Improving diagnostic accuracy and efficiency-
Despite such huge amounts of health data at hand, the diagnostic failure rates are still relatively high. According to the recent research by the National Academies of Sciences, Engineering, and Medicine, about 5 percent of adult patients are misdiagnosed each year in the US. This totals over 12 million people. Moreover, the postmortem examination results research shows that diagnostic errors cause approximately 10 percent of patient deaths.
Targeting this problem, a deep learning startup, Enlitic, employs data science to increase the accuracy and efficiency of diagnostics. With $15 million funding, the startup has built a deep learning algorithm that can read imaging data (such as x-rays, CT scans, etc.), and analyze it, checking the given results against extensive database of clinical reports and laboratory studies. Therefore, the company claims to deliver up to 70 percent more accurate results, 50,000 times faster.
Take for example the Dutch startup, called Bruxlab, which applies similar data science and machine learning algorithms for diagnostic purposes. Coupled with sound recognition technologies, they help diagnose and measure Bruxism symptoms. Using a vast number of audio samples, both true and false, the data scientists taught a neural network to recognize and measure teeth grinding symptoms.
With a prevalence rate of up to 31 percent, Bruxism is quite a widespread disease, yet it is mostly overlooked due to its symptoms’ concealed nature. Thus, a mobile app, powered by data science technologies, presents a significant opportunity for better diagnosis and more efficient disease monitoring.
Advancing pharmaceutical research to find cure for cancer and Ebola:
Being one of the most common and most deadly diseases, cancer has been a regular subject of scientific research. The number of cancer patients keeps growing. Researchers projected that 1,735,350 new cancer cases will be diagnosed in the US in 2018. And 609,640 of them will be lethal.
A Boston healthcare startup, BERG Health, reshapes the cancer medication market through extensive use of data science. Using powerful machine learning algorithms the company extracted and analyzed biological samples from over 1,000 patients. With over 14 trillion data points contained in each sample, that was a plenty of information to feed into the AI algorithm.
As a result, the company developed BPM 31510, the drug, which detects and triggers the natural death of cells damaged by the disease. Thus, the cancer cells can be removed from the human body naturally, without extensive medication and further damage to the patient’s health.
While the drug is still being carefully tested, it gives us a clear understanding of the transformation potential that data science and machine learning technologies can provide to the pharmaceutical industry. Finding a way to push these research areas forward can lead to discoveries in AIDS, Ebola or Zika virus treatment.
As for the latter, Atomwise, an artificial intelligence technology startup, has recently shown some advances in search for the Ebola cure. The company used virtual models and neural networks to evaluate how 7,000 existing drugs interact with the virus. Being processed by an AI-based program, the experiment took only about a day instead of months to complete and resulted in potentially promising discoveries: Two of the tested drugs has proven to make human cells resistant to the virus.
While the research is still to be continued, these early results show a huge potential of such approach to pharmaceutical research. Alexander Levy, COO of Atomwise states: “If we can fight back against deadly viruses months or years faster, that represents tens of thousands of lives. Imagine how many people might survive the next pandemic because a technology likeAtomwise exists.”
Predictive Analytics in Healthcare:
We have already recognized predictive analytics as one of the biggest business intelligence trend two years in a row, but the potential applications reach far beyond business and much further in the future. Optum Labs, an US research collaborative, has collected EHRs of over 30 million patients to create a database for predictive analytics tools that will improve the delivery of care.
The goal is to help doctors make big data-informed decisions within seconds and improve patients’ treatment. This is particularly useful in case of patients with complex medical histories, suffering from multiple conditions. New tools would also be able to predict, for example, who is at risk of diabetes, and thereby be advised to make use of additional screenings or weight management.