These 6 Machine Learning Techniques are Improving Healthcare

The ever-increasing population of the world has put a tremendous pressure on the healthcare sector to provide quality treatment and healthcare services. Now, more than ever, people are demanding smart healthcare services, applications, and wearables that’ll help them to lead better lives and prolong their lifespan. This need for a ‘better’ healthcare service is increasingly creating the scope for artificial intelligence (AI) and machine learning (ML) applications to enter the healthcare and pharma world. With no dearth of data in the healthcare sector, the time is ripe to harness the potential of this data with AI and ML applications. Today, AI, ML, and deep learning are affecting every imaginable domain, and healthcare, too, doesn’t remain untouched.

Research firm Frost & Sullivan maintains that by 2021, AI will generate nearly $6.7 billion revenue in the global healthcare industry. According to McKinsey, big data and machine learning in the healthcare sector has the potential to generate up to $100 billion annually! With the continual innovations in data science and ML, healthcare sector now holds the potential to leverage revolutionary tools to provide better care.

Here are some of the top machine learning applications that are making it big in the healthcare industry:

  1. Pattern Imaging Analytics

Today, healthcare organizations around the world are particularly interested in enhancing imaging analytics and pathology with the help of machine learning tools and algorithms. Machine learning applications can aid radiologists to identify the subtle changes in scans, thereby helping them detect and diagnose the health issues at the early stages.

One such pathbreaking advancement is Google’s ML algorithm to identify cancerous tumours in mammograms. Also, very recently, at Indiana University-Purdue University Indianapolis, researchers have made a significant breakthrough by developing a machine learning algorithm to predict (with 90% accuracy) the relapse rate for myelogenous leukaemia (AML). Other than these breakthroughs, researchers at Stanford have also developed a deep learning algorithm to identify and diagnose skin cancer.

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  1. Personalized Treatment And Behavioral Modification

Between 2012-2017, the penetration rate of Electronic Health Records in healthcare rose from 40% to 67%. This naturally means more access to individual patient health data. By compiling this personal medical data of individual patients with ML applications and algorithms, health care providers (HCPs) can detect and assess health issues better. Based on supervised learning, medical professionals can predict the risks and threats to a patient’s health according to the symptoms and genetic information in his medical history.

This is precisely what IBM Watson Oncology is doing. Using patients’ medical information and medical history, it is helping physicians to design better treatment plans based on an optimized selection of treatment choices.

Healthcare startups and organizations have also started to apply ML applications to foster behavioural modifications. Somatix, a data-analytics B2B2C software platform, is a fine example. It’s ML application uses “recognition of hand-to-mouth gestures” to help individuals understand and assess their behaviour, thus allowing them to open up to make life-affirming decisions.

  1. Drug Discovery And Manufacturing

Machine learning applications have found their way into the field of drug discovery, especially in the preliminary stage, right from initial screening of a drug’s compounds to its estimated success rate based on biological factors. This is primarily based on next-generation sequencing.

The focus here is to develop precision medicine powered by unsupervised learning, which allows physicians to identify mechanisms for “multifactorial” diseases. The MIT Clinical Machine Learning Group is one of the leading players in the game. Its precision medicine research aims to develop such algorithms that can help to understand the disease processes better and accordingly chalk out effective treatment for health issues like Type 2 diabetes.

According to the UK Royal Society, machine learning can be of great help in optimizing the bio-manufacturing for pharmaceuticals. Pharmaceutical manufacturers can harness the data from the manufacturing processes to reduce the overall time required to develop drugs, thereby also reducing the cost of manufacturing.

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  1. Robotics In Surgery

Today robotics is spearheading in the field of surgery. Robotics powered by AI and ML algorithms enhance the precision of surgical tools by incorporating real-time surgery metrics, data from successful surgical experiences, and data from pre-op medical records within the surgical procedure. According to Accenture, robotics has reduced the length of stay in surgery by almost 21%.  

Mazor Robotics uses AI to enhance customization and keep invasiveness at a minimum in surgical procedures involving body parts with complex anatomies, such as the spine.

  1. Clinical Trial Research

Machine learning applications present a vast scope for improving clinical trial research. By applying smart predictive analytics to candidates of clinical trials, medical professionals could assess a more comprehensive range of data, which would, of course, reduce the costs and time needed for conducting medical experiments. McKinsey maintains that there is an array of ML applications that can further enhance the clinical trial efficiency, such as helping to find the optimum sample sizes for increased efficacy and reduce chance data errors by using EHRs.

ML tools can also facilitate remote monitoring by accessing real-time medical data of patients. By feeding the health statistics of patients in the Cloud, ML applications can allow HCPs to predict any potential threats that might compromise the health of the patients.

  1. Predicting Epidemic Outbreaks

Healthcare organizations are applying ML and AI algorithms to monitor and predict the possible epidemic outbreaks that can take over various parts of the world. By collecting data from satellites, real-time updates on social media, and other vital information from the web, these digital tools can predict epidemic outbreaks. This can be a boon particularly for the third-world countries that lack proper healthcare infrastructure.

For instance, Support vector machines and artificial neural networks have helped predict the outbreak of malaria by considering factors such as temperature, average monthly rainfall, etc.

ProMED-mail, a web-based program allows health organizations to monitor diseases and predict disease outbreaks in real-time. Using automated classification and visualization, HealthMap actively relies on ProMED to track and alert countries about the possible epidemic outbreaks.

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Today, we stand on the cusp of a medical revolution, all thanks to machine learning and artificial intelligence. However, using technology alone will not improve healthcare. There also needs to be curious and dedicated minds who can give meaning to such brilliant technological innovations as machine learning and AI. Understanding the importance of people in the healthcare sector, Kevin Pho states:

“Technology is great. But people and process improve care. The best predictions are merely suggestions until they’re put into action. In healthcare, that’s the hard part. Success requires talking to people and spending time learning context and workflows — no matter how badly vendors or investors would like to believe otherwise.”

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