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Advancing Personalised Medicine with Machine Learning in the U.S.

A new study by the Oregon State University College of Engineering shows that machine learning techniques can offer powerful new tools for advancing personalised medicine, care that optimises outcomes for individual patients based on unique aspects of their biology and disease features.

The research with machine learning, a branch of Artificial Intelligence (AI) in which computer systems use algorithms and statistical models to look for trends in data, tackles long-unsolvable problems in biological systems at the cellular level.

Those systems tend to have high complexity—first, because of the vast number of individual cells and second, because of the highly nonlinear way in which cells can behave. Nonlinear systems present a challenge for upscaling methods, which is the primary means by which researchers can accurately model biological systems at the larger scales that are often the most relevant.

The new research, funded in part by the U.S. Department of Energy and published in the Journal of Computational Physics, is one of the first examples of using machine learning to address issues with modelling nonlinear systems and understanding complex processes that might occur in human tissues.

The advent of machine learning has given us a new tool in our arsenal to solve problems we could not solve before. While the tools themselves are not necessarily new, the particular applications we have are very different. We are beginning to apply machine learning in a more constrained way, and this is allowing us to solve physical problems we had no way of solving before.

– Professor, Environmental Engineering, Oregon State University

In modelling cellular activity within an organ, it is not possible to individually model each cell in that organ—a cubic centimetre of tissue may contain a billion cells—so researchers rely on what’s known as upscaling.

Upscaling seeks to decrease the data required to analyze or model a particular biological process while maintaining the fidelity—the degree to which a model accurately reproduces something—of the core biology, chemistry and physics occurring at the cellular level.

By reducing the information load for a very complicated system at the cellular level, researchers can better analyse and model the impact or response of those cells with high fidelity without having to model each individual one. The researchers describe it as simplifying a computational problem that has tens of millions of data points by reducing it to thousands of data points.

The new approach could pave the way to potential patient treatments based on numerical model outcomes. In this study, researchers were able to employ machine learning and develop a novel method to resolve classic nonlinear problems in biological and chemical systems.

The promises of individualised medicine are rapidly becoming a reality. The combination of multiple disciplines—such as molecular biology, applied mathematics and continuum mechanics—are being combined in new ways to make this possible. One of the key components of this will certainly be the continuing advances in machine learning methods.

U.S. researchers have been utilising machine learning in various areas, including healthcare. As reported by OpenGov Asia, to help clinicians avoid remedies that may potentially contribute to a patient’s death, researchers at MIT have developed a machine learning model that could be used to identify treatments that pose a higher risk than other options. Their model can also warn doctors when a septic patient is approaching a medical dead end — the point when the patient will most likely die no matter what treatment is used — so that they can intervene before it is too late.

When utilised to a dataset of sepsis sufferers in a hospital intensive care unit, the investigator mannequin confirmed that about 12% of the therapies for deceased sufferers have been dangerous. The research additionally exhibits that about 3 p.c of sufferers who didn’t survive have been caught in a medical stalemate 48 hours earlier than demise.

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Qlik’s vision is a data-literate world, where everyone can use data and analytics to improve decision-making and solve their most challenging problems. A private company, Qlik offers real-time data integration and analytics solutions, powered by Qlik Cloud, to close the gaps between data, insights and action. By transforming data into Active Intelligence, businesses can drive better decisions, improve revenue and profitability, and optimize customer relationships. Qlik serves more than 38,000 active customers in over 100 countries.

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As a Titanium Black Partner of Dell Technologies, CTC Global Singapore boasts unparalleled access to resources.

Established in 1972, we bring 52 years of experience to the table, solidifying our position as a leading IT solutions provider in Singapore. With over 300 qualified IT professionals, we are dedicated to delivering integrated solutions that empower your organization in key areas such as Automation & AI, Cyber Security, App Modernization & Data Analytics, Enterprise Cloud Infrastructure, Workplace Modernization and Professional Services.

Renowned for our consulting expertise and delivering expert IT solutions, CTC Global Singapore has become the preferred IT outsourcing partner for businesses across Singapore.

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Planview has one mission: to build the future of connected work. Our solutions enable organizations to connect the business from ideas to impact, empowering companies to accelerate the achievement of what matters most. Planview’s full spectrum of Portfolio Management and Work Management solutions creates an organizational focus on the strategic outcomes that matter and empowers teams to deliver their best work, no matter how they work. The comprehensive Planview platform and enterprise success model enables customers to deliver innovative, competitive products, services, and customer experiences. Headquartered in Austin, Texas, with locations around the world, Planview has more than 1,300 employees supporting 4,500 customers and 2.6 million users worldwide. For more information, visit www.planview.com.

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SIRIM is a premier industrial research and technology organisation in Malaysia, wholly-owned by the Minister​ of Finance Incorporated. With over forty years of experience and expertise, SIRIM is mandated as the machinery for research and technology development, and the national champion of quality. SIRIM has always played a major role in the development of the country’s private sector. By tapping into our expertise and knowledge base, we focus on developing new technologies and improvements in the manufacturing, technology and services sectors. We nurture Small Medium Enterprises (SME) growth with solutions for technology penetration and upgrading, making it an ideal technology partner for SMEs.

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HashiCorp provides infrastructure automation software for multi-cloud environments, enabling enterprises to unlock a common cloud operating model to provision, secure, connect, and run any application on any infrastructure. HashiCorp tools allow organizations to deliver applications faster by helping enterprises transition from manual processes and ITIL practices to self-service automation and DevOps practices. 

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IBM is a leading global hybrid cloud and AI, and consulting services provider, helping clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. Nearly 3,800 government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM’s hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently, and securely. IBM’s breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and business services deliver open and flexible options to our clients. All of this is backed by IBM’s legendary commitment to trust, transparency, responsibility, inclusivity, and service. For more information, visit www.ibm.com