100 research outputs found

    Applications of the Internet of Medical Things to Type 1 Diabetes Mellitus

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    Type 1 Diabetes Mellitus (DM1) is a condition of the metabolism typified by persistent hyperglycemia as a result of insufficient pancreatic insulin synthesis. This requires patients to be aware of their blood glucose level oscillations every day to deduce a pattern and anticipate future glycemia, and hence, decide the amount of insulin that must be exogenously injected to maintain glycemia within the target range. This approach often suffers from a relatively high imprecision, which can be dangerous. Nevertheless, current developments in Information and Communication Technologies (ICT) and innovative sensors for biological signals that might enable a continuous, complete assessment of the patient’s health provide a fresh viewpoint on treating DM1. With this, we observe that current biomonitoring devices and Continuous Glucose Monitoring (CGM) units can easily obtain data that allow us to know at all times the state of glycemia and other variables that influence its oscillations. A complete review has been made of the variables that influence glycemia in a T1DM patient and that can be measured by the above means. The communications systems necessary to transfer the information collected to a more powerful computational environment, which can adequately handle the amounts of data collected, have also been described. From this point, intelligent data analysis extracts knowledge from the data and allows predictions to be made in order to anticipate risk situations. With all of the above, it is necessary to build a holistic proposal that allows the complete and smart management of T1DM. This approach evaluates a potential shortage of such suggestions and the obstacles that future intelligent IoMT-DM1 management systems must surmount. Lastly, we provide an outline of a comprehensive IoMT-based proposal for DM1 management that aims to address the limits of prior studies while also using the disruptive technologies highlighted beforePartial funding for open access charge: Universidad de Málag

    Electronic Health Record Phenotyping in Cardiovascular Epidemiology

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    The secondary use of EHR data for research is a cost-effective resource for a variety of research questions and domains; however, there are many challenges when using electronic health record (EHR) data for epidemiologic research.This dissertation quantified differences in prevalence for acute myocardial infarction (MI) and heart failure (HF) using phenotyping algorithms differing in diagnosis position of ICD-10-CM codes and the inclusion of clinical components. The period of interest was January 1, 2016 to December 31, 2019 for UNC Clinical Data Warehouse for Health data and October 1, 2015 and December 31, 2019 for Atherosclerosis Risk in Communities (ARIC) Study data, the latter used for validation analyses. During the period of interest, 13,200 acute MI cases and 53,545 HF cases were identified in the UNC data. Age-standardized prevalence of acute MI and HF were highest using Any Diagnosis Position algorithm and lowest for acute MI using 1st or 2nd Diagnosis Position with Lab or Procedure and 1st Diagnosis Position for HF. Projected differences in healthcare expenditures by algorithm as well as patient and clinical characteristics, such as event severity and mortality, were also estimated. When compared to physician-adjudicated hospitalizations in the ARIC study, the phenotyping algorithms used for the UNC analysis performed well given their simplicity. The algorithm with the highest sensitivity was Any Diagnosis Position for acute MI and HF at 75.5% and 70.5%. Specificity, PPV, and NPV ranged from 80-99% for all algorithms. Requiring clinical components had little effect except for increasing PPV slightly, while restricting diagnosis position to 1st or 2nd position decreased sensitivity and increased PPV. The impact of clinical components or diagnosis position did not differ by race, age, or sex subgroups.The results from this dissertation can be used by researchers using EHR data for a variety of reasons from informing their own analytic decisions to validating their study findings. The continued use of EHR data for research requires transparency to facilitate reproducibility as well as studies focused on what we are measuring.Doctor of Philosoph

    Data Science in Healthcare

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    Data science is an interdisciplinary field that applies numerous techniques, such as machine learning, neural networks, and deep learning, to create value based on extracting knowledge and insights from available data. Advances in data science have a significant impact on healthcare. While advances in the sharing of medical information result in better and earlier diagnoses as well as more patient-tailored treatments, information management is also affected by trends such as increased patient centricity (with shared decision making), self-care (e.g., using wearables), and integrated care delivery. The delivery of health services is being revolutionized through the sharing and integration of health data across organizational boundaries. Via data science, researchers can deliver new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at the individual and population levels. This Special Issue focuses on how data science is used in healthcare (e.g., through predictive modeling) and on related topics, such as data sharing and data management

    Information Technology's Role in Global Healthcare Systems

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    Over the past few decades, modern information technology has made a significant impact on people’s daily lives worldwide. In the field of health care and prevention, there has been a progressing penetration of assistive health services such as personal health records, supporting apps for chronic diseases, or preventive cardiological monitoring. In 2020, the range of personal health services appeared to be almost unmanageable, accompanied by a multitude of different data formats and technical interfaces. The exchange of health-related data between different healthcare providers or platforms may therefore be difficult or even impossible. In addition, health professionals are increasingly confronted with medical data that were not acquired by themselves, but by an algorithmic “black box”. Even further, externally recorded data tend to be incompatible with the data models of classical healthcare information systems.From the individual’s perspective, digital services allow for the monitoring of their own health status. However, such services can also overwhelm their users, especially elderly people, with too many features or barely comprehensible information. It therefore seems highly relevant to examine whether such “always at hand” services exceed the digital literacy levels of average citizens.In this context, this reprint presents innovative, health-related applications or services emphasizing the role of user-centered information technology, with a special focus on one of the aforementioned aspects

    Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis

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    Cardiovascular diseases are one of the leading global causes of mortality. Currently, clinicians rely on their own analyses or automated analyses of the electrocardiogram (ECG) to obtain a diagnosis. However, both approaches can only include a finite number of predictors and are unable to execute complex analyses. Artificial intelligence (AI) has enabled the introduction of machine and deep learning algorithms to compensate for the existing limitations of current ECG analysis methods, with promising results. However, it should be prudent to recognize that these algorithms also associated with their own unique set of challenges and limitations, such as professional liability, systematic bias, surveillance, cybersecurity, as well as technical and logistical challenges. This review aims to increase familiarity with and awareness of AI algorithms used in ECG diagnosis, and to ultimately inform the interested stakeholders on their potential utility in addressing present clinical challenges

    IoT Applications Computing

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    The evolution of emerging and innovative technologies based on Industry 4.0 concepts are transforming society and industry into a fully digitized and networked globe. Sensing, communications, and computing embedded with ambient intelligence are at the heart of the Internet of Things (IoT), the Industrial Internet of Things (IIoT), and Industry 4.0 technologies with expanding applications in manufacturing, transportation, health, building automation, agriculture, and the environment. It is expected that the emerging technology clusters of ambient intelligence computing will not only transform modern industry but also advance societal health and wellness, as well as and make the environment more sustainable. This book uses an interdisciplinary approach to explain the complex issue of scientific and technological innovations largely based on intelligent computing

    Data Science Techniques for COVID-19 in Intensive Care Units

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    Data scientists aim to provide techniques and tools to the clinicians to manage the new coronavirus disease. Nowadays, new emerging tools based on Artificial Intelligence (AI), Image Processing (IP) and Machine Learning (ML) are contributing to the improvement of healthcare and treatments of different diseases. This paper reviews the most recent research efforts and approaches related to these new data driven techniques and tools in combination with the exploitation of the already available COVID-19 datasets. The tools can assist clinicians and nurses in efficient decision making with complex and heavily heterogeneous data, even in hectic and overburdened Intensive Care Units (ICU) scenarios. The datasets and techniques underlying these tools can help finding a more correct diagnosis. The paper also describes how these innovative AI+IP+ML-based methods (e.g., conventional X-ray imaging, clinical laboratory data, respiratory monitoring and automatic adjustments, etc.) can assist in the process of easing both the care of infected patients in ICUs and Emergency Rooms and the discovery of new treatments (drugs)
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