AI-Based Natural Language Processing for the Generation of Meaningful Information Electronic Health Record (EHR) Data

Kaswan, Kuldeep Singh; Gaur, Loveleen; Dhatterwal, Jagjit Singh; Kumar, Rajesh

doi:10.1201/9781003126164-3

Cited by 14 publications

(6 citation statements)

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“…AI-based natural language processing for the generation of meaningful information electronic health record (EHR) data (Kaswan et al, 2021) This paper discusses how natural language processing (NLP) converts paper records to electronic health record systems (EHR). This reduces treatment time and eases patient management.…”

Section: Ai In-patient Managementmentioning

confidence: 99%

Smart Health

Yang

Siau

Xie

et al. 2022

Journal of Organizational and End User Computing

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In recent decades, healthcare organizations around the world have increasingly appreciated the value of information technologies for a variety of applications. Three of the new technological advancements that are impacting smart health are metaverse, artificial intelligence (AI), and data science. The metaverse is the intersection of three major technologies — AI, augmented reality (AR), and virtual reality (VR). Metaverse provides new possibilities and potential that are still emerging. The increased work efficiency enabled by artificial intelligence and data science in hospitals not only improves patient care but also cuts costs and workload for healthcare providers.The availability of big data enables data scientists to use the data for descriptive, predictive, and prescriptive analytics. This article reviews multiple case studies and the literature on AI and data science applications in hospital administration. The article also presents unresolved research questions and challenges in the applications of the metaverse, AI, and data science in the smart health context.

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Section: Ai In-patient Managementmentioning

confidence: 99%

Smart Health

Yang

Siau

Xie

et al. 2022

Journal of Organizational and End User Computing

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“…Significant progress has been achieved in our understanding of the polygenic, complex, and heterogeneous disease pathways due to the advancement of disease models in vivo and in vitro (Xu et al, 2021). Diseases that can be studied through translational Frontiers in Pharmacology frontiersin.org research include neurodegenerative disorders, such as AD, PD, multiple sclerosis, HD, and ALS, and psychiatric disorders, such as major depressive disorder, bipolar disorder, substance abuse disorder, post-traumatic stress disorder, anxiety disorder, schizophrenia, somatic symptom disorder, autism spectrum disorder, and hyperactive ataxia (Kaswan et al, 2021). There are clinician guides for using neuroscience to guide case framework, understand psychotherapeutic techniques, aid in treatment personalization and outcome prediction, and develop novel mechanistically targeted treatments for disorders (Shirbandi et al, 2021).…”

Section: Traditional Holistic Approach For the Management Of Neurolog...mentioning

confidence: 99%

“…Neuroinformatics is the study of the neurological system via the development of databases and tools that aims to design and manage web-accessible databases of experimental and computational data and novel software tools that are necessary for understanding the nervous system in diseased and healthy states ( Pu and Li, 2018 ; Usman et al, 2022 ). Brain imaging using positron emission tomography ( Kaswan et al, 2021 ; Ruiz-Olazar et al, 2021 ), functional magnetic resonance imaging ( Stefanovski et al, 2021 ; Li et al, 2022 ), electroencephalography ( Wojcik et al, 2018 ; Shirbandi et al, 2021 ), magnetoencephalography ( Gorina-Careta et al, 2021 ), and other methods; several electrophysiological recording methods; and clinical neurological data are examples of neuroinformatics ( Sharma et al, 2019 ). In an interesting study, 679 flavonoid-based compounds and their 481 relative targets were screened, and their bioinformatic analysis exhibited multiple pharmacological pathways, especially for neuronal diseases.…”

Section: Traditional Holistic Approach For the Management Of Neurolog...mentioning

confidence: 99%

Natural product-based pharmacological studies for neurological disorders

Puri

Kanojia²,

Sharma³

et al. 2022

Front. Pharmacol.

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Central nervous system (CNS) disorders and diseases are expected to rise sharply in the coming years, partly because of the world’s aging population. Medicines for the treatment of the CNS have not been successfully made. Inadequate knowledge about the brain, pharmacokinetic and dynamic errors in preclinical studies, challenges with clinical trial design, complexity and variety of human brain illnesses, and variations in species are some potential scenarios. Neurodegenerative diseases (NDDs) are multifaceted and lack identifiable etiological components, and the drugs developed to treat them did not meet the requirements of those who anticipated treatments. Therefore, there is a great demand for safe and effective natural therapeutic adjuvants. For the treatment of NDDs and other memory-related problems, many herbal and natural items have been used in the Ayurvedic medical system. Anxiety, depression, Parkinson’s, and Alzheimer’s diseases (AD), as well as a plethora of other neuropsychiatric disorders, may benefit from the use of plant and food-derived chemicals that have antidepressant or antiepileptic properties. We have summarized the present level of knowledge about natural products based on topological evidence, bioinformatics analysis, and translational research in this review. We have also highlighted some clinical research or investigation that will help us select natural products for the treatment of neurological conditions. In the present review, we have explored the potential efficacy of phytoconstituents against neurological diseases. Various evidence-based studies and extensive recent investigations have been included, which will help pharmacologists reduce the progression of neuronal disease.

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“…SA is also a machine learning technique that uses Natural language Processing (NLP) to ascertain individuals' sentiments through their comments, tweets, and reviews [24]. SA technique is highly relevant for businesses for getting the customer's insights [25]. The sentiments expressed on social media platforms can assist in predicting major and minor fluctuations in businesses.…”

Section: Literature Reviewmentioning

confidence: 99%

A Latent Dirichlet Allocation Technique for Opinion Mining of Online Reviews of Global Chain Hotels

Afaq

Gaur

Singh

2022

2022 3rd International Conference on Intelligent Engineering and Management (ICIEM)

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The hospitality industry has faced unprecedented challenges with the outbreak of Covid-19, which has changed customers' expectations. Therefore, it is essential to identify customers' new perceptions and expectations that lead to positive and negative opinions towards the service providers. Accordingly, this study aims to perform topic modeling and sentiment analysis on 94,200 online reviews of five global chain hotels in South Asia. Topic modeling as a text mining, unsupervised machine learning technique can decipher topics from a corpus such as online reviews, online reports, news covers, etc. In this study, the data is extracted from Trip Advisor through web scraping. Topic modeling is performed using the Latent Dirichlet Approach (LDA) on the extracted data set to analyze the key topics mentioned by the customers in the online reviews. The analysis depicted that cleanliness, food, staff, and service were the main concerns of the hotel guests. Furthermore, the findings represented that the main issues impacting the hotel guests were service delays. However, food and services were the keywords with the maximum word count as depicted by topic modeling.

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AI-Based Natural Language Processing for the Generation of Meaningful Information Electronic Health Record (EHR) Data

Cited by 14 publications

References 0 publications

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Natural product-based pharmacological studies for neurological disorders

A Latent Dirichlet Allocation Technique for Opinion Mining of Online Reviews of Global Chain Hotels

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