Deep Learning for Predictive Analytics in Healthcare – Pubrica
                     Deep 
Learning  for 
Predictive  
Analytics in  
Healthcare
An Academic presentation by
Dr. Nancy Agnes, Head, Technical Operations, 
Pubrica  Group: www.pubrica.com
Email: [email protected]
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
In 
bDresipeitef the current abundance of data and  
information, the healthcare industry needs  
actionable knowledge.
Electronic recor management,
integration, cdomputer-aided data
dililangensossisp,redictions are areas an
whehaeltrhecare business faces issues. Reducedd    
healthcare expenditures and a shift towardth  
individualised treatment are boteh 
necessary.
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.
Introductio
nGiven 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  
predictive analytics in healthcare.
In the healthcare business, the digitalisation of healthcare results in the  
generation of enormous new data 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.

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.
Deep Learning  
Predictive 
Analytics  Survey 
inHe aHlthcearea plretdihctiv eC aanarlyteics 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.
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.
Deep Learning 
MTohed feaetulres 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 network (CNN)
• Recurrent neural network (RNN)
• Deep belief network (DBN)
• Deep neural network (DNN)
• Generative Adversarial Network (GAN)
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.
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.
Deep neural network (DNN): It contains several levels, allowing for a complicated  
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.
Figure: Deep learning data analysis to clinical decision 
making
Future trends of 
Deep  Learning in 
Healthcare  
PSrinece dthei cbetgininoingn ofs 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.
The acquisition of Merge's medical  
management platform by IBM Watson  
recently bolstered the company's 
billion-  dollar entry into the 
imaging field.
The lack of a dataset, specialised  
medical professionals, 
nonstandard data  machine learning 
techniques, privacy,  and legal 
difficulties are all obstacles.
Conclusion
In a summary of deep learningresearch related
to healthcare data  predictive analysis in this paper, to employ 
deep learning in healthcare.
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.
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