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Natural Language Processing to Detect Cognitive Concerns in Electronic Health Records Using Deep Learning [article]

Zhuoqiao Hong, Colin G. Magdamo, Yi-han Sheu, Prathamesh Mohite, Ayush Noori, Elissa M. Ye, Wendong Ge, Haoqi Sun, Laura Brenner, Gregory Robbins, Shibani Mukerji, Sahar Zafar (+7 others)
2020 arXiv   pre-print
In order to identify patients with cognitive concerns in electronic medical records, we applied natural language processing (NLP) algorithms and compared model performance to a baseline model that used  ...  An attention-based deep learning model outperformed the baseline model and other simpler models.  ...  In this study, we aim to use NLP to detect signs of cognitive dysfunction from clinician notes in electronic health records (EHR) by applying deep learning techniques that have not been hitherto applied  ... 
arXiv:2011.06489v1 fatcat:rmrbtp57tnefpbnskbq2pzs7he

Development and Validation of a Deep Learning Model for Earlier Detection of Cognitive Decline From Clinical Notes in Electronic Health Records

Liqin Wang, John Laurentiev, Jie Yang, Ying-Chih Lo, Rebecca E. Amariglio, Deborah Blacker, Reisa A. Sperling, Gad A. Marshall, Li Zhou
2021 JAMA Network Open  
Clinical notes in longitudinal electronic health records (EHRs) provide opportunities to detect cognitive decline earlier than it is noted in structured EHR fields as formal diagnoses.  ...  To develop and validate a deep learning model to detect evidence of cognitive decline from clinical notes in the EHR.  ...  We may use different approaches to address these issues, for example, rule-based natural language processing to handle negations and family history, 4 increasing the size of the training data set, and  ... 
doi:10.1001/jamanetworkopen.2021.35174 pmid:34792589 pmcid:PMC8603078 fatcat:kemhxnltjzfdjfy6vagfilck6m

Using Deep Learning to Identify Patients with Cognitive Impairment in Electronic Health Records [article]

Tanish Tyagi
2021 arXiv   pre-print
Information relevant to cognitive impairment (CI) is often found within electronic health records (EHR), but manual review of clinician notes by experts is both time consuming and often prone to errors  ...  We developed natural language processing (NLP) tools to identify patients with cognitive impairment and demonstrate that linguistic context enhances performance for the cognitive impairment classification  ...  Our work illustrates the need of more complex, expressive language models for the nuanced task of detecting dementia in electronic health records.  ... 
arXiv:2111.09115v1 fatcat:z4xlsnbbajg4rcbhmk355kyibu

Artificial intelligence-enabled healthcare delivery

Sandeep Reddy, John Fox, Maulik P Purohit
2018 Journal of the Royal Society of Medicine  
In recent years, there has been massive progress in artificial intelligence (AI) with the development of deep neural networks, natural language processing, computer vision and robotics.  ...  These techniques are now actively being applied in healthcare with many of the health service activities currently being delivered by clinicians and administrators predicted to be taken over by AI in the  ...  The natural language processing aims to bridge the divide between the languages that humans and computers use to operate.  ... 
doi:10.1177/0141076818815510 pmid:30507284 pmcid:PMC6348559 fatcat:7u5r7jgr7jga7jznyufzugd2gm

NeuraHealthNLP: An Automated Screening Pipeline to Detect Undiagnosed Cognitive Impairment in Electronic Health Records with Deep Learning and Natural Language Processing [article]

Tanish Tyagi
2022 arXiv   pre-print
To understand the linguistic context from complex language structures in EHR, a database of 8,656 sequences was constructed to train attention-based deep learning natural language processing model to classify  ...  Information relevant to CI is often found in the electronic health records (EHRs) and can provide vital clues for early diagnosis, but a manual review by experts is tedious and error prone.  ...  To understand the linguistic context from complex language structures in EHR, a database of 8,656 sequences was constructed to train attention-based deep learning natural language processing model to classify  ... 
arXiv:2202.00478v1 fatcat:tshedbdfjvfg5e2u6aj3dxtoou

A Year of Papers Using Biomedical Texts: Findings from the Section on Natural Language Processing of the IMIA Yearbook

Natalia Grabar, Cyril Grouin, Section Editors for the IMIA Yearbook Section on Natural Language Processing
2019 IMIA Yearbook of Medical Informatics  
Objectives: To analyze the content of publications within the medical Natural Language Processing (NLP) domain in 2018.  ...  We also proposed an analysis of the content of main research trends of NLP publications in 2018. Conclusions: The year 2018 is very rich with regard to NLP issues and topics addressed.  ...  As the research advances, we expect that other application domains may become concerned. In 2018, researchers addressed some novel issues and used original approaches.  ... 
doi:10.1055/s-0039-1677937 pmid:31419835 pmcid:PMC6697498 fatcat:7sg3ogylvrhqfidbclr3f3g7pe

Measuring Cognitive Status from Speech in a Smart Home Environment

Kathleen C. Fraser, Majid Komeili
2021 IEEE Instrumentation & Measurement Magazine  
Cognitive health is a key component to independence and well-being in old age, and smart homes present many opportunities to measure cognitive status in a continuous, unobtrusive manner.  ...  In this article, we focus on speech as a measurement instrument for cognitive health.  ...  Technological advances in natural language processing and machine learning are making virtual assistants more capable.  ... 
doi:10.1109/mim.2021.9513645 fatcat:3xyd7gkdonhjba7yf4ns3lktqm

EEG Pathology Detection based on Deep Learning

Musaed Alhussein, Ghulam Muhammad, M. Shamim Hossain
2019 IEEE Access  
INDEX TERMS EEG pathology, deep learning, EEG processing,  ...  In this paper, we propose an automatic electroencephalogram (EEG) pathology detection system based on deep learning. Various types of pathologies can affect brain signals.  ...  A system for natural language processing was developed in [21] to answer questions in a human-like manner. Big data analytics were also performed in [22] using cognitive technology.  ... 
doi:10.1109/access.2019.2901672 fatcat:7mdgs66m65g2zmwxwasvuewn34

Active Monitoring of Adverse Drug Reactions with Neural Network Technology

Chang-Chun Gao, Tao Wu, Jing-Sheng Lin, Jia-Ling Zha
2017 Chinese Medical Journal  
At present, neural networks and deep learning provide the best solution for many problems in image and speech recognition, and in natural language processing. In 2006, Prof.  ...  Hinton, the founder of the academic field of deep learning and a professor at the University of Toronto, Canada, based on the hierarchical nature of human brain cognitive processes, proposed to increase  ... 
doi:10.4103/0366-6999.207468 pmid:28584215 pmcid:PMC5463482 fatcat:ojql5mlnavhi3i6cuktqhfmxdy

Artificial Intelligence Approaches to Predicting and Detecting Cognitive Decline in Older Adults: A Conceptual Review

Sarah A. Graham, Ellen E. Lee, Dilip V. Jeste, Ryan Van Patten, Elizabeth W. Twamley, Camille Nebeker, Yasunori Yamada, Ho-Cheol Kim, Colin A. Depp
2019 Psychiatry Research  
For each category we provide examples of AI approaches, including supervised and unsupervised ML, deep learning, and natural language processing.  ...  Artificial Intelligence (AI) in healthcare is the use of computational algorithms that mimic human cognitive functions to analyze complex medical data.  ...  Acknowledgments This study was supported, in part, by the National Institute of Mental Health T32 Geriatric Mental Health Program (grant MH019934 to DVJ [PI]), NIMH K23MH119375-01 (PI: EEL), the IBM Research  ... 
doi:10.1016/j.psychres.2019.112732 pmid:31978628 pmcid:PMC7081667 fatcat:gcfvge3wwrdt5kp6cv35iyijd4

Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications [article]

Shaoxiong Ji and Shirui Pan and Xue Li and Erik Cambria and Guodong Long and Zi Huang
2020 arXiv   pre-print
Domain-specific applications of suicidal ideation detection are also reviewed according to their data sources, i.e., questionnaires, electronic health records, suicide notes, and online user content.  ...  engineering or deep learning for automatic detection based on online social contents.  ...  Electronic Health Records The increasing volume of electronic health records (EHRs) has paved the way for machine learning techniques for suicide attempter prediction.  ... 
arXiv:1910.12611v2 fatcat:63z4uvh5zrgyzb2bawtlbuo34m

Cognitive Computing-Based CDSS in Medical Practice

Jun Chen, Chao Lu, Haifeng Huang, Dongwei Zhu, Qing Yang, Junwei Liu, Yan Huang, Aijun Deng, Xiaoxu Han
2021 Health Data Science  
From the diagnosis of diseases till the generation of treatment plans, cognitive computing encompasses both data-driven and knowledge-driven machine intelligence to assist health care roles in clinical  ...  The last decade has witnessed the advances of cognitive computing technologies that learn at scale and reason with purpose in medicine studies.  ...  For medical images, deep learning models can be used to detect salience objects, segment lesions, and classify the types of lesions.  ... 
doi:10.34133/2021/9819851 fatcat:iiq3i22yszec7g2zi333u2adea

Application of Artificial Intelligence on Psychological Interventions and Diagnosis: An Overview

Sijia Zhou, Jingping Zhao, Lulu Zhang
2022 Frontiers in Psychiatry  
health disorders," "artificial intelligence," and "deep learning."  ...  as deep learning (DL) and AI is affecting psychological assessment and psychotherapy, we performed a search on PUBMED, and Web of Science using the terms "psychological interventions," "diagnosis on mental  ...  (32) revealed that Natural language processing of electronic health records in social media can be used to analyse people's mental health status on a daily basis.  ... 
doi:10.3389/fpsyt.2022.811665 pmid:35370846 pmcid:PMC8968136 fatcat:ptuubkzb5jczzkaz64szuhey6q

Artificial Intelligence for Mental Healthcare: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom

Ellen E. Lee, John Torous, Munmun De Choudhury, Colin A. Depp, Sarah A. Graham, Ho-Cheol Kim, Martin P. Paulus, John H. Krystal, Dilip V. Jeste
2021 Biological Psychiatry: Cognitive Neuroscience and Neuroimaging  
While published research on AI in neuropsychiatry is rather limited, there is a growing number of successful examples of AI's use with electronic health records, brain imaging, sensor-based monitoring  ...  However, the use of AI in mental healthcare and neurobiological research has been modest.  ...  Koleck TA, Dreisbach C, Bourne PE, Bakken S (2019): Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review.  ... 
doi:10.1016/j.bpsc.2021.02.001 pmid:33571718 pmcid:PMC8349367 fatcat:o2mnj3wmfjaq5pno45giyyzxny

Selected articles from the Third International Workshop on Semantics-Powered Data Analytics (SEPDA 2018)

Zhe He, Jiang Bian, Cui Tao, Rui Zhang
2019 BMC Medical Informatics and Decision Making  
In this editorial, we first summarize the Third International Workshop on Semantics-Powered Data Analytics (SEPDA 2018) held on December 3, 2018 in conjunction with the 2018 IEEE International Conference  ...  on Bioinformatics and Biomedicine (BIBM 2018) in Madrid, Spain, and then briefly introduce five research articles included in this supplement issue, covering topics including Data Analytics, Data Visualization  ...  Acknowledgments The Guest Editors of this supplement would like to thank the authors and the reviewers for their scientific contribution and congratulate them on their high quality work.  ... 
doi:10.1186/s12911-019-0855-3 pmid:31391050 pmcid:PMC6686213 fatcat:jnw2rysftvcixl7bmwnmwftkzi
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