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. In addition, the task is to classify patients with suspected myocardial infarction using machine learning methods. We have developed four models based on the k-nearest neighbor classifier, radial basis function, decision tree, and random forest to do this. An analysis of time parameters showed that the most significant parameters for diagnosing myocardial infraction are SDNN, BPM, and IBI. An experimental investigation was conducted on the data of the open PTB-XL dataset for patients with suspected myocardial infarction. The results showed that, according to the parameters of the short ECG, it is possible to classify patients with a suspected myocardial infraction as sick and healthy with high accuracy. The optimized Random Forest model showed the best performance with an accuracy of 99.63%, and a root mean absolute error is less than 0.004. The proposed novel approach can be used for patients who do not have other indicators of heart attacks.

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). Demographic information and questions tailored to the problem were collected via an online questionnaire. An online survey was chosen in part because of the pandemic conditions, and Facebook was chosen because of the widespread discussions of health topics on that platform. The results confirmed the central hypothesis that people who had experienced a disease are more willing to accept new medical technologies and showed that curiosity about new technologies and willingness to use them were significantly higher in the two groups currently or previously affected by COVID-19, whereas fears of being "monitored" (in the sense of surveillance) were significantly higher among people who had not experienced the disease and threat. Experiencing a serious disease ("patient experience") promotes acceptance of new medical technologies.

Evaluating the use of prostate-specific antigen as an instrument for early detection of prostate cancer beyond urologists: results of a representative cross-sectional questionnaire study of general practitioners and internal specialists.

OBJECTIVES: The aim of this cross-sectional study was to evaluate the value of prostate-specific antigen (PSA) testing as a tool for early detection of prostate cancer (PCa) applied by general practitioners (GPs) and internal specialists (ISs) as well as to assess criteria leading to the application of PSA-based early PCa detection. METHODS: Between May and December 2012, a questionnaire containing 16 items was sent to 600 GPs and ISs in the federal state Brandenburg and in Berlin (Germany). The independent influence of several criteria on the decision of GPs and ISs to apply PSA-based early PCa detection was assessed by multivariate logistic regression analysis (MLRA). RESULTS: 392 evaluable questionnaires were collected (return rate 65%). 81% of the physicians declared that they apply PSA testing for early PCa detection; of these, 58 and 15% would screen patients until the age of 80 and 90 years, respectively. In case of a pathological PSA level, 77% would immediately refer the patient to a urologist, while 13% would re-assess elevated PSA levels after 3-12 months. Based on MLRA, the following criteria were independently associated with a positive attitude towards PSA-based early PCa detection: specialisation (application of early detection more frequent for GPs and hospital-based ISs) (OR 3.12; p < 0.001), physicians who use exclusively GP or IS education (OR 3.95; p = 0.002), and physicians who recommend yearly PSA assessment after the age of 50 (OR 6.85; p < 0.001). CONCLUSIONS: GPs and ISs frequently apply PSA-based early PCa detection. In doing so, 13% would initiate specific referral to a urologist in case of pathological PSA values too late. Improvement of this situation could possibly result from specific educational activities for non-urological physicians active in fields of urological core capabilities, which should be guided by joint boards of the national associations of urology and general medicine.