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1. Deep Learning
for Predictive
Analytics in
Healthcare
An Academicpresentation by
Dr. NancyAgnes, Head, Technical Operations, Pubrica
Group: www
.pubrica.com
Email: sales@ pubrica.com

2. Today's discussion
In brief
Introduction
Deep Learning Predictive Analytics Survey in Health Care
Deep Learning Models
Future trends of Deep Learning in Healthcare Predictions
Conclusion

3. In brief
Despite the current abundance of data and
information, the healthcare industry needs
actionable knowledge.
record
Electronic
integration, computer-aided diagnosis,
illness predictions are areas where
management, data
and
the
healthcare business faces issues. Reduced
healthcare expenditures and a shift toward
individualised treatment areboth necessary.

4. Deep learning and predictive analytics, which are fast increasing domains, have
begun to play a vital role in creating enormous volumes of healthcare data
practises and research.
Deep learning provides various tools, methodologies, and frameworks to solve
these issues. Predictive analytics for health data is gathering steam as a game-
changing technology that can enable more preventive treatment choices. In a
nutshell, the framework for deep learning data emphasises this research.

5. Introduction
Given the enormous expense of delayed
diagnosis and treatment, healthcare is an
area where prediction may be more
essential than explanation.
Prior information systems (IS) research
has frequently emphasised the benefits of
predictiveanalytics in healthcare.

6. In the healthcare business, the digitalisation of healthcare results in the
generation of enormous newdata sets.
Computerised physician order entries, physicians' notes, and imaging
devices, to mention a few, are also potential sources of clinical data.
Compared to other industries, these datasets are exceptionally complicated
and fragmented, presenting significant diagnoses, treatment, and prevention
challenges, and their improvement represents immeasurable value.

8. Predictive analytics services helps healthcare life sciences and providers by utilising
various approaches such as data mining, statistics, modelling, machine learning, and
artificial intelligence to explore current results and generate future predictions.
It assists healthcare organisations in preparing for health care by lowering costs,
correctly detecting illnesses, improving patient care, maximising resources, and
improving clinical results.
Deep learning is a technique for automatically identifying patterns and extracting
features from complex unstructured data without human intervention, making it a
crucial tool in big data research. In diagnostic applications, deep learning plays an
important role.

9. Deep Learning
Predictive Analytics
Survey in Health Care
Healthcare predictive analytics service
provider aims to predict future health-
related outcomes or occurrences using
clinical and nonclinical patterns in data.
Deep learning applications in
pharmaceutical research have evolved in
recent years.

10. They have shown promise in addressing various difficulties in drug discovery by
assessing the patient's medical history and providing the appropriate therapy for the
patients based on their symptoms and tests.
In healthcare predictive analytics, study outcomes such as medical problems,
hospital readmissions, therapy responses, and patient death are frequently of
enormous practical value.
The current trend for deep learning in healthcare data analysis demonstrates its
importance.

11. The feature engineering process involves domain expertise and is time-consuming,
the primary distinction between classical machine learning and deep learning
methods.
Deep learning techniques use predictive analytics solutions automatic feature
engineering, whereas typical machine learning algorithms need us to create the
features.
In medical applications, the commonly used deep learning algorithms include
• Convolution neural netw
ork(CNN)
• Recurrent neural netw
ork(RNN)
• Deep belief netw
ork(DBN)
• Deep neural netw
ork(DNN)
• GenerativeAdversarial Netw
ork(GAN)
Deep Learning Models

12. Convolution neural network (CNN): CNN was the first approach for high-dimensional
image analysis to be suggested and used. It comprises convolutional filters that turn
2D into 3D.
Recurrent neural network (RNN): It's a neural net design that can learn sequences
and handle temporal dependencies and features recurrent connections between
hidden states.
The recurrent connections are utilised to detect correlations across time and
between inputs. As a result, it is particularly matched to health challenges that
frequently entail modelling changes in clinical data over time.

13. Deep belief network (DBN): This model has a unidirectional link between two levels
on the top of layers. Each sub-hidden network's layers serve as a visible layer for the
following.
Deepneural network(DNN): It contains several levels, allowing for acomplicated non-
linear interaction.
Generative Adversarial Network (GAN): In the training phase, the GAN architecture
consists of a generator and a discriminator. GAN is a popular tool for creating
realistic graphics.

14. Figure: Deep learning data analysis to clinical decision making

15. Since the beginning of digital imaging, deep
learning techniques have been used in medical
imaging.
Google DeepMind Health collaborates with the
UK's National Health Service to process more
patient medical data.
Future trends of Deep
Learning in Healthcare
Predictions

16. The acquisition of Merge's medical
management platform by IBM Watson
recently bolstered the company's billion-
dollar entry into theimaging field.
The lack of a dataset, specialised
medical professionals, nonstandard data
machine learning techniques, privacy,
and legal difficulties are all obstacles.

17. The main goal of this research is to develop a framework for utilising DL
with predictive analysis to monitor healthcare data. Here, a significant
region with much potential for medical imaging is getting much attention in
unsupervised learning.
Conclusion
In a summary of deep learning research related to healthcare
data predictive analysis in this paper, to employ deep learning in
healthcare.

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