ULTRE (Using Machine Learning to Triage for Religious Struggle)

ULTRE was an initiative started at Cincinnati Children's Hospital Medical Center to help chaplains screen more efficiently for those patients that are undergoing religious struggle. The goal was to build a decision support tool that can be used by hospital chaplains to help them prioritize their meetings with families who may be in spiritual crisis.

The prayers in hospital chapel prayer books were electronically transcribed. Each prayer was then annotated by chaplains from the United States and the United Kingdom based on a particular ontology. That is, each chaplain annotator identified whether certain themes, such as “Questioning where God is” and “Expression of Guilt”, occurred in the prayer. Natural language processing techniques (e.g., machine learning) were then used to build a mathematical model that could be used to automatically identify those prayers containing religious struggle.

Multiple approaches were applied to analyze the prayers. For instance, one analysis involved comparing the sentiments (e.g., positive, negative, etc.) expressed in prayers written in English and Spanish. Another analyses involved directly building machine learning (automated) classifiers that used the frequencies of words in the prayers to identify religious struggle.