Latest AI and machine learning research in primary care for healthcare professionals.
Miniaturized and wearable sensor-based measurements offer unprecedented opportunities to study and assess human behavior in natural settings with wide ranging applications including in healthcare, wellness tracking and entertainment. However, wearable sensors are vulnerable to data loss due to body movement, sensor displacement, software malfunctions, etc. This generally hinders advanced data anal...
Monitoring blood pressure (BP) in people's daily life in an unobtrusive way is of great significance to prevent cardiovascular disease and its complications. However, most of the current cuff-less BP estimation methods still suffer from two drawbacks including calibration and tedious feature selection. In this study, we first attempt to validate the feasibility of convolutional autoencoder (CAE) t...
Sitting posture recognition can be used to evaluate the awareness of a person carrying out a task, such as working or driving, and can aid in avoiding...
Imaging fluorescent disease biomarkers in tissues and skin is a non-invasive method to screen for health conditions. We report an automated process th...
This paper explores the use of ensemble classification methods in the context of the diabetes disease. An analysis was carried out that formulates and...
In this work, we present FREGEX a method for automatically extracting features from biomedical texts based on regular expressions. Using Smith-Waterma...
Certain patterns of eating behaviour during meal have been identified as risk factors for long-term abnormal eating development in healthy individuals...
Combinations of healthcare claims data with additional datasets provide large and rich sources of information. The dimensionality and complexity of th...
OBJECTIVE: Autism spectrum disorder (ASD) screening can improve prognosis via early diagnosis and intervention, but lack of time and training can dete...
The artificial intelligence based on medical aid diagnosis has been in full swing in these years. How to better and more safely utilize this new techn...
Artificial intelligence (AI) has attained a new level of maturity in recent years and is developing into the driver of digitalization in all areas of ...
OBJECTIVES: Laparoscopic metabolic surgery (MxS) can lead to remission of type 2 diabetes (T2D); however, treatment response to MxS can be heterogeneo...
Although machine learning is increasingly being applied to support clinical decision making, there is a significant gap in understanding what it is an...
Artificial Intelligence (AI) and access to "Big Data" together with the evolving techniques in biotechnology will change the medical practice a big wa...
RATIONALE: Accumulating evidence implicates inflammation in pulmonary arterial hypertension (PAH) and therapies targeting immunity are under investiga...
PURPOSE: Many studies have proposed predictive models for type 2 diabetes mellitus (T2DM). However, these predictive models have several limitations, ...
This study identifies and ranks predictors of cardiovascular health at the neighborhood level in the United States. We merged the 500 Cities Data and ...
BACKGROUND: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the ...
Hyperlipidemia casts great threats to humans around the world. The systemic co-expression and function enrichment analysis for this disease is limited...
In 2005, global cardiovascular diseases caused 30% of deaths in Europe, which is 46% of total deaths for all death groups. Today, according to the Int...