Healthcare and life sciences

Research and innovation addressing today's greatest health challenges.

 

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Enabling better health outcomes with science and technology

The IBM Research Healthcare and Life Sciences team is dedicated to exploring and developing new methodologies and improving processes for a broad range of health care challenges. From how we can help in the diagnosis of diseases, to managing population health, or a better understanding the human genome, the team blends a broad set of disciplines such as biology, chemistry, data analytics, AI and medicine to pursue their work.

Combating the opioid epidemic with machine learning

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Combating the opioid epidemic with machine learning

IBM Research is taking the first steps in understanding the exact circumstances under which medically sanctioned treatments can devolve into addiction. The team includes a graduate student intern who is driving the effort, several machine learning and data science researchers from IBM Research, and experts from IBM Watson Health. Given our combination of industry-leading data on healthcare claims, the domain knowledge and experience to leverage this data, and the statistical and machine learning expertise to deploy the right analytical tools, we believe that we are in a unique position to uncover new insights to address the opioids problem.

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How AI and machine learning aid schizophrenia research

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How AI and machine learning aid schizophrenia research

Earlier this year, IBM scientists collaborated with researchers at the University of Alberta and the IBM Alberta Centre for Advanced Studies (CAS) to publish new research regarding the use of AI and machine learning algorithms to predict instances of schizophrenia with 74 percent accuracy. The research also shows a further capability to predict the severity of specific symptoms in schizophrenia patients – something that was not possible before. Using AI and machine learning, ‘computational psychiatry’ can be used to help clinicians more quickly assess – and therefore treat – patients with schizophrenia.

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Using Watson for Genomics to understand brain tumors

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Using Watson for Genomics to understand brain tumors

About a hundred thousand cases of brain tumors are diagnosed a year in the US, and a quarter of these are gliomas, or tumors of the supportive tissue of the brain. They account for 75 percent of all malignant tumors, and nearly 50 percent of the gliomas are glioblastoma multiforme (GBM). Current commercially available genomic testing targets a small panel of a patient’s genes. A team of IBM researchers extended this analysis to a patient’s entire genome. They found that this results in identifying more variants of their individual genome that can be potentially targeted for therapy by an oncologist.

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Using AI and science to predict heart failure

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Using AI and science to predict heart failure

Heart failure is very hard to detect early, but with the help of a National Institutes of Health (NIH) grant, a team of scientists at IBM Research partnered with scientists from Sutter Health and clinical experts from Geisinger Health System to study and predict heart failure based on hidden clues in Electronic Health Records (EHRs). Over the last three years, using the latest advances in artificial intelligence (AI) like natural language processing, machine learning and big data analytics, the team trained models to identify heart failure one to two years earlier than a typical diagnosis today. This research uncovered important insights about the practical tradeoffs and types of data needed to train models, and developed new application methods that could allow future models to be more easily adopted.

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First-of-a-kind microbiome dataset published in Nature

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First-of-a-kind microbiome dataset published in Nature

It’s becoming increasingly clear that our health is influenced by our personal complement of microbes – our microbiome. Awareness of the microbiome has grown in leaps and bounds thanks to the massive capacity of scientific instruments that read the DNA of microbes. But many fundamental questions about microbes remain unanswered, even questions that seem like they should be easy. “Has anyone seen this microbe before?” “Where? “When?” After today, answering these questions will be a lot easier. The problem was not lack of data, but that each microbiome dataset was an island onto itself and not easily compared to the others. Working as a team of microbial ecologists, computational scientists, bioinformaticians, and statisticians, we analyzed the largest collection of microbiome data (by 100 times). In the current issue of Nature, we report the first-of-a-kind microbiome database that lets researchers track microbes across the planet, even if the microbes don’t have a name (as is usually the case).

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Read in Nature


Detecting diabetic retinopathy

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IBM announces new results for detecting diabetic retinopathy

IBM released the results of new research using deep learning and visual analytics technology to advance early detection of diabetic retinopathy (DR). The study found that the method created by the research team achieved an accuracy score of 86 percent in classifying the serverity of the disease across five levels.


Research areas

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Computational genomics

Progressing the intersection of algorithmics and genomics, using mathematical and statistical modeling.

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Nanobiology

Applying nanotechnology to biology and medicine, with a focus on precision diagnostics, the exploration of micro and nanoscale materials, and micro and nanofluidics.

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Healthcare informatics

Researching the application of data science across the entire spectrum of healthcare, including the health of individuals, populations and the healthcare system itself.

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Multi-scale modeling

Developing theoretical and computational frameworks to bridge the differences in spatial and temporal scale of human biology using mechanistic models, statistical models and machine learning.

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Drug discovery

Enabling and expediting the identification, discovery and design of safe and effective drugs using data science, new tools and approaches.

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Cognitive IoT and devices

Creating systems and interfaces that engage users and embed cognitive insights into everyday interaction

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