Proceedings of the First Workshop on Natural Language Generation in Healthcare

Emiel Krahmer, Kathy McCoy, Ehud Reiter (Editors)


Anthology ID:
2022.nlg4health-1
Month:
July
Year:
2022
Address:
Waterville, Maine, USA and virtual meeting
Venue:
NLG4Health
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
URL:
https://aclanthology.org/2022.nlg4health-1
DOI:
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PDF:
https://aclanthology.org/2022.nlg4health-1.pdf

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Proceedings of the First Workshop on Natural Language Generation in Healthcare
Emiel Krahmer | Kathy McCoy | Ehud Reiter

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DrivingBeacon: Driving Behaviour Change Support System Considering Mobile Use and Geo-information
Jawwad Baig | Guanyi Chen | Chenghua Lin | Ehud Reiter

Natural Language Generation has been proved to be effective and efficient in constructing health behaviour change support systems. We are working on DrivingBeacon, a behaviour change support system that uses telematics data from mobile phone sensors to generate weekly data-to-text feedback reports to vehicle drivers. The system makes use of a wealth of information such as mobile phone use while driving, geo-information, speeding, rush hour driving to generate the feedback. We present results from a real-world evaluation where 8 drivers in UK used DrivingBeacon for 4 weeks. Results are promising but not conclusive.

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In-Domain Pre-Training Improves Clinical Note Generation from Doctor-Patient Conversations
Colin Grambow | Longxiang Zhang | Thomas Schaaf

Summarization of doctor-patient conversations into clinical notes by medical scribes is an essential process for effective clinical care. Pre-trained transformer models have shown a great amount of success in this area, but the domain shift from standard NLP tasks to the medical domain continues to present challenges. We build upon several recent works to show that additional pre-training with in-domain medical conversations leads to performance gains for clinical summarization. In addition to conventional evaluation metrics, we also explore a clinical named entity recognition model for concept-based evaluation. Finally, we contrast long-sequence transformers with a common transformer model, BART. Overall, our findings corroborate research in non-medical domains and suggest that in-domain pre-training combined with transformers for long sequences are effective strategies for summarizing clinical encounters.

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LCHQA-Summ: Multi-perspective Summarization of Publicly Sourced Consumer Health Answers
Abari Bhattacharya | Rochana Chaturvedi | Shweta Yadav

Community question answering forums provide a convenient platform for people to source answers to their questions including those related to healthcare from the general public. The answers to user queries are generally long and contain multiple different perspectives, redundancy or irrelevant answers. This presents a novel challenge for domain-specific concise and correct multi-answer summarization which we propose in this paper.

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Towards Development of an Automated Health Coach
Leighanne Hsu | Rommy Marquez Hernandez | Kathleen McCoy | Keith Decker | Ajith Vemuri | Greg Dominick | Megan Heintzelman

Human health coaching has been established as an effective intervention for improving clients’ health, but it is restricted in scale due to the availability of coaches and finances of the clients. We aim to build a scalable, automated system for physical activity coaching that is similarly grounded in behavior change theories. In this paper, we present our initial steps toward building a flexible system that is capable of carrying out a natural dialogue for goal setting as a health coach would while also offering additional support through just-in-time adaptive interventions. We outline our modular system design and approach to gathering and analyzing data to incrementally implement such a system.

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Personalizing Weekly Diet Reports
Elena Monfroglio | Lucas Anselma | Alessandro Mazzei

In this paper we present the main components of a weekly diet report generator (DRG) in natural language. The idea is to produce a text that contains information on the adherence of the dishes eaten during a week to the Mediterranean diet. The system is based on a user model, a database of the dishes eaten during the week and on the automatic computation of the Mediterranean Diet Score. All these sources of information are exploited to produce a highly personalized text.The system has two main goals, related to two different kinds of users: on the one hand, when used by dietitians, the main goal is to highlight the most salient medical information of the patient diet and, on the other hand, when used by final users, the main goal is to educate them toward a Mediterranean style of eating.