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Automated and enabling technologies for medicinal chemistry.

Progress in medicinal chemistry
Having always been driven by the need to get new treatments to patients as quickly as possible, drug discovery is a constantly evolving process. This chapter will review how medicinal chemistry was established, how it has changed over the years due t...

A Deep Learning-Based Unsupervised Method to Impute Missing Values in Patient Records for Improved Management of Cardiovascular Patients.

IEEE journal of biomedical and health informatics
Physicians increasingly depend on electronic health records (EHRs) to manage their patients. However, many patient records have substantial missing values that pose a fundamental challenge to their clinical use. To address this prevailing challenge, ...

Recognition of Thyroid Ultrasound Standard Plane Images Based on Residual Network.

Computational intelligence and neuroscience
Ultrasound is one of the critical methods for diagnosis and treatment in thyroid examination. In clinical application, many reasons, such as large outpatient traffic, time-consuming training of sonographers, and uneven professional level of physician...

The case for AI-driven cancer clinical trials - The efficacy arm in silico.

Biochimica et biophysica acta. Reviews on cancer
Pharmaceutical agents in oncology currently have high attrition rates from early to late phase clinical trials. Recent advances in computational methods, notably causal artificial intelligence, and availability of rich clinico-genomic databases have ...

A Machine Learning Approach for Mortality Prediction in COVID-19 Pneumonia: Development and Evaluation of the Piacenza Score.

Journal of medical Internet research
BACKGROUND: Several models have been developed to predict mortality in patients with COVID-19 pneumonia, but only a few have demonstrated enough discriminatory capacity. Machine learning algorithms represent a novel approach for the data-driven predi...

The human-in-the-loop: an evaluation of pathologists' interaction with artificial intelligence in clinical practice.

Histopathology
AIMS: One of the major drivers of the adoption of digital pathology in clinical practice is the possibility of introducing digital image analysis (DIA) to assist with diagnostic tasks. This offers potential increases in accuracy, reproducibility, and...

An extended TDM method under probabilistic interval-valued hesitant fuzzy environment for stock selection.

PloS one
Generally, in real decision-making, all the pieces of information are used to find the optimal alternatives. However, in many cases, the decision-makers (DMs) only want "how good/bad a thing can become." One possibility is to classify the alternative...

Reporting guidelines for artificial intelligence in healthcare research.

Clinical & experimental ophthalmology
Reporting guidelines are structured tools developed using explicit methodology that specify the minimum information required by researchers when reporting a study. The use of artificial intelligence (AI) reporting guidelines that address potential so...

A Discrete Joint Model for Entity and Relation Extraction from Clinical Notes.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
Extracting clinical concepts and their relations from clinical narratives is one of the fundamental tasks in clinical natural language processing. Traditional solutions often separate this task into two subtasks with a pipeline architecture, which fi...

Assessing the speed-accuracy trade-offs of popular convolutional neural networks for single-crop rib fracture classification.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Rib fractures are injuries commonly assessed in trauma wards. Deep learning has demonstrated state-of-the-art accuracy for a variety of tasks, including image classification. This paper assesses the speed-accuracy trade-offs and general suitability o...