Innovation

Innovation

At Allwyn, we are constantly breaking through barriers of existing thinking and driving towards innovative research and concepts. Our goal is to help organizations start with data transformation and drive towards operational transformation, using analytics and Artificial Intelligence.  We are here to furnish you with answers to key questions in your organization and to assist you in leveraging data to be your differentiator.

Our approach to innovation

Our approach to innovation is to bring industry and academia together — solving problems that are relevant to industry while leveraging thought leadership coming from academia.  We also believe in giving back to the community by preparing the next generation of technical talent for industry through this collaboration.   

One key area of focus at Allwyn is the application of AI in various sectors such as healthcare, finance and transportation. 

Here are a few innovative projects leveraging nationwide data and AI algorithms in health care —

Lobectomy and Mental Illness

By studying nationwide hospital admission data in the HCUP-NIS repository, of the people who have undergone lobectomy while also having severe mental illness diagnoses, we have developed a predictive algorithm for the length of stay and cost of stay. A provisional patent with the USPTO has been filed for this process. For more information on this, please read our white paper below.

Readmission of Lung Cancer patients

According to our research of diagnoses and various socioeconomic characteristics of lobectomy patients, we have developed an algorithm to predict the likelihood of readmission after a lobectomy.  We have studied various factors such as Medicare, patient’s age, gender, wage index and the population category of the patients along with their diagnosis code groups and many other features contribute to the classification for re-admission.  Please read our white paper below for more information on our approach and the data used.

AI in Medical Imaging

Obesity has become a chronic medical condition internationally, deserving of increased medical attention and research. One recent development within obesity research is interest to improve efficiency and accuracy of the calculation of the Visceral Fat Index (VFI) obesity measure. VFI calculates the overall composition of the body by measuring the adipose tissue that lies between organs in the torso and is currently manually calculated by physicians through a visual analysis of CT scans. This manual process involves locating the slice image at the third lumbar vertebra (L3) level in the spine before computing the VFI in that location of the body. In this white paper, we address the automatic detection of the L3 slice from CT scans.

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