<b>NATURAL LANGUAGE PROCESSING: HEALTHCARE ACHIEVING BENEFITS VIA NLP </b>

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NATURAL LANGUAGE PROCESSING: HEALTHCARE ACHIEVING BENEFITS VIA NLP

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Author(s): Adarsh Kumar ¹ ^, , ANJALI GOND ²

Publication date (Electronic preprint): 2 August 2023

Journal: ScienceOpen Preprints

Publisher: ScienceOpen

Keywords: Artificial intelligence, Advanced healthcare systems, Clinical Practice by using AI, medical industry, Clinical decision support.

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Abstract

The field of Natural Language Processing (NLP) within computer science presents a complex challenge due to the wide variety of linguistic nuances across different languages. NLP involves dividing language into semantic parts like parts of speech and phrases. Its origins trace back to the early 1940s during World War 2, driven by the need for autonomous language translation machinery. NLP, a part of AI technology, employs tools that concentrate on linguistic-conceptual relationships rather than just textual analysis, structuring and extracting meaningful data from unstructured text. One significant application of NLP is the advancement of the healthcare system. Electronic Health Record (EHR) systems revolutionized medical practice, enabling efficient diagnosis, elimination of errors, and faster treatment initiation. NLP's ability to interpret unstructured data from medical records facilitated quicker and more effective analysis, improving patient care. During the COVID-19 pandemic, EHR systems played a crucial role in coordinating patient care and surveillance. NLP also supports Clinical Decision Support Systems (CDSS), aiding medical decision-making by providing tailored clinical knowledge and patient information. Knowledge-based and non-knowledge-based CDSS utilize artificial intelligence, helping prevent medication errors and improving patient safety. The adoption of Voice Recognition (VR) and speech recognition tools, such as Dragon Medical One, surged among medical professionals globally, enhancing clinical documentation quality and saving time on transcription. NLP's impact extends to clinical trial matching, automating the process of selecting suitable patients based on specific criteria, thereby increasing efficiency, accuracy, and patient safety. The Internet of Medical Things (IoMT) is an emerging technology that connects various healthcare devices and wearable, providing real-time monitoring, improved patient outcomes, and remote patient care. Recent innovations, like the AI-based vision therapy software CureSee and AI's role in detecting Alzheimer's disease, have shown great promise in revolutionizing patient care and early disease detection.

To support these advancements, organizations like SyTrue use AI, machine learning, and NLP to improve payment integrity, risk adjustment, and chart review processes, leading to increased efficiency and higher ROI. Moreover, AI-powered clinical note generation using services like Amazon Transcribe simplifies the conversion of speech to text, enhancing medical documentation and facilitating data-driven decision-making.

In conclusion, the diverse applications of AI and NLP in healthcare have significantly improved the industry, enabling accurate diagnostics, personalized medicine, predictive analytics, drug discovery, remote monitoring, administrative efficiency, and innovative treatment approaches. As AI continues to evolve, its impact on the healthcare system promises to be transformative, leading to better patient outcomes, reduced costs, and improved accessibility to healthcare services.

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Author and article information

Journal

Title: ScienceOpen Preprints

Publisher: ScienceOpen

Publication date (Electronic preprint): 2 August 2023

Affiliations

[1 ] GLA UNIVERISTY, MATHURA;

[2 ] GLA UNIVERSITY, MATHURA;

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[* ]Email: singhadd25@ 123456gmail.com .

Author information

Adarsh Kumar https://orcid.org/0009-0005-9903-3586

Article

DOI: 10.14293/PR2199.000280.v1

SO-VID: 5edb13c4-bc86-4005-a59a-257d1a52f90a

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This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

History

Date received : 2 August 2023

References

Goyal Palash, Pandey Sumit, Jain Karan. Deep Learning for Natural Language Processing. 2018. Apress. [Cross Ref]
Shickel Benjamin, Tighe Patrick James, Bihorac Azra, Rashidi Parisa. Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis. IEEE Journal of Biomedical and Health Informatics. Vol. 22(5):1589–1604. 2018. Institute of Electrical and Electronics Engineers (IEEE). [Cross Ref]
Bell David, Baker John, Williams Chris, Bassin Levi. A Trend-Based Early Warning Score Can Be Implemented in a Hospital Electronic Medical Record to Effectively Predict Inpatient Deterioration. Critical Care Medicine. Vol. 49(10)2021. Ovid Technologies (Wolters Kluwer Health). [Cross Ref]
Satterfield Benjamin A., Dikilitas Ozan, Kullo Iftikhar J.. Leveraging the Electronic Health Record to Address the COVID-19 Pandemic. Mayo Clinic Proceedings. Vol. 96(6):1592–1608. 2021. Elsevier BV. [Cross Ref]
Sutton Reed T., Pincock David, Baumgart Daniel C., Sadowski Daniel C., Fedorak Richard N., Kroeker Karen I.. An overview of clinical decision support systems: benefits, risks, and strategies for success. npj Digital Medicine. Vol. 3(1)2020. Springer Science and Business Media LLC. [Cross Ref]
Onitilo Adedayo A., Shour Abdul R., Puthoff David S., Tanimu Yusuf, Joseph Adedayo, Sheehan Michael T. Evaluating the Adoption of Voice Recognition Technology for Real-Time Dictation in a Rural Healthcare System: A Retrospective Analysis of Dragon Medical One. Cold Spring Harbor Laboratory. [Cross Ref]
Cai Tianrun, Cai Fiona, Dahal Kumar P., Cremone Gabrielle, Lam Ethan, Golnik Charlotte, Seyok Thany, Hong Chuan, Cai Tianxi, Liao Katherine P.. Improving the Efficiency of Clinical Trial Recruitment Using an Ensemble Machine Learning to Assist With Eligibility Screening. ACR Open Rheumatology. Vol. 3(9):593–600. 2021. Wiley. [Cross Ref]
Srivastava Jyoti, Routray Sidheswar, Ahmad Sultan, Waris Mohammad Maqbool. Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress. Computational Intelligence and Neuroscience. Vol. 2022:1–17. 2022. Hindawi Limited. [Cross Ref]
Manickam Pandiaraj, Mariappan Siva Ananth, Murugesan Sindhu Monica, Hansda Shekhar, Kaushik Ajeet, Shinde Ravikumar, Thipperudraswamy S. P.. Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare. Biosensors. Vol. 12(8)2022. MDPI AG. [Cross Ref]
Dixit Suneel Kr. Efficacy of Vision Therapy Software (CureSee) in Amblyopia. Journal of Optometry and Ophthalmology. 2021. Mapsci Digital Publisher OPC Pvt. Ltd. [Cross Ref]
Silva-Spínola Anuschka, Baldeiras Inês, Arrais Joel P., Santana Isabel. The Road to Personalized Medicine in Alzheimer’s Disease: The Use of Artificial Intelligence. Biomedicines. Vol. 10(2)2022. MDPI AG. [Cross Ref]

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[1] Goyal Palash, Pandey Sumit, Jain Karan. Deep Learning for Natural Language Processing. 2018. Apress. [Cross Ref]

[2] Shickel Benjamin, Tighe Patrick James, Bihorac Azra, Rashidi Parisa. Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis. IEEE Journal of Biomedical and Health Informatics. Vol. 22(5):1589–1604. 2018. Institute of Electrical and Electronics Engineers (IEEE). [Cross Ref]

[3] Bell David, Baker John, Williams Chris, Bassin Levi. A Trend-Based Early Warning Score Can Be Implemented in a Hospital Electronic Medical Record to Effectively Predict Inpatient Deterioration. Critical Care Medicine. Vol. 49(10)2021. Ovid Technologies (Wolters Kluwer Health). [Cross Ref]

[4] Satterfield Benjamin A., Dikilitas Ozan, Kullo Iftikhar J.. Leveraging the Electronic Health Record to Address the COVID-19 Pandemic. Mayo Clinic Proceedings. Vol. 96(6):1592–1608. 2021. Elsevier BV. [Cross Ref]

[5] Sutton Reed T., Pincock David, Baumgart Daniel C., Sadowski Daniel C., Fedorak Richard N., Kroeker Karen I.. An overview of clinical decision support systems: benefits, risks, and strategies for success. npj Digital Medicine. Vol. 3(1)2020. Springer Science and Business Media LLC. [Cross Ref]

[6] Onitilo Adedayo A., Shour Abdul R., Puthoff David S., Tanimu Yusuf, Joseph Adedayo, Sheehan Michael T. Evaluating the Adoption of Voice Recognition Technology for Real-Time Dictation in a Rural Healthcare System: A Retrospective Analysis of Dragon Medical One. Cold Spring Harbor Laboratory. [Cross Ref]

[7] Cai Tianrun, Cai Fiona, Dahal Kumar P., Cremone Gabrielle, Lam Ethan, Golnik Charlotte, Seyok Thany, Hong Chuan, Cai Tianxi, Liao Katherine P.. Improving the Efficiency of Clinical Trial Recruitment Using an Ensemble Machine Learning to Assist With Eligibility Screening. ACR Open Rheumatology. Vol. 3(9):593–600. 2021. Wiley. [Cross Ref]

[8] Srivastava Jyoti, Routray Sidheswar, Ahmad Sultan, Waris Mohammad Maqbool. Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress. Computational Intelligence and Neuroscience. Vol. 2022:1–17. 2022. Hindawi Limited. [Cross Ref]

[9] Manickam Pandiaraj, Mariappan Siva Ananth, Murugesan Sindhu Monica, Hansda Shekhar, Kaushik Ajeet, Shinde Ravikumar, Thipperudraswamy S. P.. Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare. Biosensors. Vol. 12(8)2022. MDPI AG. [Cross Ref]

[10] Dixit Suneel Kr. Efficacy of Vision Therapy Software (CureSee) in Amblyopia. Journal of Optometry and Ophthalmology. 2021. Mapsci Digital Publisher OPC Pvt. Ltd. [Cross Ref]

[11] Silva-Spínola Anuschka, Baldeiras Inês, Arrais Joel P., Santana Isabel. The Road to Personalized Medicine in Alzheimer’s Disease: The Use of Artificial Intelligence. Biomedicines. Vol. 10(2)2022. MDPI AG. [Cross Ref]

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