How Machine Learning Improve Healthcare?

In the patient and people-centric world of healthcare, people’s understanding of how machine learning and business intelligence can improve patient care and save valuable time and resources is only just starting to be uncovered. The idea that machines can learn about their patients and help them is becoming more widely accepted across the medical field. Sometimes, it may seem strange to talk about “business intelligence” in a sector devoted to helping people get better and stay well. That is only until we realize that BI concepts like descriptive, diagnostic, predictive, and prescriptive analytics, all which sound like the medical terms, can actually be applied to healthcare in life-saving ways.

Applied Machine Learning in Healthcare

Machinelearning in medicine has been making headlines lately. Google has developed a machine learning algorithmto help identify cancerous tumors on mammograms. On top of that, Stanford is using a deep learning algorithm to identify skin cancer. A recent JAMA article reported the results of a deep machine-learning algorithm that was able to diagnose diabetic retinopathy in retinal images. It’s clear that machine learning puts another arrow in the quiver of clinical decision making.

Still, machine learning lends itself to some processes better than others. Algorithms can provide great benefits to disciplines with processes that are reproducible or standardized. Also, those with large image datasets, such as radiology, cardiology, and pathology, are strong candidates. Machine learning can be trained to look at images, identify abnormalities, and emphasize areas that need attention, thus improving the accuracy of all these processes. Machine learning can also offer an objective opinion to improve efficiency, reliability, and accuracy.

Healthcare Machine Learning Will Address Massive Growth in Healthcare Data

A growing number of healthcare managers have patients that are supported by digital devices at home. This number is expected to significantly grow over the next couple of years. As the number of patients using these devices grows, it will eventually result in massive healthcare data streams being delivered to the associated health system. It would be impossible and too costly to hire enough people to monitor these extremely large data streams daily. However, they need to be regularly monitored to detect problems early and maintain optimal population health. Eventually, this massive growth in data will result in the need for intelligent data systems to identify problems to support care managers and health systems as they strive to optimize the health of populations under their care.

Medical Diagnosis Using Machine Learning

Take the images generated by magnetic resonance imaging (MRI) scanners to detect problems such as brain tumors. Radiographers will scrutinize each MRI image to assess the state of the patient. MRI images can be entered into a machine learning system as two distinct sets. One set shows brain tumors whereas the other set shows no brain tumors. The ML program analyzes the images to detect the patterns that typically distinguish one image from the other.When new images are then entered without being labeled, the ML program applies what it has learned previously to decide if the new image represents a brain tumor or not. The more images the ML program treats, the more it learns and the better its diagnoses become, saving medical staff time while offering smart assessments

Machine learning has gained incredible interest in the last decade fueled by cheaper computing power and inexpensive memory, making it efficient to store, and analyze growing volumes of data. Enhanced algorithms are being designed and applied on large datasets to help discover hidden insights and correlations amongst data elements not obvious to human. Machine learning offers hope with early diagnosis, help patients in making informed decisions on treatment options and can help in improving overall quality of their lives.