mimic
docx
keyboard_arrow_up
School
Nairobi Institute of Technology - Westlands *
*We aren’t endorsed by this school
Course
123
Subject
Medicine
Date
Nov 24, 2024
Type
docx
Pages
15
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
Thank you for sharing your first draft of Chapter 1 with the Doctoral Record High Committee.
You have many great ideas, and you have done quite a bit of detailed research here.
I am the Subject Matter Expert (SME), and I have attached my recommendations
and comments
on your paper below.
Please read through them. Your paper has sections that refer to a survey review of the timeline of
adaptation of ML processes. However, by reading some other sections of your RQs and your
final summary, I am convinced that you want to complete constructive research on secondary
text and audio data and compare the models' diagnostic effectiveness.
You need to clean up the paper so it has only one focus and direction, and then you need to
research what methodologies you plan to use.
Contents
No table of contents entries found.
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 process using
healthcare text and audio data to classify disease. 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 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 critical
roadmap for the implementation of the entire project ,which include 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 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
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
the NLP set of methods. This study will fully make use of aspect mining and sentiment analysis
to extract relevant features which will give desirable result during the prediction time by the
algorithm.
NLP algorithms are only ML algorithm that might be used to reduce mortality rate in the
health care. This is because the patient history data can be used to make informed decision as 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) Argues that in order to have control over the
recorded vocal task ,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
domains 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 implication which includes and not limited to; patient
data can be fed into 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, 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
study will focus on the technologies deployed on the previous research and identifies weakness
in them and propose appropriate technologies. This research proposes use of quantative analysis
of NLP and DL in the healthcare industry and how they have influenced diagnostic procedures to
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 stakeholder have influence on the data hence they should mind what avenues they need
to implement in order 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 matrix 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.
Purpose of the Study
This research primarily aims at building deep learning classifier 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 which will be applied for analysis will be divided into two
dataset, one for training and 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 help 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 implementing 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).
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.
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
Research Methodology and Design
Introduction
The nature of this constructive research is to study the medical industry segment. It will be
acquired from public website 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 in order to ensure proper prediction with
enhanced accuracy for the model.
Features selections
After data transformation, 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 for features for predictions. Proper selected features guarantee proper
results hence high performance of the classifier.
Proposed methodology
The research primarily focuses on the use of available dataset on the public website such as
Kaggle to help making prediction of the disease the patient is suffering from. The data will
undergo process of data transformation such as normalization. The data will be cleaned by
removing duplicates values, null values and other inconsistence such as wrong labelling of the
datasets. The natural language processing on the text data and audio data will be achieved
efficiently using sentiment analysis and aspect mining .The natural language process will makes
use of the two name algorithm to help in predicting the symptoms of the data based on the data
analyzed. 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 improve the rate of disease recognitions. The success of the classifier will be based on
matrix such accuracy, F1-score, precision and confusion matrix. Once all these matrix has been
performed the accuracy of our classifier will be compared with other existing classifiers based on
their performance.
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.
Precision shows how good the guesses are, while recall (sensitivity) checks if the classifier can
catch all positive cases. 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 true positive rate and 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 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:
H1
0
Text analysis of patient symptoms results in precision and recall insufficient for provider
decision support.
H1
a
Text analysis of patient symptoms results in precision and recall sufficient for provider
decision support.
H2
0
Audio analysis of patient symptoms results in precision and recall insufficient for
provider decision support.
H2
a
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
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 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
1.
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.
2.
Deep Learning (DL)
Deep Learning, is a machine learning and AI that aims to model and solve complex problems
(Castiglioni et al., 2021).This the classifier that will be deployed aimed at improve 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 provide which specifically entails developing
computer model which performs task which requires basically human intelligence.
4. H: This is a symbol which 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
perfomance 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
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
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).
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
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.2015.08.0
7
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 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_
METHODOLOGY?enrichId=rgreq-6be5390a6f24699c882b5c3de1cc9f78-
XXX&enrichSource=Y292ZXJQYWdlOzMxOTIwNzQ3MTtBUzo3MTQ4NTgxNDAw
MTY2NDJAMTU0NzQ0Njg3MDk1Mg%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
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.
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