Health & Bioscience

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class="navigation__sub__columns__mobile__link" > Data Mining & Modeling <span> </span> </a> </li> <li role="menuitem"> <a href="https://research.google/research-areas/information-retrieval-and-the-web/" class="navigation__sub__columns__mobile__link" > Information Retrieval & the Web <span> </span> </a> </li> <li role="menuitem"> <a href="https://research.google/research-areas/machine-intelligence/" class="navigation__sub__columns__mobile__link" > Machine Intelligence <span> </span> </a> </li> <li role="menuitem"> <a href="https://research.google/research-areas/machine-perception/" class="navigation__sub__columns__mobile__link" > Machine Perception <span> </span> </a> </li> <li role="menuitem"> <a href="https://research.google/research-areas/machine-translation/" class="navigation__sub__columns__mobile__link" > Machine Translation <span> </span> </a> </li> <li role="menuitem"> <a href="https://research.google/research-areas/natural-language-processing/" class="navigation__sub__columns__mobile__link" > Natural Language Processing <span> </span> </a> </li> <li role="menuitem"> <a href="https://research.google/research-areas/speech-processing/" class="navigation__sub__columns__mobile__link" > Speech Processing <span> </span> </a> </li> </ul> </div> </div> </li> <li data-gt-secondary="Computing Systems & Quantum AI"> <div class="navigation__sub__columns__desktop"> <h2 class="headline-6 navigation__sub__columns__heading">Computing Systems & Quantum AI</h2> <ul> <li> <a class="navigation__sub__columns__list-link caption js-drawer-link" href="https://research.google/research-areas/distributed-systems-and-parallel-computing/" > Distributed Systems & Parallel Computing </a> </li> <li> <a class="navigation__sub__columns__list-link caption js-drawer-link" href="https://research.google/research-areas/hardware-and-architecture/" > Hardware & Architecture </a> </li> <li> <a class="navigation__sub__columns__list-link caption js-drawer-link" 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</li> <li role="menuitem"> <a href="https://research.google/research-areas/hardware-and-architecture/" class="navigation__sub__columns__mobile__link" > Hardware & Architecture <span> </span> </a> </li> <li role="menuitem"> <a href="https://research.google/research-areas/mobile-systems/" class="navigation__sub__columns__mobile__link" > Mobile Systems <span> </span> </a> </li> <li role="menuitem"> <a href="https://research.google/research-areas/networking/" class="navigation__sub__columns__mobile__link" > Networking <span> </span> </a> </li> <li role="menuitem"> <a href="https://research.google/research-areas/quantum-computing/" class="navigation__sub__columns__mobile__link" > Quantum Computing <span> </span> </a> </li> <li role="menuitem"> <a href="https://research.google/research-areas/robotics/" class="navigation__sub__columns__mobile__link" > Robotics <span> </span> </a> </li> <li role="menuitem"> <a href="https://research.google/research-areas/security-privacy-and-abuse-prevention/" 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class="glue-breadcrumbs__link attribution" href="/">Home</a> <svg role="presentation" aria-hidden="true" class="glue-icon "> <use href="/gr/static/assets/icons/glue-icons.svg#chevron-right"></use> </svg> </li> <li class="glue-breadcrumbs__item"> <a class="glue-breadcrumbs__link attribution" href="/research-areas/">Research areas</a> <svg role="presentation" aria-hidden="true" class="glue-icon "> <use href="/gr/static/assets/icons/glue-icons.svg#chevron-right"></use> </svg> </li> </ol> </nav> </div> <h1 class="headline-1 bhoig__headline glue-grid__col--span-10 glue-grid__col--span-9-md glue-grid__col--span-10-lg">Health & Bioscience</h1> <div class="basic-hero__description bhoig__description glue-grid__col--span-10 glue-grid__col--span-9-md glue-grid__col--span-10-lg"> <p data-block-key="3aleu">Research in health and biomedical sciences has a unique potential to improve peoples’ lives, and includes work ranging from basic science that aims to understand biology, to diagnosing individuals’ diseases, to epidemiological studies of whole populations. We recognize that our strengths in machine learning, large-scale computing, and human-computer interaction can help accelerate the progress of research in this space. By collaborating with world-class institutions and researchers and engaging in both early-stage research and late-stage work, we hope to help people live healthier, longer, and more productive lives.</p> </div> <div class="bhoig__cta glue-grid__col--span-10 glue-grid__col--span-9-md glue-grid__col--span-10-lg"> </div> </div> </div> </section> <section class="offset-two-up"> <div class="glue-page glue-grid"> <div class="offset-two-up__left-col glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-3-lg"> <h3 class="offset-two-up__headline headline-3">Recent Publications</h3> <div class="offset-two-up__cta-below-description"> <a class="glue-button glue-button--medium-emphasis" href="https://research.google/pubs/?category=health-bioscience" > <span class="js-gt-item-id">See More</span> </a> </div> </div> <div class="glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-9-lg"> <div class="publications-list --theme- row-card-list" data-hot-swap="pub-list" data-gt-id="publications_list" data-gt-component-name=""> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=https://research.google/pubs/unsupervised-representation-learning-improves-genomic-discovery-for-lung-function-and-respiratory-disease-prediction/ > Unsupervised representation learning on high-dimensional clinical data improves genomic discovery and prediction </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/taedongyun/"> Taedong Yun </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/justincosentino/"> Justin Cosentino </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Babak Behsaz </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Zachary Ryan Mccaw </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Davin Hill </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Robert Luben </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Dongbing Lai </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> John Bates </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Howard Yang </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Tae-Hwi Schwantes-An </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Yuchen Zhou </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Anthony Khawaja </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Andrew Carroll </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Brian Hobbs </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Michael Cho </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/corymclean/"> Cory McLean </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/107452/"> Farhad Hormozdiari </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Nature Genetics (2024) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentalthough-high-dimensional-clinical tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentalthough-high-dimensional-clinical" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Although high-dimensional clinical data (HDCD) are increasingly available in biobank-scale datasets, their use for genetic discovery remains challenging. Here we introduce an unsupervised deep learning model, Representation Learning for Genetic Discovery on Low-Dimensional Embeddings (REGLE), for discovering associations between genetic variants and HDCD. REGLE leverages variational autoencoders to compute nonlinear disentangled embeddings of HDCD, which become the inputs to genome-wide association studies (GWAS). REGLE can uncover features not captured by existing expert-defined features and enables the creation of accurate disease-specific polygenic risk scores (PRSs) in datasets with very few labeled data. We apply REGLE to perform GWAS on respiratory and circulatory HDCD—spirograms measuring lung function and photoplethysmograms measuring blood volume changes. REGLE replicates known loci while identifying others not previously detected. REGLE are predictive of overall survival, and PRSs constructed from REGLE loci improve disease prediction across multiple biobanks. Overall, REGLE contain clinically relevant information beyond that captured by existing expert-defined features, leading to improved genetic discovery and disease prediction.</span> <a class="glue-button glue-button--low-emphasis" href="https://research.google/pubs/unsupervised-representation-learning-improves-genomic-discovery-for-lung-function-and-respiratory-disease-prediction/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=https://research.google/pubs/artificial-intelligence-in-healthcare-a-perspective-from-google/ > Artificial Intelligence in Healthcare: A Perspective from Google </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> Lily Peng </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Lisa Lehmann </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/106654/"> Vivek Natarajan </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Artificial Intelligence in Healthcare, Elsevier (2024) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentartificial-intelligence-ai-holds tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentartificial-intelligence-ai-holds" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Artificial Intelligence (AI) holds the promise of transforming healthcare by improving patient outcomes, increasing accessibility and efficiency, and decreasing the cost of care. Realizing this vision of a healthier world for everyone everywhere requires partnerships and trust between healthcare systems, clinicians, payers, technology companies, pharmaceutical companies, and governments to drive innovations in machine learning and artificial intelligence to patients. Google is one example of a technology company that is partnering with healthcare systems, clinicians, and researchers to develop technology solutions that will directly improve the lives of patients. In this chapter we share landmark trials of the use of AI in healthcare. We also describe the application of our novel system of organizing information to unify data in electronic health records (EHRs) and bring an integrated view of patient records to clinicians. We discuss our consumer focused innovation in dermatology to help guide search journeys for personalized information about skin conditions. Finally, we share a perspective on how to embed ethics and a concern for all patients into the development of AI.</span> <a class="glue-button glue-button--low-emphasis" href="https://research.google/pubs/artificial-intelligence-in-healthcare-a-perspective-from-google/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=https://research.google/pubs/three-epochs-of-artificial-intelligence-for-healthcare/ > Three Epochs of Artificial Intelligence for Healthcare </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/michaeldhowellmdmph/"> Michael Howell </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/gregcorrado/"> Greg Corrado </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/107589/"> Karen DeSalvo </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> JAMA (2024) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentimportance-interest-in-artificial tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentimportance-interest-in-artificial" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Importance: Interest in artificial intelligence (AI) has reached an all-time high, and health care leaders across the ecosystem are faced with questions about where, when, and how to deploy AI and how to understand its risks, problems, and possibilities. Observations: While AI as a concept has existed since the 1950s, all AI is not the same. Capabilities and risks of various kinds of AI differ markedly, and on examination 3 epochs of AI emerge. AI 1.0 includes symbolic AI, which attempts to encode human knowledge into computational rules, as well as probabilistic models. The era of AI 2.0 began with deep learning, in which models learn from examples labeled with ground truth. This era brought about many advances both in people’s daily lives and in health care. Deep learning models are task-specific, meaning they do one thing at a time, and they primarily focus on classification and prediction. AI 3.0 is the era of foundation models and generative AI. Models in AI 3.0 have fundamentally new (and potentially transformative) capabilities, as well as new kinds of risks, such as hallucinations. These models can do many different kinds of tasks without being retrained on a new dataset. For example, a simple text instruction will change the model’s behavior. Prompts such as “Write this note for a specialist consultant” and “Write this note for the patient’s mother” will produce markedly different content. Conclusions and Relevance: Foundation models and generative AI represent a major revolution in AI’s capabilities, ffering tremendous potential to improve care. Health care leaders are making decisions about AI today. While any heuristic omits details and loses nuance, the framework of AI 1.0, 2.0, and 3.0 may be helpful to decision-makers because each epoch has fundamentally different capabilities and risks.</span> <a class="glue-button glue-button--low-emphasis" href="https://research.google/pubs/three-epochs-of-artificial-intelligence-for-healthcare/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=https://research.google/pubs/mindful-breathing-as-an-effective-technique-in-the-management-of-hypertension/ > Mindful Breathing as an Effective Technique in the Management of Hypertension </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> Aravind Natarajan </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Hulya Emir-Farinas </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Hao-Wei Su </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Frontiers in Physiology, N/A (2024), N/A </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentintroduction-hypertension-is-one tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentintroduction-hypertension-is-one" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Introduction: Hypertension is one of the most important, modifiable risk factors for cardiovascular disease. The popularity of wearable devices provides an opportunity to test whether device guided slow mindful breathing may serve as a non-pharmacological treatment in the management of hypertension. Methods: Fitbit Versa-3 and Sense devices were used for this study. In addition, participants were required to own an FDA or Health Canada approved blood pressure measuring device. Advertisements were shown to 655,910 Fitbit users, of which 7,365 individuals expressed interest and filled out the initial survey. A total of 1,918 participants entered their blood pressure readings on at least 1 day and were considered enrolled in the study. Participants were instructed to download a guided mindful breathing app on their smartwatch device, and to engage with the app once a day prior to sleep. Participants measured their systolic and diastolic blood pressure prior to starting each mindful breathing session, and again after completion. All measurements were self reported. Participants were located in the United States or Canada. Results: Values of systolic and diastolic blood pressure were reduced following mindful breathing. There was also a decrease in resting systolic and diastolic measurements when measured over several days. For participants with a systolic pressure ≥ 130 mmHg, there was a decrease of 9.7 mmHg following 15 min of mindful breathing at 6 breaths per minute. When measured over several days, the resting systolic pressure decreased by an average of 4.3 mmHg. Discussion: Mindful breathing for 15 min a day, at a rate of 6 breaths per minute is effective in lowering blood pressure, and has both an immediate, and a short term effect (over several days). This large scale study demonstrates that device guided mindful breathing with a consumer wearable for 15 min a day is effective in lowering blood pressure, and a helpful complement to the standard of care.</span> <a class="glue-button glue-button--low-emphasis" href="https://research.google/pubs/mindful-breathing-as-an-effective-technique-in-the-management-of-hypertension/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=https://research.google/pubs/enhancing-molecular-selectivity-using-wasserstein-distance-based-reweighing/ > Enhancing molecular selectivity using Wasserstein distance based reweighing </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/pratikworah/"> Pratik Worah </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Recomb 24 (2024) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentgiven-a-training-data-set-mathca tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentgiven-a-training-data-set-mathca" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Given a training data-set $\mathcal{S}$, and a reference data-set $\mathcal{T}$, we design a simple and efficient algorithm to reweigh the loss function such that the limiting distribution of the neural network weights that result from training on $\mathcal{S}$ approaches the limiting distribution that would have resulted by training on $\mathcal{T}$. Such reweighing can be used to correct for Train-Test distribution shift, when we don't have access to the labels of $\mathcal{T}$. It can also be used to perform (soft) multi-criteria optimization on neural nets, when we have access to the labels of $\mathcal{T}$, but $\mathcal{S}$ and $\mathcal{T}$ have few common points. As a motivating application, we train a graph neural net to recognize small molecule binders to MNK2 (a MAP Kinase, responsible for cell signaling) which are non-binders to MNK1 (a very similar protein), even in the absence of training data common to both data-sets. We are able to tune the reweighing parameters so that overall change in holdout loss is negligible, but the selectivity, i.e., the fraction of top 100 MNK2 binders that are MNK1 non-binders, increases from 54\% to 95\%, as a result of our reweighing. We expect the algorithm to be applicable in other settings as well, since we prove that when the metric entropy of the input data-sets is bounded, our random sampling based greedy algorithm outputs a close to optimal reweighing, i.e., the two invariant distributions of network weights will be provably close in total variation distance.</span> <a class="glue-button glue-button--low-emphasis" href="https://research.google/pubs/enhancing-molecular-selectivity-using-wasserstein-distance-based-reweighing/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=https://research.google/pubs/understanding-metric-related-pitfalls-in-image-analysis-validation/ > Understanding metric-related pitfalls in image analysis validation </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/107444/"> Alan Karthikesalingam </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Annika Reinke </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Lena Maier-Hein </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Paul Jager </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Shravya Shetty </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Understanding Metrics Workgroup </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Nature Methods (2024) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentvalidation-metrics-are-key-for-the tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentvalidation-metrics-are-key-for-the" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. 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