By Lucas Bonatto

Hospitals must get more hands on deck after the worldwide health crisis overburdened already exhausted medical ecosystems. In July 2021, while 91% of nurses were actively looking to leave their position, 42% of healthcare and social assistance workers who quit did so without having a new job.

On top of the great resignation, two in five healthcare specialists will be 65 years old in the next decade, bringing about a predicted shortage of up to 139,000 physicians by 2033—machine learning (ML) tools provide hope.

Rising lifespan, retiring medical specialists, and goals to increase healthcare access are the top drivers for introducing ML techniques. While artificial intelligence (AI) has significantly impacted automating processes and enhancing data quality, ML is an advanced extension. Today, scientists are training machines to learn from experience and new data without being explicitly programmed.

With the vast amount of data in healthcare, including electronic health records (EHRs), datasets of medical images, bioelectrical signals, and sequences and structures of genes and proteins, ML supports speed and accuracy in analysis. As the tools collect new information and cross-examine past datasets, they help to predict known diseases and identify patterns that have yet to be discovered.

Let's take a closer look at how ML's role in identifying and predicting diseases can support healthcare systems.

Reduces accidental hospital injuries
Medical errors are avoidable, yet they considerably harm patients annually. According to Center for Disease Control (CDC), in 2020, the fourth leading cause of death after heart disease, malignant neoplasms (or cancerous tumors), and COVID was accidental hospital injuries. But where are Operation Warp Speed and investments for minimizing medical errors?

The Agency for Healthcare Research and Quality reports eight root causes of medical errors:

1. Communication problems: verbal or written between physicians, nurses, and patients.
2. Inadequate information flow: for example, between facilities and hospitals.
3. Human problems: such as knowledge-based errors or poor documentation.
4. Patient-related issues: including inappropriate patient identification.
5. Organizational transfer of knowledge: or insufficiencies in training to those providing care.
6. Staffing patterns and workflow: issues relating to communication and information flow.
7. Technical or equipment failures: such as life support machines or medical devices.
8. Inadequate policies: also linked to poor documentation procedures and hygiene protocols.

It is easy to expect individual physicals to be aware of every patient's relevant information and act at the right time in every situation. But, each patient can have a variety of carers, support staff, nurses, medications, records, allergies, inherited genes, and basic needs.

As a result, doctors spend up to 22 minutes reviewing EHRs—whether their field is gerontology, endocrinology, primary care, or internal medicine—and give a further 11% of their time on EHRs "after-hours." It's their duty to organize patient information in their head and filter what specifics are critical and irrelevant per trauma or case.

The solution, therefore, must be to improve the organizational level—the data. Hospitals and healthcare workers should be the priority in receiving ML-powered centralized systems that can integrate patients' data. The success of deep-learning models is ultimately beholden to large, diverse, balanced, and well-labeled datasets. Yet, clinical data often reflects the patient population of one or few institutions.

Stepping in to tackle the data imbalance bottleneck are generative adversarial networks (GANs). These create large amounts of synthetic yet realistic data, alleviating the problems of privacy-restricted and unbalanced datasets. The GAN model consists of two networks, one which is trained to generate, and the other discriminate, between real and synthetic data, boosting the performance of ML models by 10%.

Rich datasets, particularly for rarer conditions, are difficult to obtain. There is often an overrepresentation of common diseases and healthy populations, limiting medical teams' ability to identify and predict severe illnesses. This is where ML and GAN models shine a light at the end of the tunnel. They can help assess patients' conditions and quickly provide reliable reports in a readable format.

Supports radiologists in severe disease detection
When it comes to traumatic brain injuries (TBI), an extra hour back can save a life. Since ML algorithms can read the same imaging data a highly-skilled radiologist analyzes, they can detect abnormal skin patches, lesions, tumors, and brain bleeding, within seconds.

TBIs are a leading cause of death for US citizens under the age of 45, and every year, nearly three million people seek TBI care across the nation. Today, machines can automatically analyze brain scans and relevant clinical data from TBI patients to quickly and accurately forecast recovery expectations six months after the injury.

And what's more, these highly-detailed image scans ensure precision and accuracy in diagnostics while making the process faster and maximizing resources. As well-trained radiologists are becoming increasingly scarce worldwide, the machine's ability to scan multitudes of data 24/7 means radiologists expect their use of these platforms to skyrocket.

ML is not only helping conduct tests such as X-rays and electrocardiograms (ECGs) but also interpreting these scans and creating reports quickly so that specialists can make timely decisions and provide the patient's care.

Provides more affordable healthcare
ML is playing a pivotal role in reducing the costs of diagnostics. In developing countries such as India, a significant barrier to seeking medical support is the ability to pay for tests such as blood reports and scans. However, with ML technologies, the difference in rupees is cut back from the thousands to the hundreds.

Early diagnosis prevents more severe cases further down the line. When more people can conduct regular check-ups, monitor their health, and take corrective measures, they can make informed decisions about legal, financial, and care matters, and better manage or reduce the spread of chronic ailments—that can be dangerous and costly to cure.

From minimizing accidental errors to predicting outcomes of patients with severe TBI, ML in healthcare is life-changing. ML-based tools are already used to discover various treatment alternatives and improve the overall efficiency of healthcare systems. They will be crucial in developing clinical decision support, illness detection, and providing the best potential outcomes for patients in the years to come.

About the author: Lucas Bonatto is a technical founder who studied Computer Science and is currently leading Elemeno AI, a startup helping data science teams to increase their output in the industry. Lucas has experience working in a wide range of industries, including finance, retail and crypto. He is passionate about the advancements that AI could bring to our lives, and believes that human beings are happier doing creative tasks.