Deep Neural Network for Cardiac Magnetic Resonance Image Segmentation.

The analysis and interpretation of cardiac magnetic resonance (CMR) images are often time-consuming. The automated segmentation of cardiac structures can reduce the time required for image analysis. Spatial similarities between different CMR image types were leveraged to jointly segment multiple sequences using a segmentation model termed a multi-image type UNet (MI-UNet).

Explainable Fall Risk Prediction in Older Adults Using Gait and Geriatric Assessments.

Older adults aged 65 and above are at higher risk of falls. Predicting fall risk early can provide caregivers time to provide interventions, which could reduce the risk, potentially avoiding a possible fall. In this paper, we present an analysis of 6-month fall risk prediction in older adults using geriatric assessments, GAITRite measurements, and fall history.

NATURAL LANGUAGE PROCESSING BASED MACHINE LEARNING MODEL USING CARDIAC MRI REPORTS TO IDENTIFY HYPERTROPHIC CARDIOMYOPATHY PATIENTS.

Hypertrophic Cardiomyopathy (HCM) is the most common genetic heart disease in the US and is known to cause sudden death (SCD) in young adults. While significant advancements have been made in HCM diagnosis and management, there is a need to identify HCM cases from electronic health record (EHR) data to develop automated tools based on natural language processing guided machine learning (ML) models for accurate HCM case identification to improve management and reduce adverse outcomes of HCM patients. Cardiac Magnetic Resonance (CMR) Imaging, plays a significant role in HCM diagnosis and risk stratification.

Incorporating RTLS-Based Spatiotemporal Information in Studying Physical Activities of Clinical Staff.

Clinicians and staff who work in intense hospital settings such as the emergency department (ED) are under an extended amount of mental and physical pressure every day. They may spend hours in active physical pressure to serve patients with severe injuries or stay in front of a computer to review patients' clinical history and update the patients' electronic health records (EHR). Nurses on the other hand may stay for multiple consecutive days of 9-12 working hours.

Explanatory Analysis of a Machine Learning Model to Identify Hypertrophic Cardiomyopathy Patients from EHR Using Diagnostic Codes.

Hypertrophic cardiomyopathy (HCM) is a genetic heart disease that is the leading cause of sudden cardiac death (SCD) in young adults. Despite the well-known risk factors and existing clinical practice guidelines, HCM patients are underdiagnosed and sub-optimally managed. Developing machine learning models on electronic health record (EHR) data can help in better diagnosis of HCM and thus improve hundreds of patient lives.

Monitoring Diagnostic Safety Risks in Emergency Departments: Protocol for a Machine Learning Study.

Background: Diagnostic decision making, especially in emergency departments, is a highly complex cognitive process that involves uncertainty and susceptibility to errors. A combination of factors, including patient factors (eg, history, behaviors, complexity, and comorbidity), provider-care team factors (eg, cognitive load and information gathering and synthesis), and system factors (eg, health information technology, crowding, shift-based work, and interruptions) may contribute to diagnostic errors. Using electronic triggers to identify records of patients with certain patterns of care, such as escalation of care, has been useful to screen for diagnostic errors.

Trauma Activation Responsiveness: An RFID-enabled trauma flowsheet.

Manually documented trauma flow sheets contain critical information regarding trauma resuscitations in the emergency department (ED). The American College of Surgeons (ACS) has enforced certain thresholds on trauma surgeons' arrival time to the trauma bay. Due to the complex and fast-paced ED environment, this information can be easily overlooked or erroneously recorded, affecting compliance with ACS standards.

Corrections to "Cardiovascular Function and Ballistocardiogram: A Relationship Interpreted via Mathematical Modeling" [Oct 19 2906-2917].

Presents a missing funding acknowledgment for Dr. Mihail Popescu in the above named paper.

Using Sensor Signals in the Early Detection of Heart Failure: A Case Study.

Early detection of heart failure in older adults will be a significant issue for the foreseeable future. The current article presents a case study to describe how monitoring ballistocardiogram (BCG) waveforms captured non-invasively using sensors placed under a bed mattress can detect early heart failure changes. Heart and respiratory rates obtained from the bed sensor of a female older adult who was hospitalized with acute mixed congestive heart failure, clinic notes, and data from computer simulations reflecting increasing diastolic dysfunction were analyzed.

Cardiovascular Function and Ballistocardiogram: A Relationship Interpreted via Mathematical Modeling.

Objective: To develop quantitative methods for the clinical interpretation of the ballistocardiogram (BCG).

Methods: A closed-loop mathematical model of the cardiovascular system is proposed to theoretically simulate the mechanisms generating the BCG signal, which is then compared with the signal acquired via accelerometry on a suspended bed.

Results: Simulated arterial pressure waveforms and ventricular functions are in good qualitative and quantitative agreement with those reported in the clinical literature.

Sleep Posture Classification Using Bed Sensor Data and Neural Networks.

Sleep posture has been shown to be important in monitoring health conditions such as congestive heart failure (CHF), sleep apnea, pressure ulcers, and even blood pressure abnormalities. In this paper, we investigate the use of four hydraulic bed transducers placed underneath the mattress to classify different sleep postures. For classification, we employed a simple neural network.

Monitoring the Relative Blood Pressure Using a Hydraulic Bed Sensor System.

We propose a nonwearable hydraulic bed sensor system that is placed underneath the mattress to estimate the relative systolic blood pressure of a subject, which only differs from the actual blood pressure by a scaling and an offset factor. Two types of features are proposed to obtain the relative blood pressure, one based on the strength and the other on the morphology of the bed sensor ballistocardiogram pulses. The relative blood pressure is related to the actual by a scale and an offset factor that can be obtained through calibration.

Robust heartbeat detection from in-home ballistocardiogram signals of older adults using a bed sensor.

We propose a simple and robust method to detect heartbeats using the ballistocardiogram (BCG) signal that is produced by a hydraulic bed sensor placed under the mattress. The proposed method is found beneficial especially when the BCG signal does not display consistent J-peaks, which can often be the case for overnight, in-home monitoring, especially with frail seniors. Heartbeat detection is based on the short-time energy of the BCG signal.

Monitoring patients in hospital beds using unobtrusive depth sensors.

We present an approach for patient activity recognition in hospital rooms using depth data collected using a Kinect sensor. Depth sensors such as the Kinect ensure that activity segmentation is possible during day time as well as night while addressing the privacy concerns of patients. It also provides a technique to remotely monitor patients in a non-intrusive manner.

A novel web-based depth video rewind approach toward fall preventive interventions in hospitals.

The purpose of this study was to implement a web based application to provide the ability to rewind and review depth videos captured in hospital rooms to investigate the event chains that led to patient's fall at a specific time. In this research, Kinect depth images are being used to capture shadow-like images of the patient and their room to resolve concerns about patients' privacy. As a result of our previous research, a fall detection system has been developed and installed in hospital rooms, and fall alarms are generated if any falls are detected by the system.