Webcasts

Clinical models are often static in nature, homogenized in patient population and in patient comparisons and treatment decision making.

Clinical models are often static in nature, homogenized in patient population and in patient comparisons and treatment decision making.

Over the past two decades Biomedical Engineering has emerged as a major discipline that bridges societal needs of human health care with development of novel engineering methods and technologies.

The widespread adoption of Electronic Health Records (EHRs) has enabled the use of clinical data for clinical research and practice.

The Healthcare industry in the United States is under digital transformation to address several macroeconomic and socioeconomic challenges including high costs and low quality, increase in federal regulations and policies, better expectation on patient experience, health inequities, hospital consolidation, and so on.

Digital health is rapidly expanding due to surging healthcare costs, deteriorating health outcomes, and the growing prevalence and accessibility of mobile health and wearable technologies.

Real-world biomedical problems often involve complex biological systems that consist of numerous interacting entities.

We are witnessing a maternal health crisis with increasing rates of maternal death, preterm births and widening of disparities in care globally.

Today’s ICUs face many critical barriers to achieving continuous monitoring of the patients.

With the rapid progress of genome studies, many missense single-nucleotide polymorphisms (SNPs) in populations of somatic cells of different diseases such as cancer have been identified.