Implementing big data has revolutionized clinical trials, making it efficient, accurate, and cheaper.

Technological developments have boosted the healthcare community to improve their research. Over the past few years, clinical research has witnessed a significant growth. Researchers and healthcare specialists are implementing big data tools and technologies to accelerate the research procedure and get a cost-effective measure for accurate results. The need for faster results is mainly due to the increasing demand for a deeper understanding of various diseases and viruses and to find out the perfect treatment for these ailments.

Implementing big data in clinical trials has been a transformative move.  The fields of Healthcare and medicine are evolving and are becoming better and cheaper.

The routinely collected data (RCD) or any data, that is gathered during any medical test or population health chart is collected under big data. Some of our actions like using a Fitbit, purchasing medicines from a counter, or booking an appointment with the doctor virtually leave electronic footprints. These footprints are collected to improve healthcare and medicinal facilities.

Clinical Trials and Instant Analytics

Big data analytics uses the latest technologies to study real-time data and draw insights from them. Researchers use these insights to facilitate improved and accurate results of the trials. With the help of predictive analytics and data analysis tools, healthcare specialists can detect early signs of diseases and aid in monitoring the collected data. These tools analyze the data thoroughly and continuously, not just after the trials are completed.

Healthcare practitioners can track and detect the drug exposure levels, the immunity provided by the medicine, the tolerability and safety of the treatment, and other factors that are crucial for patients’  safety. Big data-powered strategies boost the speed of clinical trials and improve the accuracy of the results.

Other benefits of big data in clinical trials are:

• Improved patient analytics
• Boosts the sales and marketing
• Reduces drug pricing and improves promotion analytics
• Enhances efficiency in trials

The Reformation in Clinical Trials

Real-time data analysis boosts the outcome of clinical trials. Different problems, like drug accountability, protocol compliance, consent, and other complexities, can be easily resolved with the help of data analysis. The RCD can be used in the daily care of patients to analyze the outcomes and avoid unmanageable, costly treatments and follow-ups.

Even though the procedure has just started, the limitations of using RCD, the viability of the treatments, the details and format level, patient privacy issues, and several other complexities are already getting resolved. Even though there are several hindrances that are  present, but in the future, clinical trials will become more efficient with innovative developments.

Real-time data analysis reduces the chances of error and also aids in eradicating human errors. With the help of data analysis and big data tools, healthcare IT services will be able to predict the problems, hence the solution will be provided swifter than ever imagined. Data analytics has also provided several other benefits to the healthcare industry that has got in some remarkable changes

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Since its invention by a Hungarian architect in 1974, the Rubik’s Cube has furrowed the brows of many who have tried to solve it, but the 3-D logic puzzle is no match for an artificial intelligence system created by researchers at the University of California, Irvine.

DeepCubeA, a deep reinforcement learning algorithm programmed by UCI computer scientists and mathematicians, can find the solution in a fraction of a second, without any specific domain knowledge or in-game coaching from humans. This is no simple task considering that the cube has completion paths numbering in the billions but only one goal state—each of six sides displaying a solid color—which apparently can’t be found through random moves.

For a study published today in Nature Machine Intelligence, the researchers demonstrated that DeepCubeA solved 100 percent of all test configurations, finding the shortest path to the goal state about 60 percent of the time. The algorithm also works on other combinatorial games such as the sliding tile puzzle, Lights Out and Sokoban.

“Artificial intelligence can defeat the world’s best human chess and Go players, but some of the more difficult puzzles, such as the Rubik’s Cube, had not been solved by computers, so we thought they were open for AI approaches,” said senior author Pierre Baldi, UCI Distinguished Professor of computer science. “The solution to the Rubik’s Cube involves more symbolic, mathematical and abstract thinking, so a deep learning machine that can crack such a puzzle is getting closer to becoming a system that can think, reason, plan and make decisions.”

The researchers were interested in understanding how and why the AI made its moves and how long it took to perfect its method. They started with a computer simulation of a completed puzzle and then scrambled the cube. Once the code was in place and running, DeepCubeA trained in isolation for two days, solving an increasingly difficult series of combinations.

“It learned on its own,” Baldi noted.

There are some people, particularly teenagers, who can solve the Rubik’s Cube in a hurry, but even they take about 50 moves.

“Our AI takes about 20 moves, most of the time solving it in the minimum number of steps,” Baldi said. “Right there, you can see the strategy is different, so my best guess is that the AI’s form of reasoning is completely different from a human’s.”

The veteran computer scientist said the ultimate goal of projects such as this one is to build the next generation of AI systems. Whether they know it or not, people are touched by artificial intelligence every day through apps such as Siri and Alexa and recommendation engines working behind the scenes of their favorite online services.

“But these systems are not really intelligent; they’re brittle, and you can easily break or fool them,” Baldi said. “How do we create advanced AI that is smarter, more robust and capable of reasoning, understanding and planning? This work is a step toward this hefty goal.”

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