How Machine Learning Will Change Healthcare

First of all, AI( read Deep learning) won’t replace doctors any time soon, but AI should be the tool for doctors to even be better at their jobs and to have a better success rate in treating patience and overall patient wellbeing.

Healthcare is a data goldmine but still restricted due to different regulations in different countries (for example, HIPPA ). McKinsey estimates that deep learning and machine learning in medicine could generate a value of up to $100B annually, based on better decisions, optimized innovation, improved efficiency of research trials, and new genius tool creation for doctors, patients, insurance companies, and policymakers.

What is the main problem in Healthcare, and how to make it better than the current status quo?

The Healthcare market size is USD439B; 78% Global population suffers from health or wellness issues. Market growth per year is 45%, and 76% of the world’s population travel for different treatments. Global Healthcare spends projected to reach USD8.7 trillion by 2020, which, due to corona, will be 20% more. With PyHEALTH (machine learning and deep learning implementation in Healthcare), we can reduce costs by 30% to 40% annually. We have an enormous amount of data collected daily, but a small percentage of up to 4% is used practically in the industry. The Healthcare industry is the same as it was back then with the Spanish flu in 1918. And we are now in 2020 with innovations flying to Mars and self-driving car, but we are dying from Influenza, the same back then in 1918. The only difference now is due to our better-equipment hospitals and better respirators; the virus pandemic will be shorter compare to that one in 1918. But still, we can see that in the last 100 years, we did not have innovation in Healthcare that can help us prevent or minimize different diseases. Worldwide pandemics are a severe threat. COVID-19 is just the beginning of the pandemic we will have in the future.

At Kageera, we research how machine learning and deep learning is impacting the healthcare industry as part of our PyHEALTH service.

How can machine learning solutions help us?

• Better understanding who is most at risk,
• Diagnose patients,
• Develop drugs faster,
• Finding old drugs that can help
• Predict the spread of the disease,
• Understand viruses better,
• Map where viruses come from
• Predict the next pandemic.

Machine learning is the best tool currently in the world to predict different types of risks. One example is a prediction of potential hazards in the oil and gas industry or even the nuclear energy industry.

We need to invest more in Healthcare, pharma, and biomedicine innovation with machine learning and deep learning tools on the go.

Early statistics show that essential risk factors that determine how likely an individual has some disease include:

• Age,
• Pre-existing conditions,
• General hygiene habits,
• Social habits,
• Mental state
• General stress scores
• General diet and wellness
• Number of human interactions,
• Frequency of interactions,
• Location and climate,
• Socio-economic status.

Essential data may vary depending on the potential disease. So every disease has particular data points to track.

To understand diseases and to get practical outcomes, it takes years. Even then, diagnostic is a time-consuming process. This puts pressure on doctors, as we all know that we don’t have a lot of doctors in any country worldwide.

Machine Learning and Deep Learning algorithms can help disease diagnostics cheaper and more accessible. Machine learning can learn from patterns as a doctor also do. The only difference is that machine learning algorithms don’t need to rest, and they have the same accuracy at any time of the day. The key difference here between machine learning and doctor is that experts can instantly see what the problem is and find a potential cure, but algorithms need a lot of data in order to learn. That is the key restriction because a lot of hospitals don’t share their data or even don’t collect them. The other issues are that data needs to be machine-readable.

Machine learning and deep learning can be used for detecting and minimizing different disease, such as:

• Lung cancer or breast cancer on CT scans
• Risk of sudden cardiac death based on electrocardiograms and cardiac MRI
• Risk of different dental disease based on CT scans

What is the most important value that machine learning is bringing to healthcare?

Copyright United Nations Goals

Every person can have access to the same healthcare quality of top experts and for a low price. Machine learning can ensure healthy lives and well-being for all. Which is one of the main goals for the United Nations?

Personalize patient treatments

Every person is different and has less or more risk of getting different diseases. Also, we react differently to different drugs and treatments. Personalize patient treatment have enormous potential with the use of machine learning and deep learning.

Machine Learning can automate this complicated statistical work — and help discover which characteristics indicate that a patient will have a particular response to a particular treatment. So the algorithm can predict a patient’s probable response to a particular treatment.

The system learns this by cross-referencing similar patients and comparing their treatments and outcomes. The resulting outcome predictions make it much easier for doctors to design the right treatment plan. So, machine learning is a tool that helps doctors do their job even better.

What can we do with machine learning now?

Warning notifications of the potential risk of new diseases- Warning notifications can help doctors predict potential disease and prepare in time for future diseases.

We need to work more in order to develop prediction models for direct disease transmission, but knowing which data we need and working together with experts from the field is the first step to successful machine learning implementation. Key is problem discovery in the healthcare industry and then getting the data in order to resolve the problem with the use of machine learning.

CONCLUSION

Machine Learning and deep learning are an important tool in fighting different diseases and COVID-19. We need to take this opportunity; especially, time is of the essence NOW. People’s lives are at stake. We, as a company, can use our knowledge to collect the data, pool our knowledge, make cross-functional teams with expert doctors, healthcare providers, companies working with healthcare providers in order to save many lives now and in the future.

Kageera mission and vision are to build machine learning solutions that help humans live longer and focus on things that matter the most: people, profit, planet.

If you need our urgent assistance in healthcare and COVID-19 projects send me a message at manja.bogicevic(at)kageera.com or send me a message on LINKEDIN

For more follow me on LINKEDIN.

Until next time,

Happy Machine learning

Manya PyWOMEN

P.S. I want to share 4 random stuff about me:

1. I am one of the first self-made women Machine learning Entrepreneurs in the world.
2. I am on the mission to become a self-made millionaire ForbesUnder30 (3 years to go).
3. I have strong economics and business background, and in combination with my machine learning skills, it delivers invaluable guidance in making strategic business decisions.
4. I am an ex-professional tennis player, and I have run four half-marathons.