6657574_1570630992_TextandAudioClassificationEnab

docx

School

Nairobi Institute of Technology - Westlands *

*We aren’t endorsed by this school

Course

301

Subject

Medicine

Date

Nov 24, 2024

Type

docx

Pages

Uploaded by UltraLorisPerson1117

Text and Audio Classification Enabled Diagnosis for Treatment Applications by Natural Language Processing (NLP) and Deep Learning (DL) Dissertation Proposal Submitted to National University School of Technology and Engineering in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF DATA SCIENCE by i

Contents Contents Contents ................................................................................................................................................... ii List of figures ........................................................................................................................................ iii List of tables .......................................................................................................................................... iv Introduction. .......................................................................................................................................... 1 Statement of the Problem ...................................................................................................................... 3 Purpose of the Study .............................................................................................................................. 3 Introduction to the Theoretical Framework ........................................................................................ 4 Research Methodology and Design ...................................................................................................... 6 Introduction ........................................................................................................................................... 6 Features selections ................................................................................................................................. 6 Proposed methodology .......................................................................................................................... 6 Evaluation matrix for the Deep learning classifier. ............................................................................ 7 Research Questions ............................................................................................................................... 8 Significance of the Study ....................................................................................................................... 9 Definition of Key Terms ...................................................................................................................... 10 Natural Language Processing (NLP) .................................................................................................. 10 Deep Learning (DL) ............................................................................................................................. 10 Summary .............................................................................................................................................. 10 References ............................................................................................................................................ 11 ii

iii

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

List of figures iv

List of tables v

Introduction. Technological improvements are bringing about a transformational age in the healthcare sector, and Natural Language Processing (NLP) and Deep Learning (DL) are at the forefront of these developments. NLP has the potential to completely change how doctors diagnose ailments, provide treatments, and communicate with patients. There have also been improvements in the healthcare sector documentation with the adoption of the Electronic Healthcare Record and digital imagery solutions, improvements that have provided the data necessary to improve medical outcomes and streamline workflows. This research study will focus on Healthcare NLP and DL. Medical text and audio classification may improve medical treatment and diagnosis applications, reducing morbidity and mortality. Kobritz et al. (2023) suggest that "Machine-learning algorithms show promise in improving predictions of complications." This study analyzes the application of deep learning and natural language processes using healthcare text and audio data to classify diseases. This chapter provides an overview of the clinical application of NLP and audio classification in treatment and diagnosis to explicate its importance in the medical sector. The problem statement will address the challenge of difficulty experienced in medical diagnosis and treatment due to the absence of trustworthy tools for analyzing textual and auditory data in healthcare and help to improve medical diagnosis and enhance effectiveness in handling patients' diseases. The study address critical provides a critical roadmap for the implementation of the entire project, which includes and not limited to background, problem, purpose, variables, population, sample, and conceptual framework for this research. This study also develops research hypotheses, questions, and significance concerning the research topic. The healthcare sector increasingly depends on cutting-edge technology to improve patient care, reorganize clinical processes, and boost diagnostic precision. NLP provides essential tools for this setting (Johri et al., 2021). NLP is a set of methods for processing unstructured text. Studies indicate that implementing NLP in healthcare may improve medical diagnosis (Healthcare, 2020) because there is a gradual increase in the potentiality of the 1

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

health systems for interpreting, analyzing, and searching large quantities of patient information. Alan (1999) found that "the impact of NLP on information retrieval tasks has largely been one of promise rather than substance" (p. 99) for many healthcare institutions. Decision-making capabilities have been greatly enhanced in the medical sector by considering the NLP set of methods. This study will fully make use of aspect mining and sentiment analysis to extract relevant features that will give desirable results during the prediction time by the algorithm. NLP algorithms are the only ML algorithms that might be used to reduce the mortality rates in health care. This is because the patient history data can be used to make informed decisions as the DP algorithm can be used to predict the illness. Deep learning techniques might be applied to clinical audio to extract diagnoses. Fagherazzi et al. (2021) Argue that to have control over the recorded vocal task and allow patients to choose their own words to preserve their naturalness, semi-spontaneous voice tasks are designed where the patient is instructed to talk about a particular topic (e.g., picture description or story narration task). To fully grasp the importance and immediacy of this study, it is crucial to situate it within the broader context of the field. Unlike the previous research which involved placing the research within the intersection of healthcare, technology, and data analytics. This research domain proposes a study, centered on text and audio classification for medical diagnosis, gaining its significant importance and current relevance. For example, applying NLP and DL to medical diagnosis has significant practical implications which include, and not limited to; patient data that can be fed into the DL model to predict disease progression and potential outcomes for patients. Therefore this information can be of great assistance to healthcare providers in developing treatment plans and improving care. NLP can also save medical personnel time and burdensome administrative work by automatically extracting relevant information from clinical notes, and creating documentation in a simplified format. The capacity to accelerate and improve diagnostic accuracy in the healthcare domain substantially impacts patient care and outcomes. In addition, the applied significance of the research topic is relevant in the sense that the study will focus on the technologies deployed in the previous research identify weaknesses in them, and propose appropriate technologies. This research proposes the use of quantitative analysis of NLP and DL in the healthcare industry and how they have influenced diagnostic procedures to 2

date and then leverages these tools on an existing dataset to generate classification models. This acquaints the audience with the current state of the art and emphasizes the basis of the proposed research. The progress and gaps identified in previous studies highlight the importance of addressing the research problem. Statement of the Problem The problem to be addressed in this study is the difficulty experienced in medical diagnosis and treatment due to the absence of trustworthy tools for analyzing textual and auditory data in healthcare settings (Lu et al., 2020). In modern healthcare, when data is abundant, not using it causes delays, blunders, and missed opportunities for early action. Current diagnosis and treatment methods rely on human interpretation, making medical record and audio recording management complex and unpredictable. Healthcare personnel, patients, organizations, and society are affected by this issue. The problem of exhaustion and a drop in care quality is evidenced by the difficulties experienced by healthcare providers in processing massive amounts of textual and auditory data (Stark et al., 2018). Consequently, patients face challenges obtaining prompt and accurate diagnoses, resulting in subpar treatment outcomes. The rising costs and potential legal dangers healthcare organizations face are significant factors in escalating healthcare prices and deteriorating social well-being. The issue impacts patients, healthcare workers, and society. Inefficient medical data analysis causes patient suffering, treatment delays, higher healthcare expenses, and legal issues. The total stakeholders influence the data hence they should mind what avenues they need to implement to ensure that it doesn't have impacts on the healthcare fraternity. Neglecting the issue risks patient suffering, higher costs, and missed early intervention. The study will highlight the importance of answering these questions to enhance healthcare by decreasing unintended consequences and improving medical data analysis. NLP and DL approaches in medical diagnosis and therapy will be examined using performance evaluation matrices such as F1-score, confusion matrix, accuracy, and precision. This research develops cutting-edge NLP and DL models to reduce disease diagnostic and treatment inefficiencies and improve healthcare for all stakeholders. 3

Purpose of the Study This research primarily aims at building deep learning classifiers and natural language processing models to support patient diagnoses and treatment based on text and audio data. This project intends to address the inefficiencies indicated in the problem statement by employing advanced technologies to streamline healthcare diagnostic and treatment processes. The problem outlined; namely the underuse of textual and audio data in healthcare that leads to delays in diagnosis and incorrect treatment, is addressed head-on in this study by developing models that leverage both text and audio to classify patient symptoms. The study will progress in stages, beginning with data curation and ending with usable NLP and DL models for classifying patient symptoms. Multi-input DL architecture will classify audio and text elements and return the likely nature of the patient's symptoms. The dependent variable is the nominal list of possible patient symptoms, while the independent variables are the audio and text inputs. The datasets that will be applied for analysis will be divided into two datasets, one for training and the other one for testing. This requires the data modeler to specify the ratio of dividing the data. The data is trained using one data as the input while the other testing data helps to give the output which is the performance of our classifier. The prediction will be based on the learning from the classifier and stored in the knowledge base. The possible effects on healthcare providers, patients, and institutions will also be investigated. Patients in healthcare settings and the medical staff who care for them are the primary audiences of the proposed proposal. The study will leverage open-source data, including 5,385 training observations (audio and text) with labels, 381 validation observations, and 385 test observations. The study will be performed at the researcher's home using publicly available, anonymous data. Introduction to the Theoretical Framework The study focuses on how advancements in natural language processing (NLP) and deep learning (DL) are spreading throughout the healthcare industry. Therefore, this is of particular relevance. . Decisions about the study's research are based on implementing a hybrid model of natural language processing and deep learning. The issue statement is better shaped by considering the sector's complexity and the demand for novel approaches. The statement of purpose is congruent with these frameworks since it emphasizes the potential spread of NLP and DL in the healthcare industry, propelled by the novelty of these technologies themselves (Bianchini et al., 2020). 4

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

These unified models also guide the research topics concerned with the spread of NLP and DL innovations inside the complex healthcare system. Combining these theoretical frameworks paints a fuller picture of the diffusion of NLP and DL advances in healthcare, and this research technique guarantees that research decisions are grounded in theory. The integrated approach adds a more profound knowledge of the advantages and downsides of implementing cutting-edge technologies into healthcare practices. 5

Research Methodology and Design Introduction The nature of this constructive research is to study the medical industry segment. It will be acquired from public websites such as kaggle.com and healthdata.gov. The study aims to extensively scrutinize complex matters associated with applying DL and NLP technologies in the health sector. Therefore, the data selected for analysis will exhibit appropriate features which will enable the classifier to make proper prediction to ensure proper prediction with enhanced accuracy for the model. Features selections After data transformation, the desired feature for prediction must be identified effectively for analysis. Chi-square will be the best approach which must be used in earnest to ensure that the classifier uses correct features for predictions. Proper selected features guarantee proper results hence the high performance of the classifier. Proposed methodology The research primarily focuses on the use of available datasets on public websites such as Kaggle to help make predictions about the disease the patient is suffering from. The data will undergo a process of data transformation such as normalization. The data will be cleaned by removing duplicate values, null values, and other inconsistencies such as wrong labeling of the datasets. The natural language processing of the text data and audio data will be achieved efficiently using sentiment analysis and aspect mining. The natural language process will make use of the two-name algorithm to help in predicting the symptoms of the data based on the data analyzed. A deep learning algorithm will also be applied in the disease prediction by going further taking audio and text data for further analysis. This will reinforce natural language analysis hence improving the rate of disease recognition. The success of the classifier will be based on a matrix such as accuracy, F1-score, precision, and confusion matrix. Once all these matrices have been performed the accuracy of our classifier will be compared with other existing classifiers based on their performance. 6

Figure 1: Deep learning architecture. Evaluation matrix for the Deep learning classifier. Accuracy It's the easiest way to measure and shows how many right guesses there were compared to all total tries. But, just having the right answers might not be enough if there aren't many examples in all categories. Precision, Recall, and F1 Score. 7

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Precision shows how good the guesses are, while recall (sensitivity) checks if the classifier can catch all positive cases. The F1 score is the average of precision and recall using a harmonic method. These measures are very helpful when handling unbalanced data sets. Confusion Matrix. A confusion table gives clear details about right and wrong classifications. It includes true positives, true negatives, false positives, and false negatives. Receiver Operating Characteristic (ROC) Curve and Area Under the Curve (AUC): ROC curves show the balance between the true positive rate and the false positive rate at different levels for classifying things. AUC measures the area under the ROC curve, giving one number to check classifier performance. Cross-Validation: Use methods like k-fold cross-checking to measure how well the model works in different parts of the data set. This makes sure that the model works well and isn't just because of a certain way they divided data. Research Questions Research Questions (RQs) are the guiding queries that frame the inquiry, and they should be crafted to correspond precisely with the problem statement and objective of the study. RQ1 What is the effectiveness of the NLP algorithm in classifying patient symptoms from the text data? RQ2 How effective is NLP in the classifying of patient symptoms from audio data? Hypotheses Based on the research objectives, the hypothesis for this research is as follows: H10 8

Text analysis of patient symptoms results in precision and recall insufficient for provider decision support. H1a Text analysis of patient symptoms results in precision and recall sufficient for provider decision support. H20 Audio analysis of patient symptoms results in precision and recall insufficient for provider decision support. H2a Audio analysis of patient symptoms results in precision and recall sufficient for provider decision support. Significance of the Study The study will demonstrate how NLP and DL might provide decision support to providers. Looking at the context of the problem statement, it has been identified that due to the presence of unstructured medical data, there is an increase in the complexity of physicians' work in making proper documentation of clinical notes (Deshmukh, 2023). As a result, there is a gap in ethical perspectives in meeting the patients' expectations in delivering accurate clinical decisions. This research will prove NLP/DL's promise for patient diagnosis. This research has far-reaching implications, both in healthcare and in the larger areas of NLP and DL, for which it provides a foundational foundation(Al-Garadi et al.,2022). This study holds value for both applied and academic fields. First and foremost, this research addresses a significant issue in healthcare by utilizing the power of NLP and DL to advance symptom classification and, eventually, medical diagnosis and treatment. Patient care might be significantly enhanced if the findings of this study were implemented. Diagnostic errors would be reduced, and treatment decisions would be made more quickly (Wu et al., 2020). In addition, the results of this research add to the growing body of work exploring NLP and DL's potential in the medical field. Exploring their use in healthcare settings adds to the existing body of information. This research contributes to the academic discussion on incorporating innovation 9

in healthcare since it follows the theoretical frameworks of the PhD's Theory of Diffusion of Innovations and the Field Theory of Health Services (Fahy et al., 2020). The study's findings will provide essential insights into the theoretical underpinnings underlying the dynamics of innovation dissemination in a complex healthcare environment. In conclusion, this research is noteworthy because of its possible sound effects on healthcare, increased knowledge of NLP and DL's practical applications, and development of theoretical frameworks. Definition of Key Terms Natural Language Processing (NLP) The study of how computers and humans communicate is known as Natural Language Processing and falls under the umbrella of AI (GOYAL, 2023). It involves the creation of algorithms and models to enable computers to understand, interpret, and generate human language. In the context of this research, NLP means using these methods to analyze and act upon textual medical records for the objectives of diagnosis and therapy. Deep Learning (DL) Deep Learning, is a machine learning and AI that aims to model and solve complex problems (Castiglioni et al., 2021).This is the classifier that will be deployed aimed at improving the accuracy of medical diagnosis and treatment by using deep neural networks to interpret and process audio data. 3. Artificial intelligence: This is a discipline which provides which specifically entails developing a computer model which performs a task which requires human intelligence. 4. H: This is a symbol that is used to denote hypothesis. This is a wise guess which has not yet been proven. 5. Confusion matrix: A tabular representation used especially in machine learning to evaluate the performance of our deep learning classification algorithm. Summary This study addresses the problem of difficulty experienced in medical diagnosis and treatment due to the absence of trustworthy tools. Its purpose of quantitative correlational research design aims to build symptom classifier models to support patient diagnoses and treatment using NLP and DL techniques based on both text and audio data., leveraging NLP/DL techniques to summarize patient symptoms and support provider decision making. The significance of this 10

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

study applies ML methods that support decision-making around the caregiving process. Investigating the ability of NLP/ML algorithms to provide precise and sensitive classification is an essential part of its diffusion into the medical sector. References Al-Garadi, M. A., Yang, Y., & Sarker, A. (2022). The role of natural language processing during the COVID-19 pandemic: Health applications, opportunities, and challenges. Healthcare, 10(11), 2270. https://doi.org/10.3390/healthcare10112270 Bianchini, S., Müller, M., & Pelletier, P. (2020). Deep learning in science. arXiv preprint arXiv:2009.01575. Bose, P., Srinivasan, S., Sleeman IV, W. C., Palta, J., Kapoor, R., & Ghosh, P. (2021). A survey on recently named entity recognition and relationship extraction techniques on clinical texts. Applied Sciences, 11(18), 8319. https://doi.org/10.3390/app11188319 Braşoveanu, A. M., & Andonie, R. (2020, September). Visualizing transformers for nlp: a brief survey. In 2020 24th International Conference Information Visualisation (IV) (pp. 270-279). IEEE. https://ieeexplore.ieee.org/abstract/document/9373074/ Castiglioni, I., Rundo, L., Codari, M., Di Leo, G., Salvatore, C., Interlenghi, M., ... & Sardanelli, F. (2021). AI applications to medical images: From machine learning to deep learning. Physica Medica, 83, 9-24. Categorizing patient concerns using natural language processing techniques. BMJ health & care informatics, 28(1). https://doi.org/10.1136%2Fbmjhci-2020-100274 Curtis, M. (2020). Toward understanding secondary teachers' decisions to adopt geospatial technologies: An examination of Everett Rogers' diffusion of innovation framework. Journal of Geography, 119(5), 147-158. Deshmukh, S. S. (2023, June). Progress in Machine Learning Techniques for Stock Market Movement Forecast. In Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022) (Vol. 105, p. 69). Springer Nature.https://doi.org/10.2991/978-94-6463-136-4_9 11

Dobbins, N. J., Mullen, T., Uzuner, Ö., & Yetisgen, M. (2022). The Leaf Clinical Trials Corpus is a new resource for query generation from clinical trial eligibility criteria. Scientific Data, 9(1), 490. https://doi.org/10.1038/s41597-022-01521-0 Fagherazzi, G., Fischer, A., Ismael, M., & Despotovic, V. (2021). Voice for Health: The Use of Vocal Biomarkers from Research to Clinical Practice. Digital Biomarkers, 5(1), 78–88. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8138221/ Fairie, P., Zhang, Z., D'Souza, A. G., Walsh, T., Quan, H., & Santana, M. J. (2021). Fahy, N., Greenhalgh, T., & Shaw, S. (2020). PHOENIX: A new framework for applying psychological theories to the adoption of innovations by healthcare professionals. GOYAL, A. A. (2023). The Role of Machine Learning in Natural Language Processing and Computer Vision. Hisamitsu, T., Oikawa, M., & Kido, K. (2016). Care cycle optimization using digital solutions. Hitachi Review, 65(9), 399. https://www.hitachi.com/rev/archive/2016/r2016_09/pdf/r2016_09_105.pdf Iroju, O. G., & Olaleke, J. O. (2015). A systematic review of natural language processing in healthcare. International Journal of Information Technology and Computer Science, 7(8), 44- 50. https://doi.org/10.5815/ijitcs.2015.08.07 Johri, P., Khatri, S. K., Al-Taani, A. T., Sabharwal, M., Suvanov, S., & Kumar, A. (2021). Natural language processing: History, evolution, application, and future work. In Proceedings of 3rd International Conference on Computing Informatics and Networks: ICCIN 2020 (pp. 365- 375). Springer Singapore.https://doi.org/10.1007/978-981-15-9712-1_31 Kang, Y., Cai, Z., Tan, C. W., Huang, Q., & Liu, H. (2020). Natural language processing (NLP) in management research: A literature review. Journal of Management Analytics, 7(2), 139-172. https://www.tandfonline.com/doi/abs/10.1080/23270012.2020.1756939 Kobritz, M., Patel, V., Rindskopf, D., Demyan, L., Jarrett, M., Coppa, G., & Antonacci, A. C. (2023). Practice-Based Learning and Improvement: Improving Morbidity and Mortality Review 12

Using Natural Language Processing. Journal of Surgical Research, 283, 351–356. https://doi.org/10.1016/j.jss.2022.10.075 Kozłowska, U., & Sikorski, T. (2021). The Implementation of the Soviet Healthcare Model in 'People's Democracy'Countries—the Case of Post-war Poland (1944–1953). Social History of Medicine, 34(4), 1185-1211. Kruse, C. S., Goswamy, R., Raval, Y., & Marawi, S. (2016). Challenges and opportunities of big data in health care: A systematic review. JMIR Medical Informatics, 4(4), e38. https://doi.org/10.2196/medinform.5359 Leichter, H. (1979). A Comparative Approach to Policy Analysis Health Care Policy in Four Nations. Cambridge University Press. Li, I., Pan, J., Goldwasser, J., Verma, N., Wong, W. P., Nuzumlalı, M. Y., Rosand, B., Li, Y., Zhang, M., Chang, D., Taylor, R. A., Krumholz, H. M., & Radev, D. (2022). Neural natural language processing for unstructured data in electronic health records: A review. Computer Science Review, 46, 100511. https://doi.org/10.1016/j.cosrev.2022.100511 u, L., Zhang, J., Xie, Y., Gao, F., Xu, S., Wu, X., & Ye, Z. (2020). Wearable health devices in health care: Narrative systematic review. JMIR mHealth and uHealth, 8(11), e18907. https://doi.org/10.2196/18907 Mishra, S. B., & Alok, S. (2022). Handbook of research methodology. https://www.researchgate.net/publication/319207471_HANDBOOK_OF_RESEARCH_METHO DOLOGY?enrichId=rgreq-6be5390a6f24699c882b5c3de1cc9f78- XXX&enrichSource=Y292ZXJQYWdlOzMxOTIwNzQ3MTtBUzo3MTQ4NTgxNDAwMTY2 NDJAMTU0NzQ0Njg3MDk1Mg%3D%3D&el=1_x_2&_esc=publicationCoverPdf Moorhead, L. (2021, June 17). Resize multiple images to be the same size. Miro. https://community.miro.com/ask-the-community-45/resize-multiple-images-to-be-the-same-size- 5101 Nawab, K., Ramsey, G., & Schreiber, R. (2020). Natural language processing to extract meaningful information from patient experience feedback. Applied Clinical Informatics, 11(02), 242-252. 10.1055/s-0040-1708049 13

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

O'Cathain, A., Connell, J., Long, J., & Coster, J. (2020). 'Clinically unnecessary of emergency and urgent care: A realist review of patients' decision making. Health Expectations, 23(1), 19-40. Ritter, E. (2021). Your Voice Gave You Away: The Privacy Risks of Voice-Inferred Information. Duke LJ, 71, 735. Rusk, N. (2016). Deep learning. Nature Methods, 13(1), 35–35. https://doi.org/10.1038/nmeth.3707 Shilo, S., Rossman, H., & Segal, E. (2020). Axes of a revolution: Challenges and promises of big data in healthcare. Nature Medicine, 26(1), 29-38. https://doi.org/10.1038/s41591-019-0727-5 Smeaton, A. F. (1999). Using NLP or NLP Resources for Information Retrieval Tasks. Text, Speech and Language Technology, 99–111. https://doi.org/10.1007/978-94-017-2388-6_4 Stark, Z., Lunke, S., Brett, G. R., Tan, N. B., Stapleton, R., Kumble, S., ... & Melbourne Genomics Health Alliance. (2018). Meeting the challenges of implementing rapid genomic testing in acute pediatric care. Genetics in Medicine, 20(12), 1554-1563. Strijker, D., Bosworth, G., & Bouter, G. (2020). Research methods in rural studies: Qualitative, quantitative, and mixed methods. Journal of Rural Studies, 78, 262-270.https://doi.org/10.1016/j.jrurstud.2020.06.007 Tang, R., Chuang, Y. N., & Hu, X. (2023). The science of detecting LLM-generated texts. arXiv preprint arXiv:2303.07205. https://arxiv.org/abs/2303.07205 Universal Rules for Fooling Deep Neural Networks based Text Classification. (n.d.). Ieeexplore.ieee.org. Retrieved October 24, 2023, from https://ieeexplore.ieee.org/abstract/document/8790213 Verleye, K. (2019). Designing, writing-up and reviewing case study research: an equifinality perspective. Journal of Service Management, 30(5), 549-576. Wu, S., Roberts, K., Datta, S., Du, J., Ji, Z., Si, Y., ... & Xu, H. (2020). Deep learning in clinical natural language processing: a systematic review. Journal of the American Medical Informatics Association, 27(3), 457-470. 14

6657574_1570630992_TextandAudioClassificationEnab

Related Documents