Probing Pre-trained Language Models (PLMs) using prompts has indirectly implied that language models (LMs) can be treated as knowledge bases. To this end, this phenomena has been effective especially when these LMs are fine-tuned towards not just data of a specific domain, but also to the style or linguistic pattern of the prompts themselves. We observe that, satisfying a particular linguistic pattern in prompts is an unsustainable constraint that unnecessarily lengthens the probing task, especially because, they are often manually designed and the range of possible prompt template patterns can vary depending on the prompting objective and domain. We therefore explore an idea of using a position-attention mechanism to capture positional information of each word in a prompt relative to the mask to be filled, hence avoiding the need to re-construct prompts when the prompts linguistic pattern changes. Using our approach, we demonstrate the ability of eliciting answers to rare prompt templates (in a case study on health outcome generation) such as Postfix and Mixed patterns whose missing information is respectively at the start and in multiple random places of the prompt. More so, using various biomedical PLMs, our approach consistently outperforms a baseline in which the default mask language model (MLM) representation is used to predict masked tokens.
Prompting Large Language Models for Zero-Shot Clinical Prediction with Structured Longitudinal Electronic Health Record Data
Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；Centre for Medical Informatics, University of Edinburgh（爱丁堡大学医学信息学中心）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）；National Engineering Research Center for Software Engineering, Peking University（北京大学国家软件工程研究中心）；Health Data Research UK（英国健康数据研究中心）
Enhancing Depression Detection with Chain-of-Thought Prompting: From Emotion to Reasoning Using Large Language Models
College of Information Science and Engineering, Ritsumeikan University（立命馆大学信息科学与工程学院）；Fujita Health University（藤田医科大学）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
Large Language Models for Mental Health Diagnostic Assessments: Exploring The Potential of Large Language Models for Assisting with Mental Health Diagnostic Assessments -- The Depression and Anxiety Case
Indian Institute of Research and Science, Bhopal（博帕尔印度研究与科学学院）；Indian Institute of Technology, Kharagpur（克勒格布尔印度理工学院）
RephQA: Evaluating Readability of Large Language Models in Public Health Question Answering
Yale University, Department of Computer Science（耶鲁大学计算机科学系）；Yale University, School of Nursing（耶鲁大学护理学院）；Yale University, School of Public Health（耶鲁大学公共卫生学院）；Northeastern University（东北大学）
Towards Interpretable Mental Health Analysis with Large Language Models
The University of Manchester（曼彻斯特大学）；University of Helsinki（赫尔辛基大学）；Yale University（耶鲁大学）；Artificial Intelligence Research Center, AIST（日本国立研究开发法人产业技术综合研究所人工智能研究中心）；Jiangxi Normal University（江西师范大学）
Cognitive-Mental-LLM: Evaluating Reasoning in Large Language Models for Mental Health Prediction via Online Text
Ira A. Fulton Schools of Engineering Arizona State University Tempe, USA（伊拉·A·福林工程学校亚利桑那州立大学Tempe分校）；Department of Psychology Santa Clara University Santa Clara, USA（心理学系圣克拉拉大学Santa Clara分校）
Guiding Language Models to Be More Empathetic: Culturally Sensitive Mental Health Advice Generation Through Human-LLM Collaboration
Ahsanullah University of Science and Technology（阿山努拉科技大学）；Bangladesh University of Business and Technology（孟加拉国商业与技术大学）；The University of New South Wales（新南威尔士大学）；Jashore University of Science and Technology（贾绍尔科技大学）；American International University - Bangladesh（孟加拉国美国国际大学）
Enhancing LLMs for Identifying and Prioritizing Important Medical Jargons from Electronic Health Record Notes Utilizing Data Augmentation: A Comparative Study
Miner School of Computer and Information Sciences, UMass Lowell, MA, USA（UMass Lowell 计算机与信息科学学院）；Manning College of Information and Computer Sciences, UMass Amherst, MA, USA（UMass Amherst 信息与计算机科学学院）；Center for Healthcare Organization and Implementation Research, VA Bedford Health Care, MA, USA（VA Bedford医疗中心医疗组织与实施研究中心）
The advent of the Internet era has led to an explosive growth in the Electronic Health Records (EHR) in the past decades. The EHR data can be regarded as a collection of clinical events, including laboratory results, medication records, physiological indicators, etc, which can be used for clinical outcome prediction tasks to support constructions of intelligent health systems. Learning patient representation from these clinical events for the clinical outcome prediction is an important but challenging step. Most related studies transform EHR data of a patient into a sequence of clinical events…
A health outcome is a measurement or an observation used to capture and assess the effect of a treatment. Automatic detection of health outcomes from text would undoubtedly speed up access to evidence necessary in healthcare decision making. Prior work on outcome detection has modelled this task as either (a) a sequence labelling task, where the goal is to detect which text spans describe health outcomes, or (b) a classification task, where the goal is to classify a text into a pre-defined set of categories depending on an outcome that is mentioned somewhere in that text. However, this decoupl…
We study how language on social media is linked to diseases such as atherosclerotic heart disease (AHD), diabetes and various types of cancer. Our proposed model leverages state-of-the-art sentence embeddings, followed by a regression model and clustering, without the need of additional labelled data. It allows to predict community-level medical outcomes from language, and thereby potentially translate these to the individual level. The method is applicable to a wide range of target variables and allows us to discover known and potentially novel correlations of medical outcomes with life-style…
LocalTweets to LocalHealth: A Mental Health Surveillance Framework Based on Twitter Data
University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；University of Minnesota（明尼苏达大学）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；University of Colorado Anschutz Medical Campus（科罗拉多大学安舒茨医学校区）；University of Massachusetts, Chan Medical School（马萨诸塞大学陈医学院）
Investigating the Association Between Text-Based Indications of Foodborne Illness from Yelp Reviews and New York City Health Inspection Outcomes (2023)
Columbia University, Department of Computer Science（哥伦比亚大学计算机科学系）
Mental illness is one of the most pressing public health issues of our time. While counseling and psychotherapy can be effective treatments, our knowledge about how to conduct successful counseling conversations has been limited due to lack of large-scale data with labeled outcomes of the conversations. In this paper, we present a large-scale, quantitative study on the discourse of text-message-based counseling conversations. We develop a set of novel computational discourse analysis methods to measure how various linguistic aspects of conversations are correlated with conversation outcomes. A…
