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New AI Can Read Patient Data Accurately

Reading patient data manually takes a huge amount of time. Hence, U.S. Scientists have developed a new, automated, AI-based algorithm that can learn to read patient data from Electronic Health Records (EHR). The scientists, in a side-by-side comparison, showed that their method accurately identified patients with certain diseases as well as the traditional, “gold-standard” method, which requires much more manual labour to develop and perform.

There continues to be an explosion in the amount and types of data electronically stored in a patient’s medical record. Extracting and analysing this complex web of data can be highly ineffective, thus slowing advancements in clinical research.

In this study, we created a new method for mining data from electronic health records with machine learning that is faster and less labour intensive than the industry standard. We hope that this will be a valuable tool that will facilitate further, and less biased, research in clinical informatics.

– Assistant Professor of Genetics and Genomic Sciences

Currently, to mine medical records for new information, scientists rely on a set of established computer programmes or algorithms. A system called the Phenotype Knowledgebase (PheKB) manages the development and storage of these algorithms. While the system is highly effective at correctly identifying a patient diagnosis, the process of developing an algorithm can be very time-consuming and inflexible.

For instance, when researchers want to study disease. They first have to scour through all the medical records to look for relevant information, such as certain lab tests or prescriptions, which are uniquely associated with the disease.

They then programme the algorithm that guides the computer to search for patients who have those disease-specific pieces of data, which constitute a “phenotype”. In turn, the list of patients identified by the computer needs to be manually double-checked by researchers. Each time researchers want to study a new disease, they have to restart the process from scratch. In this study, the researchers tried a different approach in which the computer learns on its own, such as how to spot disease phenotypes and thus save researchers time and effort.

A senior author of the study stated that, previously, the researchers showed that unsupervised machine learning could be a highly efficient and effective strategy for mining EHR. The potential advantage of their approach is that it learns representations of diseases from the data itself. Therefore, the machine does much of the work experts would normally do to define the combination of data elements from health records that best describes a particular disease.

Essentially, a computer was programmed to scour through millions of EHR and learn how to find connections between data and diseases. This programming relied on “embedding” algorithms that had been previously developed by other researchers, such as linguists, to study word networks in various languages. One of the algorithms, called word2vec, was particularly effective. Then, the computer was programmed to use what it learned to identify the diagnoses of nearly 2 million patients whose data was stored in the health system.

Finally, the researchers compared the effectiveness between the new and the old systems. For nine out of ten diseases tested, they found that the new Phe2vec system was as effective as, or performed slightly better than, the gold standard phenotyping process at correctly identifying diagnoses from EHR.

Overall the results are encouraging and suggest that the system is a promising technique for large-scale phenotyping of diseases in EHR data. With further testing and refinement, they hope that it could be used to automate many of the initial steps of clinical informatics research, thus allowing scientists to focus their efforts on downstream analyses like predictive modelling.

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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 business services provider. We help 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 3,000 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.