Mobile Personal Healthcare System for Non-Invasive, Pervasive and Continuous Blood Pressure Monitoring: A Feasibility Study

Background: Smartphone-based blood pressure (BP) monitor using photoplethysmogram (PPG) technology has emerged as a promising approach to empower users with self-monitoring for effective diagnosis and control ofhypertension (HT). Objective: This study aimed to develop a mobile personal healthcare system for non-invasive, pervasive, and continuous estimation of BP level and variability to be user-friendly to elderly. Methods: The proposed approach was integrated by a self-designed cuffless, calibration-free, wireless and wearable PPG-only sensor, and a native purposely-designed smartphone application using multilayer perceptron machine learning techniques from raw signals. We performed a pilot study with three elder adults (mean age 61.3 ± 1.5 years; 66% women) to test usability and accuracy of the smartphone-based BP monitor. Results: The employed artificial neural network (ANN) model performed with high accuracy in terms of predicting the reference BP values of our validation sample (n=150). On average, our approach predicted BP measures with accuracy \u3e90% and correlations \u3e0.90 (P \u3c .0001). Bland-Altman plots showed that most of the errors for BP prediction were less than 10 mmHg. Conclusions: With further development and validation, the proposed system could provide a cost-effective strategy to improve the quality and coverage of healthcare, particularly in rural zones, areas lacking physicians, and solitary elderly populations

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care

Applications of Deep Learning and Machine Learning in Healthcare Domain – A Literature Review

In recent years, Artificial Intelligence (AI) has advanced rapidly in terms of software algorithms, hardware implementation, and implementations in a wide range of fields. The latest advances in AI applications in biomedicine, such as disease diagnostics, living assistance, biomedical information processing, and biomedical science, are summarised in this study. Brain-Computer Interfaces (BCIs), Arterial Spin Labeling (ASL) imaging, ASL-MRI, biomarkers, Natural Language Processing (NLP), and various algorithms all help to reduce errors and monitor disease progression. Computer-assisted diagnosis, decision support systems, expert systems, and software implementation can help doctors reduce intra- and inter-observer variability. In this paper, numerous researchers conduct a systematic literature review on the application and implementation of Machine Learning, Deep Learning, and Artificial Intelligence in the healthcare industry

Optical imaging and spectroscopy for the study of the human brain: status report.

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions

Optical imaging and spectroscopy for the study of the human brain: status report

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 125

This special bibliography lists 323 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1974

Optical imaging and spectroscopy for the study of the human brain: status report

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions. Keywords: DCS; NIRS; diffuse optics; functional neuroscience; optical imaging; optical spectroscop

Recent Advances in Machine Learning Applied to Ultrasound Imaging

Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Deep learning for causal inference on electronic health records

Cardiovascular diseases (CVD) are the leading causes of mortality around the world and disentangling cause and effect is central to better understanding and treating these diseases. While randomised clinical trials are the “gold standard” of assessing the effect of an intervention, some hypotheses cannot be feasibly tested in the randomised setting. In these cases, observational studies with appropriate methods of confounding adjustment can deliver reliable evidence concerning the association between an exposure and outcome. Indeed, trusted conventional statistical models guided by subject area experts for confounder selection have been used to estimate associations in many observational studies; however, in the observational studies for which confounding is unknown and/or the population suffers from complex illness, the conventional approaches render insufficiently adjusted estimates. In parallel, recently, there has been unprecedented access to nationally representative multimodal electronic health record (EHR) datasets and advances in statistical learning including “deep” machine learning, a form of machine learning that relies on automatic feature capture dissolving the need for expert-driven feature engineering. In this doctoral research, the aim was to develop a deep learning approach for causal inference on EHR. To do so in a structured way, the research was split into three investigations: 1) The development of a deep learning model for EHR data and assessment of risk prediction performance 2) Given the “black box” nature of deep learning modelling, the development of methods to explain the proposed model. 3) The derivation of a model for causal inference, and application of the models for association estimation in elderly/at-risk patient subgroups. The model, Bidirectional EHR Transformer (BEHRT) was created for EHR representation learning and risk prediction. The model outperformed several benchmarks for risk prediction on a variety of tasks including incident heart failure prediction. Furthermore, in the second work, explainability investigations yielded that the model captured validated factors of risk (e.g., hypertension, diabetes, and other diseases) and offered several more factors that could be potentially preventative of incident heart failure. Lastly, a derivation of BEHRT was developed for association estimation, Targeted-BEHRT, that fused advances in deep learning and semi- parametric statistics. The model demonstrated superior estimation abilities on several simulated data experiments, and was applied to better understand the effects of antihypertensives, blood pressure, and paracetamol on cardiovascular endpoints, mortality, and other outcomes in at-risk patients. Overall, the doctoral research has made advances in both methodological and clinical cardiovascular research. While the research focuses on developing methods for the study of cardiovascular diseases, the methods developed and tested have several important implications for epidemiological research in the observational setting at large. Especially in patient groups with pre-existing health issues, the causal models developed can be a more appropriate approach for association analysis than conventional statistical ones. In terms of clinical impact, the research has progressed our understanding of risk and protection in the context of CVD

A survey of deep learning models in medical therapeutic areas.

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