Latest AI and machine learning research in prescriptions for healthcare professionals.
Current clinical practice guidelines for managing Coronary Artery Disease (CAD) account for general cardiovascular risk factors. However, they do not present a framework that considers personalized patient-specific characteristics. Using the electronic health records of 21,460 patients, we created data-driven models for personalized CAD management that significantly improve health outcomes relativ...
Despite huge technological advances in the capabilities to capture, store, link and analyse data electronically, there has been some but limited impact on routine pharmacovigilance. We discuss emerging research in the use of artificial intelligence, machine learning and automation across the pharmacovigilance lifecycle including pre-licensure. Reasons are provided on why adoption is challenging an...
Drug-target interactions (DTIs) play a key role in drug development and discovery processes. Wet lab prediction of DTIs is time-consuming, expensive, ...
Along with digitization, automatic data-driven decision support systems become increasingly popular. Mortality prediction is a vital part of that deci...
Facial photographs of the subjects are often used in the diagnosis process of orthognathic surgery. The aim of this study was to determine whether con...
Recently, various computational methods have been proposed to find new therapeutic applications of the existing drugs. The Multimodal Restricted Boltz...
Recently, considerable research has focused on personal assistant robots, and robots capable of rich human-like communication are expected. Among huma...
In order to track the desired path under unknown parameters and environmental disturbances, an adaptive backstepping sliding mode control algorithm wi...
A digital medical health system named Tianxia120 that can provide patients and hospitals with "one-step service" is proposed in this paper. Evolving f...
Intravenous (IV) medication administration processes have been considered as high-risk steps, because accidents during IV administration can lead to s...
Long non-coding RNA (LncRNA) and microRNA (miRNA) are both non-coding RNAs that play significant regulatory roles in many life processes. There is cum...
The antimicrobial activities of DC essential oil (EO) and hydroalcoholic extract (HE) were evaluated. The EO showed broad antimicrobial activity and ...
Immense amount of high-content image data generated in drug discovery screening requires computationally driven automated analysis. Emergence of advan...
Artificial intelligence (AI) has the potential to reshape pharmaceutical formulation development through its ability to analyze and continuously monit...
Drug repurposing or repositioning is a technique whereby existing drugs are used to treat emerging and challenging diseases, including COVID-19. Drug ...
The inverse relationship between the cost of drug development and the successful integration of drugs into the market has resulted in the need for inn...
BACKGROUND: Drug-target interaction prediction is of great significance for narrowing down the scope of candidate medications, and thus is a vital ste...
Stress is subjective and is manifested differently from one person to another. Thus, the performance of generic classification models that classify st...
Attractor neural networks such as the Hopfield model can be used to model associative memory. An efficient associative memory should be able to store ...
Accurately predicting essential genes using computational methods can greatly reduce the effort in finding them via wet experiments at both time and r...