A study reveals the use of machine learning algorithms leveraging EHR data could assist in a patient’s lung cancer prognosis.

By using machine learning algorithms, researchers examined if creating a large-scale electronic health record (EHR) data-based lung cancer cohort could be effective in studying a patient’s prognosis and estimating survival. The cohort study was recently published in JAMA.

Across the world, lung cancer is one of the most diagnosed cancers and is the leading cause of cancer-related deaths behind skin cancer. In the United States, the current five-year survival rate is around 20.6 percent. However, patients with lung cancer will have different outcomes based on a variety of clinical factors.

“A large cohort with adequate clinical information is necessary to identify stable and reliable prognostic variables and the factors associated with improved survival outcomes,” the authors wrote in the study.

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As the accessibility of EHR data continues to grow, researchers are given a timely and low-cost alternative to the traditional cohort study. With EHR data being coding in various ways, implementing machine learning algorithms was an important step for researchers to compare information accurately.

“Our primary goal was to build a large and reliable lung cancer EHR cohort that could be used for studying lung cancer progression with a set of generalizable approaches. To this end, we combined structured data and unstructured data to identify patients with lung cancer and extract clinical variables. We evaluated the completeness and accuracy of the extracted data,” the authors wrote.

“To further illustrate the application of EHR cohort data, we developed and validated a prognostic model to predict 1-year to 5-year overall survival (OS) among individuals with non–small cell lung cancer (NSCLC).,” the study authors continued.

In the cohort study, patients with lung cancer were identified from 76,643 individuals with at least one lung cancer diagnostic coded deposited in an EHR in Mass General Brigham health care system from July 1988 to October 2018.

A machine learning algorithm identified patients and extracted clinical information from structured and unstructured data by using natural language processing tools. Researchers then examined the data’s completeness and accuracy by comparing the Boston Lung Cancer study to the standard EHR review results.

Additionally, a prognostic model for non-small cell lung cancer (NSCLC) overall survival was created for clinical application.

Of the 76,642 patients with at least one lung cancer diagnostic code, 42,069 patients were identified to have lung cancer. The AI tool produced a positive predictive value of 94.4 percent. The study cohort was made up of 35,375 patients after removing those with a history of lung cancer and less than 14 days of follow-up after the initial diagnosis.

“We assembled a large lung cancer cohort from EHRs using a phenotyping algorithm and extraction strategies combining structured and unstructured data. Our findings suggest that a prognostic model based on EHR cohort may be used conveniently to facilitate prediction of NSCLC survival,” the authors concluded.

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Capgemini’s Project Marconi is using machine learning on a radio access network card using an Intel processor boosts spectral efficiency by 15 percent for low latency 5G applications

European consultancy Capgemini has developed a machine learning framework that works with OpenRAN hardware to boost spectral efficiency in 5G cellular networks

Project Marconi uses the Open Radio Access Network OpenRAN guidelines to maximize spectrum efficiency with real-time predictive analytics in a 5G Mediua Access Control (MAC) schedule. This is optimised for Intel’s AI Software and third generation Intel Xeon Scalable processors.

Capgemini used its NetAnticipate5G and RATIO OpenRAN platform to introduce advanced AI/ML techniques. The AI powered predictive analytical solution forecasts and assigns the appropriate MCS (modulation and coding scheme) values for signal transmission through forecasting of the user signal quality and mobility patterns accurately. The project improved AI accuracy to 55 percent and reduced AI inference time to 0.64msec, a 41 percent improvement.

In this way, the RAN can intelligently schedule MAC resources to achieve up to 40 percent more accurate MCS prediction which gives 15 percent better spectrum efficiency in testing. This is particularly important for applications that use low latency connectivity such as robotics-based manufacturing and V2X (vehicle-to-everything).

The Capgemini ML software on the Intel Xeon increases the amount of traffic each cell can handle and allows more subscribers alongside new Industry 4.0 services such as enhanced Mobile Broadband (eMBB) and Ultra Reliable Low Latency Communications (URLLC) use cases.

“Our teams worked closely with Intel to create a truly innovative solution that can really move the needle for operators,” said Walid Negm, Chief Research and Innovation Officer at Capgemini Engineering. “We gathered and utilized over one terabyte of data and conducted countless test runs with NetAnticipate5G to fine-tune the predictive analytics to meet diverse operator requirements. In short, machine learning can be deployed for intelligent decision-making on the RAN without any additional hardware requirement. This makes it cost efficient in the short run and future proof in the long run as we move into Cloud Native RAN implementations.”

“Our 3rd Gen Intel Xeon Scalable processors with built-in AI acceleration provide high performance for deep learning on the Net Anticipate 5G platform. Together, our collaboration delivered ultra-fast inference data to enhance the Open-Source ML libraries resulting in an intelligent RAN that can predict and quickly react to subscriber coverage requirements while reducing TCO,” said Cristina Rodriguez, VP of Wireless Access Network Division at Intel.

The €16bn Capgemini group has now integrated engineering group Altran that includes Cambridge Consultants, and has opened 5G R&D labs around Europe.

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Google recently released TensorFlow Quantum, a toolset for combining state-of-the-art machine learning techniques with quantum algorithm design. This is an essential step to build tools for developers working on quantum applications.

Simultaneously, they have focused on improving quantum computing hardware performance by integrating a set of quantum firmware techniques and building a TensorFlow-based toolset working from the hardware level up – from the bottom of the stack.

The fundamental driver for this work is tackling the noise and error in quantum computers. Here’s a small overview of the above and how the impact of noise and imperfections (critical challenges) is suppressed in quantum hardware. 

Noise And Error: The Chinks In Armor When It Comes To Quantum Computers

Quantum computing combines information processing and quantum physics to solve challenging computer problems. However, a significant issue in quantum computers is susceptibility to noise and error, limiting quantum computing hardware efficiency. Noise refers to all sorts of things that can cause interference, like the electromagnetic signals from the WiFi or disturbances in the Earth’s magnetic field. Most quantum computing hardware can run just a few dozen calculations over much less than 1 ms before requiring a reset due to the noise’s influence. That is about 1024 times worse than the hardware in a laptop.

Many teams have been working to make the hardware resistant to the noise to overcome these weaknesses. Many theorists have also designed a smart algorithm called Quantum Error Correction. QEA can identify and fix errors in the hardware, but it is very slow or incapable of practice. Because the information is to be spread in one qubit over lots of qubits, it may take a thousand or more physical qubits to realize just one error-corrected “logical qubit.”

To overcome this, Q-CTRL’s “quantum firmware” can stabilize the qubits against noise and decoherence without the need for extra resources. This is done by adding the new solutions that improve the hardware’s robustness to the error at the lowest layer of the quantum computing stack.

The protocols described by the Quantum firmware are there to deliver the quantum hardware with augmented performance to higher levels of the abstraction in the quantum computing stack.

In general, quantum computing hardware relies on light-matter interaction, which is made to enact quantum logic operations.

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