Carolyn Dickens


Heart Failure Education of African American and Hispanic/Latino Patients: Data Collection and Analysis
Itika Gupta | Barbara Di Eugenio | Devika Salunke | Andrew Boyd | Paula Allen-Meares | Carolyn Dickens | Olga Garcia
Proceedings of the First Workshop on Natural Language Processing for Medical Conversations

Heart failure is a global epidemic with debilitating effects. People with heart failure need to actively participate in home self-care regimens to maintain good health. However, these regimens are not as effective as they could be and are influenced by a variety of factors. Patients from minority communities like African American (AA) and Hispanic/Latino (H/L), often have poor outcomes compared to the average Caucasian population. In this paper, we lay the groundwork to develop an interactive dialogue agent that can assist AA and H/L patients in a culturally sensitive and linguistically accurate manner with their heart health care needs. This will be achieved by extracting relevant educational concepts from the interactions between health educators and patients. Thus far we have recorded and transcribed 20 such interactions. In this paper, we describe our data collection process, thematic and initiative analysis of the interactions, and outline our future steps.


A Quantitative Analysis of Patients’ Narratives of Heart Failure
Sabita Acharya | Barbara Di Eugenio | Andrew Boyd | Richard Cameron | Karen Dunn Lopez | Pamela Martyn-Nemeth | Debaleena Chattopadhyay | Pantea Habibi | Carolyn Dickens | Haleh Vatani | Amer Ardati
Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue

Patients with chronic conditions like heart failure are the most likely to be re-hospitalized. One step towards avoiding re-hospitalization is to devise strategies for motivating patients to take care of their own health. In this paper, we perform a quantitative analysis of patients’ narratives of their experience with heart failure and explore the different topics that patients talk about. We compare two different groups of patients- those unable to take charge of their illness, and those who make efforts to improve their health. We will use the findings from our analysis to refine and personalize the summaries of hospitalizations that our system automatically generates.


Towards Generating Personalized Hospitalization Summaries
Sabita Acharya | Barbara Di Eugenio | Andrew Boyd | Richard Cameron | Karen Dunn Lopez | Pamela Martyn-Nemeth | Carolyn Dickens | Amer Ardati
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop

Most of the health documents, including patient education materials and discharge notes, are usually flooded with medical jargons and contain a lot of generic information about the health issue. In addition, patients are only provided with the doctor’s perspective of what happened to them in the hospital while the care procedure performed by nurses during their entire hospital stay is nowhere included. The main focus of this research is to generate personalized hospital-stay summaries for patients by combining information from physician discharge notes and nursing plan of care. It uses a metric to identify medical concepts that are Complex, extracts definitions for the concept from three external knowledge sources, and provides the simplest definition to the patient. It also takes various features of the patient into account, like their concerns and strengths, ability to understand basic health information, level of engagement in taking care of their health, and familiarity with the health issue and personalizes the content of the summaries accordingly. Our evaluation showed that the summaries contain 80% of the medical concepts that are considered as being important by both doctor and nurses. Three patient advisors (i.e. individuals who are trained in understanding patient experience extensively) verified the usability of our summaries and mentioned that they would like to get such summaries when they are discharged from hospital.