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Blood Pressure

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Mechanistic Assessment of Cardiovascular State Informed by Vibroacoustic Sensors.

Sensors (Basel, Switzerland)
Monitoring blood pressure, a parameter closely related to cardiovascular activity, can help predict imminent cardiovascular events. In this paper, a novel method is proposed to customize an existing mechanistic model of the cardiovascular system thro...

Development of a machine learning-based model for predicting individual responses to antihypertensive treatments.

Nutrition, metabolism, and cardiovascular diseases : NMCD
BACKGROUND AND AIMS: Personalized antihypertensive drug selection is essential for optimizing hypertension management. The study aimed to develop a machine learning (ML) model to predict individual blood pressure (BP) responses to different antihyper...

A Survey on Blood Pressure Measurement Technologies: Addressing Potential Sources of Bias.

Sensors (Basel, Switzerland)
Regular blood pressure (BP) monitoring in clinical and ambulatory settings plays a crucial role in the prevention, diagnosis, treatment, and management of cardiovascular diseases. Recently, the widespread adoption of ambulatory BP measurement devices...

Prediction of cardiovascular and renal risk among patients with apparent treatment-resistant hypertension in the United States using machine learning methods.

Journal of clinical hypertension (Greenwich, Conn.)
Apparent treatment-resistant hypertension (aTRH), defined as blood pressure (BP) that remains uncontrolled despite unconfirmed concurrent treatment with three antihypertensives, is associated with an increased risk of developing cardiovascular and re...

Construction of Risk Prediction Model of Type 2 Diabetic Kidney Disease Based on Deep Learning.

Diabetes & metabolism journal
BACKGRUOUND: This study aimed to develop a diabetic kidney disease (DKD) prediction model using long short term memory (LSTM) neural network and evaluate its performance using accuracy, precision, recall, and area under the curve (AUC) of the receive...

Artificial Intelligence-Derived Risk Prediction: A Novel Risk Calculator Using Office and Ambulatory Blood Pressure.

Hypertension (Dallas, Tex. : 1979)
BACKGROUND: Quantification of total cardiovascular risk is essential for individualizing hypertension treatment. This study aimed to develop and validate a novel, machine-learning-derived model to predict cardiovascular mortality risk using office bl...

The association between PM components and blood pressure changes in late pregnancy: A combined analysis of traditional and machine learning models.

Environmental research
BACKGROUND: PM is a harmful mixture of various chemical components that pose a challenge in determining their individual and combined health effects due to multicollinearity issues with traditional linear regression models. This study aimed to develo...

Use of an Artificial Intelligence Device for Evaluating Blood Loss in Complex Major Orthopaedic Surgery Procedures.

The Journal of arthroplasty
BACKGROUND: An artificial intelligence algorithm that analyzes the pulse oximeter waveform in the fingertip can be used to determine the compensatory reserve index (CRI) in trauma patients. This measurement shows the remaining cardiovascular capacity...

HGCTNet: Handcrafted Feature-Guided CNN and Transformer Network for Wearable Cuffless Blood Pressure Measurement.

IEEE journal of biomedical and health informatics
Biosignals collected by wearable devices, such as electrocardiogram and photoplethysmogram, exhibit redundancy and global temporal dependencies, posing a challenge in extracting discriminative features for blood pressure (BP) estimation. To address t...

Predicting Blood Pressures for Pregnant Women by PPG and Personalized Deep Learning.

IEEE journal of biomedical and health informatics
Blood pressure (BP) is predicted by this effort based on photoplethysmography (PPG) data to provide effective pre-warning of possible preeclampsia of pregnant women. Towards frequent BP measurement, a PPG sensor device is utilized in this study as a ...