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AlmaBetter Blogs > Machine Learning Case Study in Health Care Industry

Machine Learning Case Study in Health Care Industry

Data Science
In this blog, We have taken case studies where Machine learning is playing a vital role for people and also the business.
Machine Learning Case Study in Health Care Industry

Before beginning, I would like to thank my co-authors of this blog Rajat Pal, Shubham Sharma, Shubham Kumar for their contribution.

One day my mother asked me to accompany her to the hospital where she was receiving treatment for her knee issues. I attempted to ignore her, assuming that it would take a long time because we would have to wait in line to meet Dr. once we were at the hospital. I had no choice but to accompany her. I went to reception to register, and as I was going to ask for a registration slip after sharing the information, I received a text from the hospital confirming my registration with the token number.

While waiting for our turn, I noticed my mother looking through her bag for something, and upon inquiry, I found she was looking for a test report. We heard our token number called while looking for reports, and I thought the whole day was a waste because we had to come back tomorrow with reports, but we went to see the doctor because our turn had come. He began his normal query with the registration number, and while looking at the laptop, he stated that our reports appeared to be in order. He wrote a prescription and instructed us to pick it up from the pharmacist/reception. The utilization of computers and networking in the hospital made me both thrilled and amazed.

As data science enthusiasts, we were intrigued by the amount of data generated by the hospital and wondered how we could make use of it. When I got home, I started looking into how machine learning enhances the efficiency of the health sector, and I was amazed by the work done.

The massive amounts of data generated from Electronic medical records, billing, clinical systems, data from wearables, and a variety of research projects continue to generate massive amounts of data. This creates a significant opportunity for healthcare practitioners to improve patient care by leveraging actionable insights from past patient data. Of course, data science is the driving force behind it. Data scientists all over the world are steadily transforming the healthcare industry with superior machine learning and analytics. They’re trying to optimize every area of healthcare operations by tapping the potential of data, from increasing care delivery to achieving operational experience.

Here is the broad classification of health care where ML is found very useful and is emerging day by day.

  1. Use of ML for doctors and hospitals.

2.Drug discovery and clinical trials.

3.Diagnosis for predicting disease on basis of symptoms.

4.Diagnosis for improvement.

5.Recovery analysis

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In a country like India where it became difficult to get doctor appointments. In this case, how does Data science is used?

According to the Indian government, there is one doctor for every 1457 persons in the country. So doctors are unable to maintain a healthy work-life balance as a result of these factors, which make life difficult for them. We’ve also come across many articles in which doctors have committed suicide as a result of work stress. On the other side, we hear about people who are suffering because there aren’t enough doctors to care for them. Getting an appointment at AIIMS usually takes 20–25 days. Many people mainly from rural areas travel a long distance only to obtain a better prescription.

We can use the internet to increase the reach of doctors now that it has reached every corner of the country. Many platforms are now available via which you can contact any specialized doctor and obtain a prescription while sitting at home.

  • Practo

  • Doctor24X7

  • DocsApp

  • Curofy

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Using these platforms, also solved the problem of information loss. Patients’ prescriptions and diagnoses are documented, which is useful for future treatment. Now doctors can better comprehend their patients if they have a thorough record of their disease therapy.

Patients have the option of receiving therapy from a professional on these platforms. On the other side, it aids doctors in better managing their patients. This may also assist us in lowering the costs associated with treatment.

With the rise in the number of fitness bands and smartwatches, we can now collect a huge amount of data such as heart rate, spo2 (which measures the oxygen level in the human body), temperature, sleeping pattern, and so on. Combining these data with a person’s location, height, weight, diet, and habits can help us to prevent disease.

PAI(Personal Activity Intelligence) is functionality that comes standard in smartwatches. The PAI score is computed based on our heartbeat, and if we can maintain a high PAI number, it can help us reduce our risk of cardiovascular disease.

Rapid economic development is causing a lot of environmental changes and unhealthy lifestyles, increasing the number of people suffering from critical sickness conditions like Cardiovascular disease, Lung ailment, Hepatitis is a disease of the liver, Cancer, Kidney Illness.

We can predict these diseases at an early stage using machine learning, which saves a person’s life and money.

How does drug discovery happen and how does ML is helping in drug discovery and labs.

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We all know that the volume of laboratory testing is growing at the same rate as the demand for healthcare. Many businesses like BenevolentAI, Airamatrix are now getting involved in different aspects of the treatment process, from diagnosis through therapy and drug development with the help of ML.

Here, I will talk about Drug research and development using ML.

Have you ever wondered how a drug is coming to market? Can you even guess the time it could take generally for a drug to reach us? It takes roughly 10 years for the drug to come into the market after starting the process of discovery. It’s almost 1/4th of career time for an individual. It’s a long process.

Not only its a time taking process but also corporations invest billions of rupees In drug research to clinical trials. In the field of drug development, computational solutions can dramatically cut the cost of bringing new medications to market. By 2028, according to Bekryl, AI has the potential to save $70 billion in the drug research process. As biomedical data is so complicated, utilizing algorithms to develop novel medications is now more feasible than ever.

There are many steps of the drug discovery process that can be aided by machine learning: Here are two important steps

  1. ML is used in designing the chemical structure of medicine.

  2. Examining a drug’s effect — both in fundamental preclinical research and clinical trials, which generate a large amount of biological data. Machine learning can help in the discovery of new patterns in your data.

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Now let’s deep dive into how Machine learning plays a role in the drug discovery process.

Researchers want to find compounds that can interact with a promising therapeutic target to create desirable biological effects once the target has been discovered. For hit compound(molecule that exhibits the desired type of activity) discovery and the subsequent hit-to-lead selection process, it is critical to construct pharmacologically relevant screening assays. By refining the screening criteria, lead compounds are picked from a pool of hits, allowing for the selection of the most promising molecules for further development.

QSAR(quantitative structure-activity relationship) is labor, time, and cost-effective strategy for obtaining compounds with desired biological properties because no substance needs to be manufactured or evaluated before computational evaluation. As a result, QSAR is widely used in companies, universities, and research institutions all over the world.

QSAR modeling has become increasingly important in choosing compounds for synthesis and/or biological evaluation. The QSAR models can be used to identify hits as well as optimize hit-to-lead conversion. Several optimization cycles could be used to create a desirable balance between which are essential to developing a new, safe, and effective medicine.

Previously modeling of the QSAR was restricted to a small number of congeneric chemicals and simple regression approaches. Nowadays, QSAR modeling has expanded, varied, and evolved to include the modeling and virtual screening (VS) of very large data sets containing thousands of different chemical structures and the use of a wide range of machine learning approaches.

A good quality data collection is required to develop a superb QSAR model utilizing artificial intelligence.

These are some of the factors that could influence medication development:

· Structure variables: shear rate, particle sizes, molecular weight, Melting point, Quantum based features, etc.

· Particle variables: adhesive strength, resistance, valency, hybridization, etc.

· ADME properties: Absorption, distribution, metabolism, and excretion Other qualities, such as toxicity, environmental conditions can be utilized in specific instances.

You’ll need to examine the data and construct a model to design or optimize rational drugs after you’ve built the data collection.

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That is why QSAR predictive models are built using AI approaches. It has been shown to outperform existing methods in terms of predicting a molecule’s activity from its physicochemical characteristics while also lowering costs.

You can also execute a feature or input selection to extract the features that have the greatest impact on your model.

To be more specific, neural networks have been employed to develop predictive QSAR models in recent years.

Because ANNs do not require a prior understanding of how input and output are connected, they may swiftly adjust to non-linear and linear relationships.

It can also teach you how to use statistical methods to investigate the properties of molecules, reduce the dimensionality of the data set, create a prediction model, and test it against real-world data to determine its accuracy.

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Machine learning has been attempted by scientists and engineers from research institutions and pharmaceutical businesses such as Roche and Pfizer to extract useful information from clinical data acquired in clinical trials. The interpretation of this data in the context of medication safety is a hot topic in academia right now.

Clinical trials are also the most costly part of the medication development process. It is critical to apply the experience gathered during past clinical trials in the early stages of medication development to lower expenses. This is accomplished in two steps:

Machine learning could be used to examine and understand biological data from research experiments to forecast a drug’s effects and adverse effects.

The interpretation of biological data should be aided by data from clinical trials evaluated with machine learning.

It is conceivable to design superior preclinical tests to come up with the most effective medicines with the fewest adverse effects if those two approaches are developed concurrently.

How Machine Learning is helping in Diagnosing Diseases?

What will be your reaction if an alien invaded our planet and blinded 45% of people that can be saved in India alone and killed many more? panic? Plan of action? Well, something of that sort is happening right under our nose and we completely ignore it. In our case, it’s not aliens but diseases that are preventable causing havoc in the lives of underprivileged and needy people but we have a superhero disguised in form of Machine Learning/Deep learning that’s there for our rescue.

In India alone, there is a shortage of 127,000 eye doctors, and of all the patients that go blind 45% of them suffer from vision loss even before diagnosis.

So here comes our Avenger to the rescue named as ‘Inception Neural Network’ that was built in collaboration with Google and Indian Doctors Which produced an overall staggering 0.95 F-1 Score, it performed even better than highly-trained doctors when it came to diagnosing a disease. Here are some reviews that this artificial neural network received in its first attempt —

“The study by Gulshan and colleagues truly represent the brave world in medicine” -Dr. Andrew Beam( Harvard Medical school).

“Google just published this paper in JAMA and it actually lives up to the hype” — Dr. Luke Oakden-Rayner ( University of Adelaide).

Apart from diagnosis, another question that might come up is — “Can you show exactly where the disease is?” and our hero never disappoints and uses the power of HeatMap to show exactly where the issue is.

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We were successful in diagnosing a preventable disease that can cause blindness but is it limited to only that? Not at all — there are several others like Breast cancer, tumors, and other types of cancers that can be prevented if diagnosed on time.

So these were discussions of diseases that a Doctor can detect as well ( although with less accuracy) but the problem here is a shortage of those doctors. Machine Learning being our savior cannot just possess normal powers it has to be Supernatural and guess what? it does have those. How? Well, it might not be a shocker but it can even predict only through images what our most highly skilled Doctors cannot! and in certain cases, it was able to predict diseases before they happen!! and not to mention the accuracy and availability of them is tremendous.

What’s the best thing with technology you can do? it certainly is Saving a life and the best use of that technology in recent times should go to our healthcare industries to ensure we indeed have a brighter future.

How does data science is improving diagnosis to decrease failure rates?

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Diagnostic failure rates remain high despite having access to large amounts of health data. According to the National Academies of Sciences, Engineering, and Medicine, around 5% of adult patients in the United States are misdiagnosed each year. More than 12 million persons are included in this figure. Furthermore, postmortem investigation results suggest that diagnostic errors are responsible for about 10% of patient deaths.

Machine learning is being used in hospitals to speed up the diagnosis and analysis process. Doctors can now seek assistance with the diagnosis so that they can move on to the treatment phase as quickly as possible.

Using machine learning to analyze diagnosis reports or CT images, finding anomalies or even cancerous cells can be done with superhuman precision.

Machine learning is beneficial in oncology, pathology, dermatology, and other fields since it improves the precision and accuracy of the results. The amount of time it takes to complete analysis has also dropped, allowing lab personnel to work more efficiently.

Reviewing machine learning data allows clinicians to better assess the severity of a condition, which helps to speed up therapy. This also assists doctors in prioritizing patients and increasing the success rate of disease prevention.

Machine learning also aids in the review of the prescriptions issued to patients; it will compare the prescriptions to those provided to other persons with similar symptoms and will issue a warning if the doctor has recommended another medication or if a valuable medication has been ignored.

How hospitals are using data science to improve patient care and experience? Hospitals gather data from patients who have visited their facility and ask them to rate the medical staff, services, and overall experience. They can come up with more information after analyzing the data provided by the patients.

Obstacles :

  1. The datasets are insufficiently large.

  2. Inconsistent labels

  3. The characteristics are subjective and do not have universal acceptance.

  4. Laboratory medicine is undergoing a digitization and automation phase.

Conclusion:

Medicine is entering a new era thanks to data science and machine learning. It’s fascinating to consider where it could go. Soon, having incorporated machine learning knowledge that assesses patient status in real-time, what’s going on with similar patients in other healthcare systems, what suitable clinical trials are underway, and the efficacy and cost of new treatment alternatives will be extremely frequent.

Opportunities and therapies that were once just a concept are now becoming a reality. We live in the age of machine learning, in which algorithms can help us prevent and treat diseases by evaluating data and assisting clinicians in making better judgments.

Machine learning is providing a lot of new capabilities to doctors, nurses, and healthcare staff, allowing them to better care for their patients. “The secret of patient care is in caring for the patient,” declared Dr. Francis Peabody many years ago. While machines take on additional jobs, humans can focus on what they do best: caring for others and assisting them.

That's the end of this blog…Thanks for reading our blog. Hope it is found to be informative. If you have enjoyed do give a clap…Happy learning !!! Also do let us know your ideas on ML in health care……

Reference links :

  1. https://deepsense.ai/machine-learning-in-drug-discovery/

  2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6262347/

  3. https://www.neuraldesigner.com/solutions/drug-design

  4. https://www.sciencedirect.com/science/article/pii/S2001037019303988

Keerthana Reddy
Data Analyst at Decimal Technologies

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