Machine Learning Methods in Predicting Patients with Suspected Myocardial Infarction Based on Short-Time HRV Data.

Diagnosis of cardiovascular diseases is an urgent task because they are the main cause of death for 32% of the world's population. Particularly relevant are automated diagnostics using machine learning methods in the digitalization of healthcare and introduction of personalized medicine in healthcare institutions, including at the individual level when designing smart houses. Therefore, this study aims to analyze short 10-s electrocardiogram measurements taken from 12 leads.

Does Being Ill Improve Acceptance of Medical Technology?-A Patient Survey with the Technology Usage Inventory.

Acceptance of new medical technology may be influenced by social conditions and an individual's background and particular situation. We studied this acceptance by hypothesizing that current and former COVID-19 patients would be more likely to accept an electrocardiogram (ECG) "patch" (attached to the chest) that allows continuous monitoring of the heart than individuals who did not have the disease and thus the respective experience. Currently infected COVID-19 patients, individuals who had recovered from COVID-19, and a control group were recruited online through Facebook (and Instagram) and through general practitioners (GPs).

Empirical Research on Acceptance of Digital Technologies in Medicine Among Patients and Healthy Users: Questionnaire Study.

Background: In recent years, interest in digital technologies such as electronic health, mobile health, telemedicine, big data, and health apps has been increasing in the health care sector. Acceptance and sustainability of these technologies play a considerable role for innovative health care apps.

Objective: This study aimed to identify the spread of and experience with new digital technologies in the medical sector in Germany.

Acceptance and Resistance of New Digital Technologies in Medicine: Qualitative Study.

Background: This study discusses the acceptance of new medical technologies in health care settings and resistance to these technologies from hospitals, doctors' surgical centers, electronic health (eHealth) centers, and related institutions. We suggest a novel method of identifying factors that influence the acceptance of, and resistance to, new technologies by medical staff and patients.

Objective: The objective of this study was to determine and evaluate the factors that influence acceptance and resistance to achieve a successful implementation of new technologies.