Data Intelligence

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.

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.

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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