Generating a summary from findings has been recently explored (Zhang et al., 2018, 2020) in note types such as radiology reports that typically have short length.
We developed a novel prompt-based clinical NLP framework called HealthPrompt and applied the paradigm of prompt-based learning on clinical texts.
In this study, we developed a rule-based NLP algorithm and machine learning models to automate the extraction of sleep-related concepts, including snoring, napping, sleep problem, bad sleep quality, daytime sleepiness, night wakings, and sleep duration, from the clinical notes of patients diagnosed with AD.
no code implementations • 19 Apr 2021 • Bhavani Singh Agnikula Kshatriya, Nicolas A Nunez, Manuel Gardea- Resendez, Euijung Ryu, Brandon J Coombes, Sunyang Fu, Mark A Frye, Joanna M Biernacka, Yanshan Wang
The experimental results indicate that our proposed approach is effective in identifying MDD phenotypes and that the Bio- Clinical BERT, a specific BERT model for clinical data, achieved the best performance in comparison with conventional machine learning models.
Keywords: Social and Behavioral Determinants of Health, Artificial Intelligence, Electronic Health Records, Natural Language Processing, Predictive Model
We performed two case studies: physical activity and excessive diet, in order to validate the effectiveness of BERT models in classifying lifestyle factors for AD.
However, such models are rarely applied and evaluated in the healthcare domain, to meet the information needs with accurate and up-to-date healthcare data.
no code implementations • 24 Oct 2019 • Sunyang Fu, David Chen, Huan He, Sijia Liu, Sungrim Moon, Kevin J Peterson, Feichen Shen, Li-Wei Wang, Yanshan Wang, Andrew Wen, Yiqing Zhao, Sunghwan Sohn, Hongfang Liu
Background Concept extraction, a subdomain of natural language processing (NLP) with a focus on extracting concepts of interest, has been adopted to computationally extract clinical information from text for a wide range of applications ranging from clinical decision support to care quality improvement.
The total lifetime cost of care for someone with dementia is estimated to be $350, 174 in 2018, 70% of which is associated with family-provided care.
Through this tutorial, we would like to introduce NLP methodologies and tools developed in the clinical domain, and showcase the real-world NLP applications in clinical research and practice at Mayo Clinic (the No.
Both unsupervised machine learning approaches could be leveraged to discover patient subgroups using EHRs but with different foci.
A subset of MedSTS (MedSTS_ann) containing 1, 068 sentence pairs was annotated by two medical experts with semantic similarity scores of 0-5 (low to high similarity).
First, the word embeddings trained on clinical notes and biomedical publications can capture the semantics of medical terms better, and find more relevant similar medical terms, and are closer to human experts' judgments, compared to these trained on Wikipedia and news.
In this paper, we propose a method that directly uses prices data to predict market index direction and stock price direction.