
Medical Information ◽  
Tertiary Care ◽  
Vital Signs ◽  
Kidney Injury ◽  
The United States ◽  
Care Hospital ◽  
Electronic Health Record Data ◽  
Electronic Health

Background and objectivesSepsis-associated AKI is a heterogeneous clinical entity. We aimed to agnostically identify sepsis-associated AKI subphenotypes using deep learning on routinely collected data in electronic health records.Design, setting, participants, & measurementsWe used the Medical Information Mart for Intensive Care III database, which consists of electronic health record data from intensive care units in a tertiary care hospital in the United States. We included patients ≥18 years with sepsis who developed AKI within 48 hours of intensive care unit admission. We then used deep learning to utilize all available vital signs, laboratory measurements, and comorbidities to identify subphenotypes. Outcomes were mortality 28 days after AKI and dialysis requirement.ResultsWe identified 4001 patients with sepsis-associated AKI. We utilized 2546 combined features for K-means clustering, identifying three subphenotypes. Subphenotype 1 had 1443 patients, and subphenotype 2 had 1898 patients, whereas subphenotype 3 had 660 patients. Subphenotype 1 had the lowest proportion of liver disease and lowest Simplified Acute Physiology Score II scores compared with subphenotypes 2 and 3. The proportions of patients with CKD were similar between subphenotypes 1 and 3 (15%) but highest in subphenotype 2 (21%). Subphenotype 1 had lower median bilirubin levels, aspartate aminotransferase, and alanine aminotransferase compared with subphenotypes 2 and 3. Patients in subphenotype 1 also had lower median lactate, lactate dehydrogenase, and white blood cell count than patients in subphenotypes 2 and 3. Subphenotype 1 also had lower creatinine and BUN than subphenotypes 2 and 3. Dialysis requirement was lowest in subphenotype 1 (4% versus 7% [subphenotype 2] versus 26% [subphenotype 3]). The mortality 28 days after AKI was lowest in subphenotype 1 (23% versus 35% [subphenotype 2] versus 49% [subphenotype 3]). After adjustment, the adjusted odds ratio for mortality for subphenotype 3, with subphenotype 1 as a reference, was 1.9 (95% confidence interval, 1.5 to 2.4).ConclusionsUtilizing routinely collected laboratory variables, vital signs, and comorbidities, we were able to identify three distinct subphenotypes of sepsis-associated AKI with differing outcomes.

scholarly journals AI-Based Early Change Detection in Smart Living Environments

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3549
Author(s):  
Giovanni Diraco ◽  
Alessandro Leone ◽  
Pietro Siciliano
Keyword(s):  
Deep Learning ◽  
Vital Signs ◽  
Big Data Analytics ◽  
Synthetic Data ◽  
Value Added ◽  
Care Recipient ◽  
Sensor Data ◽  
Detection Accuracy ◽  
Learning Techniques ◽  
Unsupervised Deep Learning

In the smart environments we live today, a great variety of heterogeneous sensors are being increasingly deployed with the aim of providing more and more value-added services. This huge availability of sensor data, together with emerging Artificial Intelligence (AI) methods for Big Data analytics, can yield a wide array of actionable insights to help older adults continue to live independently with minimal support of caregivers. In this regard, there is a growing demand for technological solutions able to monitor human activities and vital signs in order to early detect abnormal conditions, avoiding the caregivers’ daily check of the care recipient. The aim of this study is to compare state-of-the-art machine and deep learning techniques suitable for detecting early changes in human behavior. At this purpose, specific synthetic data are generated, including activities of daily living, home locations in which such activities take place, and vital signs. The achieved results demonstrate the superiority of unsupervised deep-learning techniques over traditional supervised/semi-supervised ones in terms of detection accuracy and lead-time of prediction.

scholarly journals Evidence and Recommendations on the Use of Telemedicine for the Management of Arterial Hypertension

Hypertension ◽  
2020 ◽  
Vol 76 (5) ◽  
pp. 1368-1383 ◽  
Author(s):  
Stefano Omboni ◽  
Richard J. McManus ◽  
Hayden B. Bosworth ◽  
Lucy C. Chappell ◽  
Beverly B. Green ◽  
...  
Keyword(s):  
Healthcare Professionals ◽  
Vital Signs ◽  
Healthcare Services ◽  
Hypertension Management ◽  
Team Physician ◽  
Video Consultation ◽  
Ideal Approach ◽  
Healthcare Model ◽  
Automated Feedback ◽  
The Ideal

Telemedicine allows the remote exchange of medical data between patients and healthcare professionals. It is used to increase patients’ access to care and provide effective healthcare services at a distance. During the recent coronavirus disease 2019 (COVID-19) pandemic, telemedicine has thrived and emerged worldwide as an indispensable resource to improve the management of isolated patients due to lockdown or shielding, including those with hypertension. The best proposed healthcare model for telemedicine in hypertension management should include remote monitoring and transmission of vital signs (notably blood pressure) and medication adherence plus education on lifestyle and risk factors, with video consultation as an option. The use of mixed automated feedback services with supervision of a multidisciplinary clinical team (physician, nurse, or pharmacist) is the ideal approach. The indications include screening for suspected hypertension, management of older adults, medically underserved people, high-risk hypertensive patients, patients with multiple diseases, and those isolated due to pandemics or national emergencies.

A Review of Wireless Sensor Networks for Wellness Monitoring in Residential Aged Care

2009 ◽  
Vol 1 (4) ◽  
pp. 32-47
Author(s):  
Leroy Lai Yu Chan ◽  
Branko George Celler ◽  
James Zhaonan Zhang ◽  
Nigel Hamilton Lovell
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Vital Signs ◽  
Healthcare Services ◽  
Developed Countries ◽  
Aged Care ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Residential Aged Care ◽  
Enabling Technologies

With the increasing shift in the population profile to the older demographic and rising healthcare costs, it is more critical for developed countries to deliver long-term and financially sustainable healthcare services, especially in the area of residential aged care. A consensus exists that innovations in the area of Wireless Sensor Networks (WSNs) are key enabling technologies for reaching this goal. The major focus of this article is WSN design considerations for ubiquitous wellness monitoring systems in residential aged care facilities. Major enabling technologies for building a pervasive WSN will be detailed, including descriptions on sensor design, wireless communication protocols and network topologies. Also examined are data processing methods and knowledge management tools to support the collection of sensor data and their subsequent analysis for health assessment. To introduce future healthcare reform in residential aged care, two aspects of wellness monitoring, vital signs and activities of daily living (ADL) monitoring, will be discussed.

scholarly journals A Scheduling Mechanism Based on Optimization Using IoT-Tasks Orchestration for Efficient Patient Health Monitoring

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5430
Author(s):  
Naeem Iqbal ◽  
Imran Imran ◽  
Shabir Ahmad ◽  
Rashid Ahmad ◽  
Do-Hyeun Kim
Keyword(s):  
Real Time ◽  
Health Monitoring ◽  
Vital Signs ◽  
Healthcare Services ◽  
Healthcare Systems ◽  
Physiological Data ◽  
Objective Functions ◽  
Health Monitoring System ◽  
The Past ◽  
Patient Health

Over the past years, numerous Internet of Things (IoT)-based healthcare systems have been developed to monitor patient health conditions, but these traditional systems do not adapt to constraints imposed by revolutionized IoT technology. IoT-based healthcare systems are considered mission-critical applications whose missing deadlines cause critical situations. For example, in patients with chronic diseases or other fatal diseases, a missed task could lead to fatalities. This study presents a smart patient health monitoring system (PHMS) based on an optimized scheduling mechanism using IoT-tasks orchestration architecture to monitor vital signs data of remote patients. The proposed smart PHMS consists of two core modules: a healthcare task scheduling based on optimization and optimization of healthcare services using a real-time IoT-based task orchestration architecture. First, an optimized time-constraint-aware scheduling mechanism using a real-time IoT-based task orchestration architecture is developed to generate autonomous healthcare tasks and effectively handle the deployment of emergent healthcare tasks. Second, an optimization module is developed to optimize the services of the e-Health industry based on objective functions. Furthermore, our study uses Libelium e-Health toolkit to monitors the physiological data of remote patients continuously. The experimental results reveal that an optimized scheduling mechanism reduces the tasks starvation by 14% and tasks failure by 17% compared to a conventional fair emergency first (FEF) scheduling mechanism. The performance analysis results demonstrate the effectiveness of the proposed system, and it suggests that the proposed solution can be an effective and sustainable solution towards monitoring patient’s vital signs data in the IoT-based e-Health domain.

Dependable Services for Mobile Health Monitoring Systems

2012 ◽  
Vol 4 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Marcello Cinque ◽  
Antonio Coronato ◽  
Alessandro Testa
Keyword(s):  
Mobile Health ◽  
Health Monitoring ◽  
Failure Modes ◽  
Vital Signs ◽  
Healthcare Services ◽  
Point Of View ◽  
Monitoring Systems ◽  
Business Market ◽  
Health Monitoring Systems ◽  
Mobile Health Monitoring

The design and realization of health monitoring systems has attracted the interest of large communities both from industry and academia. Remote and continuous monitoring of patient’s vital signs is the target of an emerging business market that aims both to improve the quality of life of patients and to reduce costs of national healthcare services. Such applications, however, are particularly critical from the point of view of dependability. This presents the design of a set of services for the assurance of high degrees of dependability to generic mobile health monitoring systems. The design is based on the results of a detailed failure modes and effects analysis (FMEA), conducted to identify the typical dependability threats of health monitoring systems. The FMEA allowed the authors to conceive a set of configurable monitoring services, enriching the system with the ability to detect failures at runtime, and enabling the realization of dependable services for future mobile health monitoring systems.

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