Abstract
Machine learning makes the machines learn from provided data and with
the help of its algorithms it predicts and analyzes the data. This makes
the machines artificially intelligent. These techniques spread its
wings in all the areas of healthcare whether it is the diagnosis,
treatment etc. Here a brief overview of all the areas where machine
learning / artificial intelligence techniques can be applied.
Abbreviations:
RPA: Robotic Process Automation; ML: Machine Learning; NLP: Natural
Language Processing. Introduction Healthcare industry is a big dealing
with patients, medicines, research, diseases, biomedical scientists,
academia, government officials, laboratories, pharma etc. One big aim of
this industry is disease free country. To achieve this goal all parts
are working very hard day-night. Still many of the diseases are in
scene. And many are treating in a proper way to cure. Making clinical
trials more successful, machine learning techniques are used. These
methods are robust, time consuming and help in roadmap of every
experiment a success. Because of these methods our system becomes
intelligent and efficient enough to work. Here are some of the areas
where machine learning and artificial intelligence come into existence
and play a wonderful role to overcome the problems specified. Disease
Diagnosis and Treatment Many pharma companies using AI in research and
develop diagnostics and therapeutics for diseases including cancer.
Currently the burning topic of research is prevention from disease.
According to the available data of disease symptoms machine learning
model is developed which will help our medical practitioners to
understand the disease in a fast and patient oriented pace. If diagnosis
of a disease
Dr.Anubha Dubey
Keywords: Machine Learning; Data; Healthcare; Artificial Intelligence,
Abbreviations:
RPA: Robotic Process Automation; ML: Machine Learning; NLP: Natural
Language Processing. Introduction Healthcare industry is a big dealing
with patients, medicines, research, diseases, biomedical scientists,
academia, government officials, laboratories, pharma etc. One big aim of
this industry is disease free country. To achieve this goal all parts
are working very hard day-night. Still many of the diseases are in
scene. And many are treating in a proper way to cure. Making clinical
trials more successful, machine learning techniques are used. These
methods are robust, time consuming and help in roadmap of every
experiment a success. Because of these methods our system becomes
intelligent and efficient enough to work. Here are some of the areas
where machine learning and artificial intelligence come into existence
and play a wonderful role to overcome the problems specified. Disease
Diagnosis and Treatment Many pharma companies using AI in research and
develop diagnostics and therapeutics for diseases including cancer.
Currently the burning topic of research is prevention from disease.
According to the available data of disease symptoms machine learning
model is developed which will help our medical practitioners to
understand the disease in a fast and patient oriented pace. If diagnosis
of a disease
is correct in a given time, the doctors will treat that
patient comfortably. These ML/AI based approaches help doctors to see
the affected areas, degree of severity of disease etc. So, doctors can
focus more on treatment. These approaches improve the human errors that
may occur during diagnosis. This will help a step ahead for curing a
patient [1]. Precision Medicine Academia and pharma industry have been
focused on how to improve disease diagnostics and prognosis in spite of
drug response and adverse effects to improve the safety and efficacy of
drugs, toxicity of certain drugs are also increasing high. The concept
of personalized medicine is come into existence because every individual
has difference in genome. Genomic information from patients can
contribute to biomarker based guided personalized drug/or treatment.
Moreover pharmacogenomics come over pharmacokinetics which involves the
mechanism of the action of drugs on cells as gene-expression pattern is
different in different individuals. Not only genotyping, the study of
metabolite and their contribution to personalizing drug treatment are
very next step. AI/ML techniques help to study the gene expression
patterns and identify the disease development. Metagenomics can be
carried out by DNA qPCR/microarray. And these results help to design
individual treatment. Over the last few years, Genome wide association
studies have been developed,to identify the wealth of genomic variations
associated with diseases. These will provide clinical applications [2].
Genome Diversity Genomic revolution in medicine has opened the avenues
to understand the disease at micro level. Technology, personalized
medicine, policies, public-private partnerships are improving the way of
medicines day by day. Now this is needed to study genotype to
phenotype, and their interaction with environment. Because human and
disease variability are very important to understand the complexicity of
gene environment interactions [3]. It is a big challenge to understand
gene-gene, protein-protein, and gene /protein interaction with
environment interactions at certain point. These discoveries help to
understand disease-risk prediction. The role of AI/ML methods used in
data to understand the gene association patterns which will help to
understand the disease better and genetics of particular human being, it
will help to understand the medicine. If anything is found rare then it
is a matter of study. Drug Manufacturing The use of machine learning in
the early stages of drug discovery /designing has the potential for
initial screening of drug compounds to predict the success rate based on
biological factors monitored. This will lead to the existence of
technology like next generation sequencing. This will involve
unsupervised learning of machine learning. This will involve identifying
patterns in data without predictions. If there are known patterns in
the data then it is supervised learning in AI. One example is decision
tree that make decisions on the basis of predictions. These innovative
methods of machine learning will also be beneficial in vaccine designing
of crucial diseases [4]. Clinical Trial Research Machine learning has
the potential in helping clinical trials. For ex, genetic information to
target specific populations. ML can also use for remote monitoring and
real time data access for increased safety. For example, monitoring
biological signs for disease, drug response etc. These ML techniques
help to increase clinical trials efficiently and these techniques help
to reduce data errors in electronically saved medical records [5].
Radiology Radiology and radiotherapy are the one that can diagnose
diseases better. Radiologists are the cyborgs of today that will read
the algorithms for available thousands of data in a minute. In near
future, hospitals are ready to develop machine learning algorithm based
devices that can
detect differences in cancerous and healthy
tissues/cells. This will improve radiation treatments [6]. Big Data of
Electronic Health Records The big data of electronic health records lead
to the understand the machine learning methods. Because these methods
are very useful to analyze different types of data. Classification is a
machine learning method for document classification like patient’s
queries or optical character recognition. They help in collection and
digitization of health information. Here artificial neural network using
MATLAB will develop intelligent machines to help in diagnostics,
clinical decisions and moreover provide suggestions for personalized
treatments [1,7]. Prediction of Disease Epidemic At present there are
data available for epidemic outbreak around the world. These data can be
collected from satellites information on web, real time updates on
social media and other sources. Support vector machines and artificial
neural network are the ML/ AI techniques that can predict malaria
outbreaks on account of available data, i.e. temperature, average
monthly rainfall, total number of unified database including databases
for epidemiology, weather and geographical data. Using machine learning
algorithms provide the system to predict and geolocate the disease
outbreaks. This system can monitor multiple outbreaks in a time. This
will also focus on the prediction of antibiotic resistance. atabases
which will communicate through 5G networking. This will enable
clinicians /physicians to contribute data from the field, from emergency
sites routine in-home visits and receive real time advice from doctors.
This will enable the accuracy faster. These platforms give data
security to many administrations whether public or government. This
security is achieved by Cloud platforms like AWS, Google, and Azure etc.
These devices will make medical treatment online in case of emergency
[10]. RPA Technology As the name suggests robotic process automation
(RPA) allows any of organizations to automate all the tasks as like
human beings is doing. Robotic automations interact
Bioinformatics & Proteomics Open Access Journal3
Dubey
A. Showcasing the Impact of Machine Learning in Healthcare. Bioinform
Proteom Opn Acc J 2020, 4(1): 000131. Copyright© Dubey A.
with the
existing IT architecture of the organizations. This software program
also runs on an end user’s PC, laptop or mobile device. The sequences of
commands are executed by Bots under some defined set of organization
rules. This will remove the repetitive and clerical tasks. It requires
direct access to the code [11]. Discussions Artificial intelligent
system: For developing these cutting
edge technologies artificial
intelligence system is developed which will make algorithms to learn
from data and make machine efficient to use the data for desired output.
These ML algorithms may be neural net, classification, deep learning
etc. The data can be obtained from any of the sources (laboratory),
collectsed in large server for reuse in future and then experimenter
(our system) where machine learning methods are performed. And we will
get the desired output as information which will be shared or reuse for
anytime when needed (Figure 1).
Figure1: Roadmap of artificial
intelligent system by machine learning. Examples of AI Based Methods for
Healthcare Nowadays the big IT giants are come closer to pharma
industries to work with public –partner relationship to develop AI based
models which will improve and well affect the medical field. As per
need many types of software are developed and programming languages are
modified day by day to get the desired results. Programming languages
like Python and Rare popular for application of ML. Julia is another
programming language that best offers support for modern ML framework
like tensor flow and MXN et. Julia works better in the diagnosing of
diabetic retinopathy. Deep learning in Julia helps to diagnose the
diabetic retinopathy. The role of radiologist is really remarkable in
diagnosing diseases. To make them fully utilizable machine learning (ML)
techniques, content flow image search engine in collaboration with
Julia solve the diagnosing problem in seconds. Content flow is a 3D
image –based search engine that again uses deep learning to put the
recorded knowledge of medical images. Due to shortage of radiologists
this could be the best solution. Companies like Google joins hands with
these ML techniques to develop more tools for medical industry. One of
them is Deep mind which predicts the kidney diseases before it occurs.
Hence it is said that this AI based deep learning continuously predict
the risk of future patient deterioration. The working of these AI
methods is possible due to large datasets of electronic medical records
covering various clinical records like adult patients patients,
inpatients, and outpatients
Bioinformatics & Proteomics Open Access Journal4
Dubey
A. Showcasing the Impact of Machine Learning in Healthcare. Bioinform
Proteom Opn Acc J 2020, 4(1): 000131. Copyright© Dubey A.
settings.
Patient’s treatment policies, their different medical tests come etc.
Augmented reality microscope is one such microscope that evaluates
tissue samples for diagnosis of cancer. As there is a need of trained
pathologists this ARM is ready to use for detecting metastatic breast
cancer and identifying prostate cancer with latency compatible with real
time use. These AI designed microscopes improve the accuracy and
efficiency of a cancer diagnosis. Another breakthrough AI application in
healthcare is protein net neural network which has the capacity to
predict the structure of a protein in milliseconds. Convolution neural
network show promising results in this regard. This AI based model
predicts local and global structure of the protein through geometric
units that optimize global geometry. Co evolution and experimental
pattern is studied. It will improve the path from drug discovery to
protein construction. Similarly these AI based techniques help in drug
discovery and delivery processes and soon vaccine development of
infectious diseases like HIV, malaria, tuberculosis etc are possible.
The researchers working on computer model designed to pick out potential
antibiotics that kill bacteria using different mechanisms than those of
existing drugs and it can screen available drugs. It is believed that
it will also identify other promising antibiotic candidates which they
plan to test further and able to design new drugs. Blue dot a Canada
based firm was the first to predict the outbreak of corona virus on
December 31.2019 using an artificial intelligence proved system.
Epidemiologist and physicians manually classify the data and developed
taxonomy for corona virus. Later they applied natural language
processing NLP and machine learning techniques to train the system.
Using classification as priority the system algorithm predicts the cases
in Wuhan. As Air travelling is significant in disease dispersal. Blue
dot uses geo informatics system data and flight tickets sales to create
dispersion graph for each disease based on the airport counted to city
and where passengers are likely to fly. The locations receiving highest
volume of travelers are identified and evaluated
This is a technology
driven era. And it is very important to have right data for achieving
particular goal. Right framework for regulation with right approach of
data analysis will predict cholera and other diseases outbreak well in
time. This will enhance the proper care. These at present technologies
machine learning, artificial intelligence will also improve the way of
drug or vaccine development. Moreover it will provide disease diagnosis
and treatment well in time. These cutting edge technologies save time,
money and serve patients more efficiently. Still these techniques are in
infancy and developing as per the need. Which needs collaboration
between artificial intelligence powered system and so on,
Dr.Anubha Dubey
Education Director
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