AI for Health

Recent Key efforts from The Innovation Center For Biomedical Informatics (ICBI)

  • The Innovation Center For Biomedical Informatics (ICBI) at Georgetown University has been collaborating with the US Dept. of Veterans Affairs (VA) and the Georgetown Department of Psychiatry to develop innovative e-Mental Health interventions that leverage our expertise in mHealth and telemedicine. Most recently, we mined acoustic and semantic features from audio interviews to predict suicidal tendencies in military veterans. Using the 208 narrative audios collected from veterans, a classifier was built that differentiates suicidal from non-suicidal veterans based on acoustic features of speech and sentiment analysis of the transcribed narratives. The classifier correctly identified veterans with suicidal ideation from with an overall accuracy of 86.2% and area under the receiver operating characteristic curve (AUC) equal to 83.6%. This work, done using tools such as Google speech-to-text and Natural Language Processing (NLP) APIs and Watson Tone Analyzer, was presented in a poster session at the Technology in Psychiatry Summit 2018. Correlating different types of data about the veterans will help identify veterans at higher risk of suicide in a clinical setting. Figure 1 shows the workflow diagram for this project.

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Figure 1: Study Workflow For the Suicide Ideation Detection Project

  • The ICBI team participated in the 2018 Data Science Bowl. The Data Science Bowl is a worldwide competition that brings together data scientists, technologists, and domain experts across industries to take on the world’s challenges with data and technology. In the 2018 Data Science Bowl challenge, the aim was to identify the nuclei in divergent microscopy images, regardless of the experimental setup, over a period of 90 days. Our team approached this automated identification challenge in three different ways and scored in the top 12% out of more than 68,000 algorithms that were submitted. Figure 2 shows an example of automated recognition of the nuclei from pathology images using a deep learning algorithm.

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Figure 2: The image shows the automated recognition of the nuclei from pathology images using a deep learning algorithm

  • We are currently working on integrating molecular data with information extracted from radiology MRI images from a large collection of brain cancer patients, called the REMBRANDT study. The goal of this computational analysis is to better understand how immune cells affect clinical outcome and survival in brain cancer patients, and ultimately improving treatment options.
  • In the past, we have used machine learning Support Vector Method (SVM) algorithms to integrate genome wide multi-modal molecular data from cancer patients. Gene expression, microRNA, copy number and metabolomics data from urine and serum were integrated to identify biomarkers strongly associated with recurrence (Madhavan et al, Frontiers in Genetics, 2013). Figure 3 shows the Bioinformatics workflow of this multivariate analysis

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Figure 3: Bioinformatics workflow of multivariate analysis

Artificial Intelligence efforts from our collaborators at Georgetown University