Cancer survival prediction is a challenging task that involves analyzing of the tumor microenvironment within Whole Slide Image (WSI). Previous methods cannot effectively capture the intricate interaction features among instances within the local area of WSI. Moreover, existing methods for cancer survival prediction based on WSI often fail to provide better clinically meaningful predictions. To overcome these challenges, we propose a Sparse Context-aware Multiple Instance Learning (SCMIL) framework for predicting cancer survival probability distributions. SCMIL innovatively segments patches into various clusters based on their morphological features and spatial location information, subsequently leveraging sparse self-attention to discern the relationships between these patches with a context-aware perspective. Considering many patches are irrelevant to the task, we introduce a learnable patch filtering module called SoftFilter, which ensures that only interactions between task-relevant patches are considered. To enhance the clinical relevance of our prediction, we propose a register-based mixture density network to forecast the survival probability distribution for individual patients. We evaluate SCMIL on two public WSI datasets from the The Cancer Genome Atlas (TCGA) specifically focusing on lung adenocarcinom (LUAD) and kidney renal clear cell carcinoma (KIRC). Our experimental results indicate that SCMIL outperforms current state-of-the-art methods for survival prediction, offering more clinically meaningful and interpretable outcomes. Our code is accessible at https://github.com/yang-ze-kang/SCMIL.

An accurate model of patient survival time can help in the treatment and care of cancer patients. The common practice of providing survival time estimates based only on population averages for the site and stage of cancer ignores many important individual differences among patients. In this paper, we propose a local regression method for learning patient-specific survival time distribution based on patient attributes such as blood tests and clinical assessments. When tested on a cohort of more than 2000 cancer patients, our method gives survival time predictions that are much more accurate than popular survival analysis models such as the Cox and Aalen regression models. Our results also show that using patient-specific attributes can reduce the prediction error on survival time by as much as 20% when compared to using cancer site and stage only.

Knowledge graphs (KGs) have become a cornerstone in organizing and utilizing information across various domains, from enhancing search engines to improving recommendation systems. KGs comprise nodes (entities) and edges (relations) that depict the knowledge within a specific field or a collection of domains. The potential of KGs to enable intricate reasoning and inference has been investigated across various endeavors, encompassing tasks such as information retrieval, and knowledge discovery. While KGs have come a long way, representing knowledge effectively remains a formidable challenge, especially in complex fields like healthcare and biomedicine. This article highlights our recent publication Representation Learning for Person or Entity-centric Knowledge Graphs: An Application in Healthcare (presented at K-CAP 2023) and explores the concept of entity-centric knowledge graphs, a relatively uncharted territory in the KG landscape, but one that holds immense promise in reshaping how we organize, access, and leverage data.

Early diagnosis of diseases holds the potential for deep transformation in healthcare by enabling better treatment options, improving long-term survival and quality of life, and reducing overall cost. With the advent of medical big data, advances in diagnostic tests as well as in machine learning and statistics, early or timely diagnosis seems within reach. Early diagnosis research often neglects the potential for optimizing individual diagnostic paths. To enable personalized early diagnosis, a foundational framework is needed that delineates the diagnosis process and systematically identifies the time-dependent value of various diagnostic tests for an individual patient given their unique characteristics. Here, we propose the first foundational framework for early and timely diagnosis. It builds on decision-theoretic approaches to outline the diagnosis process and integrates machine learning and statistical methodology for estimating the optimal personalized diagnostic path. To describe the proposed framework as well as possibly other frameworks, we provide essential definitions. The development of a foundational framework is necessary for several reasons: 1) formalism provides clarity for the development of decision support tools; 2) observed information can be complemented with estimates of the future patient trajectory; 3) the net benefit of counterfactual diagnostic paths and associated uncertainties can be modeled for individuals 4) 'early' and 'timely' diagnosis can be clearly defined; 5) a mechanism emerges for assessing the value of technologies in terms of their impact on personalized early diagnosis, resulting health outcomes and incurred costs. Finally, we hope that this foundational framework will unlock the long-awaited potential of timely diagnosis and intervention, leading to improved outcomes for patients and higher cost-effectiveness for healthcare systems.

From the soaring costs of US healthcare to the recurrent NHS crisis, it can often seem that effective and affordable healthcare is impossible. This will only get worse as chronic conditions grow in prevalence and we discover new ways to treat previously fatal diseases. These new treatments tend to be costly, while new approaches can be hard to introduce into healthcare systems that are either resistant to change or fatigued by too much of it. Meanwhile, growing demand for social care is compounding funding pressure and making the allocation of resources even more complicated. Artificial intelligence (AI) is often glibly posed as the answer for services that are already forced to do more with less.

Fox News medical contributor Dr. Marc Siegel joins'Fox & Friends' to discuss the benefits of artificial intelligence in the medical industry if used with caution. Artificial intelligence is taking on an ever-widening role in the health and wellness space, assisting with everything from cancer detection to medical documentation. Soon, AI could make it easier for dentists to give patients a more natural, functional smile. Researchers from the University of Hong Kong recently developed an AI algorithm that uses 3D machine learning to design personalized dental crowns with a higher degree of accuracy than traditional methods, according to a press release from the university. The AI analyzes data from the teeth adjacent to the crown to ensure a more natural, precise fit than the crowns created using today's methods, the researchers said.

The healthcare sector has grown by leaps and bounds in the past few years and Artificial Intelligence (AI) has dramatically transformed the healthcare sector leading to many promising discoveries and outcomes. AI has enabled practitioners to deploy precise, timely and impactful interventions, synonymous with an engine that drives constant improvements across the care continuum. The pandemic has accelerated AI adoption which directly correlates with researchers conducting millions of experiments by simulating chemistry with computers, identifying more compounds that could pass the regulatory process and speeding up the drug discovery process. According to Nasscom's report, data analytics and AI in the healthcare sector can boost India's GDP by $25-$30 billion by 2025. AI and Machine Learning (ML) technologies are assisting businesses in making healthcare accessible to all sections of the country, including the most distant ones.

Cancer patients have been given new hope with a test that analyses tumours to predict the most effective drugs for individual patients. The breakthrough produces a result in as little as 24 hours and is more accurate than current genetic approaches to personalising treatment. Scientists from the Institute of Cancer Research in London say the technique, using artificial intelligence to analyse large amounts of data from tumour samples, allows doctors to quickly establish which drug combinations are most likely to work. Researchers tested tumour cells from lung cancer patients, examining how seven drugs affected 52 proteins linked to the disease's growth and spread. Of 252 combinations of drugs tested, 128 showed some level of synergy, meaning their combined effect exceeded that of each individual drug added together.

The Covid-19 crisis has placed the spotlight on the healthcare systems around the world. It placed an additional strain on systems that in many cases were already under stress to meet demand and led to a growth in digital medicine. A video from the BBC observers that Covid-19 brings remote medicine revolution to the UK "Apps which allow doctors to connect with patients remotely have been available for a while, but the coronavirus pandemic has seen doctors finding new ways to consult with critical patient care, including reviewing scans and X-rays from home." McKinsey in an article relating to the US healthcare situation and entitled "Preparing for the next normal now: How health systems can adopt a growth transformation in the COVID-19 world" state that "Covid-19 unprecedented impact on health, economies, and daily life has created a humanitarian crisis. Health systems have been at the epicenter of the fight against COVID-19, and have had to balance the need to alleviate suffering and save lives with substantial financial pressures."

As the country moves toward value-based care, artificial intelligence and machine learning – paired with data interoperability – have the potential to improve patient outcomes while driving operational efficiency to lower the overall cost of care. By enabling interoperability securely and supporting healthcare providers with predictive machine learning models and insights afforded by genomic research, clinicians will be able to seamlessly forecast clinical events – such as strokes, cancer or heart attacks – and intervene early with personalized care and access to curated information to support a superior patient experience. Further powering these predictive capabilities with location-agnostic, voice-enabled, accessible modalities of providing care advances the practice of medicine to align with what is most convenient, affordable and targeted for the specific needs of patients. Healthcare IT News sat down with Phoebe Yang, general manager for non-profit healthcare at Amazon Web Services, to discuss these subjects, offering healthcare CIOs and other health IT leaders lessons in state-of-the-art technologies. How can AI and machine learning combined with data interoperability enhance patient outcomes and operational efficiency to lower care costs? A. Interoperability among medical information systems is foundational – or should be – because without it, physicians don't have ready access to their patients' complete medical histories.