How can machine learning algorithm improve healthcare?

  • industry Highlights
  • 6 months ago

While doctors, nurses and medical officers will probably never be replaced by robots and computers, modern technology, machine learning and AI are already transforming the healthcare industry, improving outcomes, and changing the way doctors think about providing care. A recent study by industry analysts IDC predicts that 30 percent of providers will use cognitive analytics with patient data by 2018. At the same time global spending on cognitive systems will grow from nearly $8 billion in 2016 to more than $47 billion in 2020. We’re talking real big in numbers here!

But what exactly is machine learning process?

It is the process that involves computer algorithms that are able to learn highly complex and intricate relationships in multi-dimensional ocean of data. Benefits of machine learning process can be widely favorable as it provides:

  • Massive data input from unlimited sources – it can consume virtually unlimited amounts of detailed data to constantly review and adjust accordingly
  • Rapid processing, analysis, and predictions – thus support the ability to act, optimize and modify in real time
  • Action systems – create models and experiences that are much more dynamic and tailored
  • Learning from past behaviors – and as a result offering much more personalized solution


Machine learning and predictive analysis in healthcare

Advancements in medical technology have dramatically increased the quantity of data available in the healthcare industry - patient reports, genomic data, electronic medical records, etc. that give a wide range of insights to analyze patient cases for prevention and cure. Predictive analytics leverage these large, heterogeneous data sets to further knowledge and foster discovery – it can identify similarities among patients based on a wider and deeper range of variables or characteristics (age, genetic marks, previous illness, etc.) leading to finer groupings and thus support predictive analysis. Such predictive modeling can facilitate appropriate and timely care by forecasting an individual’s health risk, clinical course, or outcome. Uncovering these types of patterns at an early stage can be of great benefit for both doctors and patients and might explain use of drug combinations and treatments independent of any promotion by mapping the predicted targets on the disease signaling network. Compared to statistical methods, machine learning can increase prediction accuracy, sometimes doubling it, with less strict assumptions, e.g., on data distribution.

Digging deeper, machine learning methods have been employed for breast cancer screening to discriminate malignant and benign micro calcifications for predicting breast cancer survival and to model prognosis of breast cancer relapse. Given the large number of patients being treated for breast cancer, and the fact that most of the adverse effects of chemotherapy are cumulative, identifying patients who are not responding to a particular treatment allows for switching to a potentially more effective regimen and avoiding unnecessary side effects.

Recent studies show that predictive modeling using readily available clinical and quantitative MRI data show promise in distinguishing breast cancer responders from non-responders after the first cycle of NAC (neoadjuvant chemotherapy). In the research twenty-eight patients with stage II/III breast cancer were enrolled in the study and completed at least two of the three scans to provide usable data for the analysis. Eleven clinical variables available before initiation of the first cycle of NAC were used for generating the predictive models.

Figure 1. List of pretreatment clinical variables with a short description

As a result of studies, three datasets were made and used for creating of prediction modeling. Final conclusion of study showed that predictive modeling approaches based on machine learning using available clinical and quantitative MRI data scan can be a reliable source for distinguishing breast cancer responders from non-responders after the first cycle of NAC.

Similar ML algorithms were used in research to predict two-Year leukemia free survival in cord blood transplantation – Random Survival Forest (RSF). RSF is known to be adaptive to data, as it automatically recover nonlinear effects and complex interactions among variables, and yields nonparametric prediction over test data. Variables in the study were also ranked according to their predictive contribution as disease status, age, and TNC (total nucleated cell dose) count were found to be the most important factors.

Figure 2. A general schema template for predictive model building and evaluation.

In general, most studies until now showed predictive accuracy of machine-learning processes of about 75% - not high enough for widespread clinical use. This month, however, a new approach to imaging analytics driven by machine learning algorithms reported rates that exceed other automated methods and even rival human pathologists - the machine learning tool was able to achieve 89 percent accuracy, compared to just 73 percent accuracy from a human pathologist who spent 30 hours poring over 130 slides.

Researchers still have a lot of work to do in order to develop reliable, accurate, and highly sensitive algorithms that can become meaningfully integrated into the patient care process, but test cases and pilots are bringing vendors ever closer to routine clinical care. As these technologies develop, new and improved treatments and diagnoses will save more lives and cure more diseases. The future of medicine is based on data and analytics.

See how our Sqilline team is transforming healthcare towards more personalized medicine here.








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