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Big Data

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Big data in IBD: big progress for clinical practice.

Gut
IBD is a complex multifactorial inflammatory disease of the gut driven by extrinsic and intrinsic factors, including host genetics, the immune system, environmental factors and the gut microbiome. Technological advancements such as next-generation se...

Advanced Data Analytics for Clinical Research Part II: Application to Cardiothoracic Surgery.

Innovations (Philadelphia, Pa.)
In the first part of this series, we introduced the tools of Big Data, including Not Only Standard Query Language data warehouse, natural language processing (NLP), optical character recognition (OCR), and Internet of Things (IoT). There are nuances ...

Advanced Data Analytics for Clinical Research Part I: What are the Tools?

Innovations (Philadelphia, Pa.)
The concept of Big Data is changing the way that clinical research can be performed. Cardiothoracic surgeons need to understand the dynamic digital transformation taking place in the healthcare industry. In the last decade, technological advances and...

Ensuring trustworthy use of artificial intelligence and big data analytics in health insurance.

Bulletin of the World Health Organization
Technological advances in big data (large amounts of highly varied data from many different sources that may be processed rapidly), data sciences and artificial intelligence can improve health-system functions and promote personalized care and public...

Learning from Artificial Intelligence and Big Data in Health Care.

European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery

Machine learning and ligand binding predictions: A review of data, methods, and obstacles.

Biochimica et biophysica acta. General subjects
Computational predictions of ligand binding is a difficult problem, with more accurate methods being extremely computationally expensive. The use of machine learning for drug binding predictions could possibly leverage the use of biomedical big data ...

Generalized Pharmacometric Modeling, a Novel Paradigm for Integrating Machine Learning Algorithms: A Case Study of Metabolomic Biomarkers.

Clinical pharmacology and therapeutics
There is an unmet need for identifying innovative machine learning (ML) strategies to improve drug treatment regimens and therapeutic outcomes. We investigate Generalized Pharmacometric Modeling (GPM), a novel paradigm that integrates ML algorithms w...

Big Data Analytics and Sensor-Enhanced Activity Management to Improve Effectiveness and Efficiency of Outpatient Medical Rehabilitation.

International journal of environmental research and public health
Numerous societal trends are compelling a transition from inpatient to outpatient venues of care for medical rehabilitation. While there are advantages to outpatient rehabilitation (e.g., lower cost, more relevant to home and community function), the...

Structural Analysis and Optimization of Convolutional Neural Networks with a Small Sample Size.

Scientific reports
Deep neural networks have gained immense popularity in the Big Data problem; however, the availability of training samples can be relatively limited in specific application domains, particularly medical imaging, and consequently leading to overfittin...