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Data Science

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A fusion of data science and feed-forward neural network-based modelling of COVID-19 outbreak forecasting in IRAQ.

Journal of biomedical informatics
BACKGROUND: Iraq is among the countries affected by the COVID-19 pandemic. As of 2 August 2020, 129,151 COVID-19 cases were confirmed, including 91,949 recovered cases and 4,867 deaths. After the announcement of lockdown in early April 2020, situatio...

Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes: The 2019 Literature Year in Review.

Computers, informatics, nursing : CIN
Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this art...

A review of AI and Data Science support for cancer management.

Artificial intelligence in medicine
INTRODUCTION: Thanks to improvement of care, cancer has become a chronic condition. But due to the toxicity of treatment, the importance of supporting the quality of life (QoL) of cancer patients increases. Monitoring and managing QoL relies on data ...

Prescreening in Oncology Using Data Sciences: The PreScIOUS Study.

Studies in health technology and informatics
The development of precision medicine in oncology to define profiles of patients who could benefit from specific and relevant anti-cancer therapies is essential. An increasing number of specific eligibility criteria are necessary to be eligible to ta...

A data science approach for early-stage prediction of Patient's susceptibility to acute side effects of advanced radiotherapy.

Computers in biology and medicine
The prediction by classification of side effects incidence in a given medical treatment is a common challenge in medical research. Machine Learning (ML) methods are widely used in the areas of risk prediction and classification. The primary objective...

Artificial intelligence and the future of life sciences.

Drug discovery today
Over the past few decades, the number of health and 'omics-related data' generated and stored has grown exponentially. Patient information can be collected in real time and explored using various artificial intelligence (AI) tools in clinical trials;...

From Utopia Through Dystopia: Charting a Course for Learning Analytics in Competency-Based Medical Education.

Academic medicine : journal of the Association of American Medical Colleges
The transition to the assessment of entrustable professional activities as part of competency-based medical education (CBME) has substantially increased the number of assessments completed on each trainee. Many CBME programs are having difficulty syn...

Surgical data science and artificial intelligence for surgical education.

Journal of surgical oncology
Surgical data science (SDS) aims to improve the quality of interventional healthcare and its value through the capture, organization, analysis, and modeling of procedural data. As data capture has increased and artificial intelligence (AI) has advanc...

Surgical data science - from concepts toward clinical translation.

Medical image analysis
Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional ...

Glycowork: A Python package for glycan data science and machine learning.

Glycobiology
While glycans are crucial for biological processes, existing analysis modalities make it difficult for researchers with limited computational background to include these diverse carbohydrates into workflows. Here, we present glycowork, an open-source...