![[PukiWiki]](image/pukiwiki.png "[PukiWiki]")
Digital services are growing in the health-care field. The population in Europe is aging, and digital services are on the rise. There are also plenty of new health-care devices on the market. The aim of this study was to survey how elderly people cope with digital services or devices, especially if they are chronically ill. This quantitative study focuses on the impact of chronic diseases on the use of health technology and digital services. The target group of this study is Finnish people aged 65 or over. Based on the results, a chronic disease or disability is not an obstacle to the use of digital services or health-care technology in the Finnish elderly population. The main obstacles to the use of health technology or digital services are complexity, obscure text, or small font size. According to this study, elderly people seem to trust the device or application. Devices, applications, and online services should be designed so that elderly people's diseases or ability to function are considered.We study seven fitness trackers and their associated smartphone apps from a wide variety of manufacturers, and record who they are talking to. Our results suggest that some of them communicate with unexpected third parties, including social networks, advertisement websites, weather services, and various external APIs. This implies that such unanticipated third-parties may glean personal information of users.Biostatistics and machine learning have been the cornerstone of a variety of recent developments in medicine. In order to gather large enough datasets, it is often necessary to set up multi-centric studies; yet, centralization of measurements can be difficult, either for practical, legal or ethical reasons. As an alternative, federated learning enables leveraging multiple centers' data without actually collating them. While existing works generally require a center to act as a leader and coordinate computations, we propose a fully decentralized framework where each center plays the same role. In this paper, we apply this framework to logistic regression, including confidence intervals computation. We test our algorithm on two distinct clinical datasets split among different centers, and show that it matches results from the centralized framework. In addition, we discuss possible privacy leaks and potential protection mechanisms, paving the way towards further research.The goal of this paper is to present a word-final target phoneme automated segmentation method based on cross-correlation coefficients computed between a reference sound wave and a sample sound wave. Most existing Speech Sound Disorder (SSD) Screening solutions require human intervention to a greater or lesser extent and use segmentation methods based on hard-coded time frames. Moreover, existing solutions extract features from the frequency domain, which entails large amounts of computational power to the detriment of real-time feedback. The pre-processing algorithm proposed in this paper, implemented in a Python version 3.7 script, automatically generates 2 new .wav files corresponding to the phonemes found in word-final position in the initial sound waves. The newly-generated .wav files are meant to be used as valid and homogeneous input in a subsequent classification stage aimed at rigorously discriminating mispronunciations of the target phoneme and assist Speech-Language Pathologists (SLPs) with the SSD screening.Citizens are ready and willing to use various kinds of e-health services and Web-based portals. The purpose of this study was to describe the experiences of patients who underwent an arrhythmia procedure of the guidance they received as well as their needs and expectations for a future digital care path. The goal for the future is to utilize the results in other patient-centered digital service development activities. The research material was collected in a two-part thematic interview with patients who underwent an electrophysiology examination and supraventricular tachycardia catheter ablation procedure (n=7) or ablation treatment for atrial fibrillation (n=4). The preliminary digital care path was modified based on the results. The arrhythmia patient's digital care path was tested in a workshop using a test group consisting of patients (n=3) and nursing staff (n=6). As a result, a digital care pathway for arrhythmia patients was completed.Pain management, assessment and documentation is a crucial part of patient care. However, several studies show flaws in pain management processes. https://www.selleckchem.com/products/Clopidogrel-bisulfate.html Documentation is not unified or even sufficient. The aim of this study was to describe how patient pain management has been recorded using the nursing diagnoses and nursing interventions of a standardized terminology, the Finnish Care Classification, (FinCC), and how that terminology should be further developed. The research data consisted of the daily nursing documentation notes of patient care episodes (n=806) during inpatient days (n=2564) at several specialty units (n=9). The documentation of pain management was found inadequate and insufficient. The results support the development of a new component, Pain management, and its attendant categories in the new version, FinCC 4.0, to help nurses document pain management in their daily work.One of the most important challenges in the scenario of COVID-19 is to design and develop decision support systems that can help medical staff to identify a cohort of patients that is more likely to have worse clinical evolution. To achieve this objective it is necessary to work on collected data, pre-process them in order to obtain a consistent dataset and then extract the most relevant features with advanced statistical methods like principal component analysis. As preliminary results of this research, very influential features that emerged are the presence of cardiac and liver illnesses and the levels of some inflammatory parameters at the moment of diagnosis.The objective of this study was to test the feasibility of automatically extracting and exploiting data from the YouTube platform, with a focus on the videos produced by the French YouTuber HugoDécrypte during COVID-19 quarantine in France. For this, we used the YouTube API, which allows the automatic collection of data and meta-data of videos. We have identified the main topics addressed in the comments of the videos and assessed their polarity. Our results provide insights on topics trends over the course of the quarantine and highlight users sentiment towards on-going events. The method can be expanded to large video sets to automatically analyse high amount of user-produced data.
