AI is being increasingly incorporated by doctors to transcribe, read, analyze, and make predictions based on notes and conversations between physicians and their patients. This opens up new possibilities for care and new concerns about privacy, according to a recent account from Axios. A big and largely invisible contribution AI can make is to capture a physician's written or spoken notes automatically. Spending hours entering data manually into electronic health records (EHRs) is not helpful to medical professionals close to burning out. A recent study from researchers at the University of New Mexico, outlined in EHR Intelligence, found that 13% of stress and burnout self-reported by physicians were directly correlated to EHRs.
The field of biological sciences is becoming increasingly information-intensive and data-rich. For example, the growing availability of DNA sequence data or clinical measurements from humans promises a better understanding of the important questions in biology. However, the complexity and high-dimensionality of these biological data make it difficult to pull out mechanisms from the data. Machine Learning techniques promise to be useful tools for resolving such questions in biology because they provide a mathematical framework to analyze complex and vast biological data. In turn, the unique computational and mathematical challenges posed by biological data may ultimately advance the field of machine learning as well.
See article by Horng et al in this issue. William F. Auffermann, MD, PhD, is an associate professor of radiology and imaging sciences at the University of Utah School of Medicine. Dr Auffermann is a cardiothoracic radiologist and is ABPM board certified in clinical informatics. His research interests include imaging informatics, clinical informatics, applications of AI in radiology, medical image perception, and perceptual training. Recent research projects include image annotation for AI using eye tracking, human factors engineering, and developing simulation-based perceptual training methods to facilitate radiology education.
Health challenges represent one of the long-standing issues in the Arab region that hinder its ability to develop. Prevalence of diseases such as cardiovascular diseases, liver cirrhosis and cancer among many others has contributed to the deteriorated health status across the region leading to lower life expectancy compared to other regions. For instance, the average life expectancy in the Arab world is approximately 70 years, which is at least 10 years lower than most high-income countries.2 Among many directions of healthcare development across the region, biomedical computing research represents one main arm of tackling health challenges. Advances in computational technologies have enabled the emergence of biomedical computing as one of the most influential research areas worldwide.
Medical knowledge graphs (KGs) constructed from Electronic Medical Records (EMR) contain abundant information about patients and medical entities. The utilization of KG embedding models on these data has proven to be efficient for different medical tasks. However, existing models do not properly incorporate patient demographics and most of them ignore the probabilistic features of the medical KG. In this paper, we propose DARLING (Demographic Aware pRobabiListic medIcal kNowledge embeddinG), a demographic-aware medical KG embedding framework that explicitly incorporates demographics in the medical entities space by associating patient demographics with a corresponding hyperplane. Our framework leverages the probabilistic features within the medical entities for learning their representations through demographic guidance. We evaluate DARLING through link prediction for treatments and medicines, on a medical KG constructed from EMR data, and illustrate its superior performance compared to existing KG embedding models.
Are you searching for the best artificial intelligence startup companies in Switzerland that can help with data security and cybercrime? Artificial intelligence helps businesses identify bugs and unusual user behavior in enterprise systems like ERP and economic institutions. As a result, by integrating artificial intelligence solutions into your company, you can protect your data and prevent cyberattacks. Sophia Genetics offers a platform for optimizing genomic research based on artificial intelligence (AI). The platform processes and analyzes genomic data from patient DNA sequence data produced by the NGS platform using machine learning algorithms.
Personal health libraries (PHLs) provide a single point of secure access to patients digital health data and enable the integration of knowledge stored in their digital health profiles with other sources of global knowledge. PHLs can help empower caregivers and health care providers to make informed decisions about patients health by understanding medical events in the context of their lives. This paper reports the implementation of a mobile health digital intervention that incorporates both digital health data stored in patients PHLs and other sources of contextual knowledge to deliver tailored recommendations for improving self-care behaviors in diabetic adults. We conducted a thematic assessment of patient functional and nonfunctional requirements that are missing from current EHRs based on evidence from the literature. We used the results to identify the technologies needed to address those requirements. We describe the technological infrastructures used to construct, manage, and integrate the types of knowledge stored in the PHL. We leverage the Social Linked Data (Solid) platform to design a fully decentralized and privacy-aware platform that supports interoperability and care integration. We provided an initial prototype design of a PHL and drafted a use case scenario that involves four actors to demonstrate how the proposed prototype can be used to address user requirements, including the construction and management of the PHL and its utilization for developing a mobile app that queries the knowledge stored and integrated into the PHL in a private and fully decentralized manner to provide better recommendations. The proposed PHL helps patients and their caregivers take a central role in making decisions regarding their health and equips their health care providers with informatics tools that support the collection and interpretation of the collected knowledge.
Opioid Use Disorder (OUD) is a public health crisis costing the US billions of dollars annually in healthcare, lost workplace productivity, and crime. Analyzing longitudinal healthcare data is critical in addressing many real-world problems in healthcare. Leveraging the real-world longitudinal healthcare data, we propose a novel multi-stream transformer model called MUPOD for OUD prediction. MUPOD is designed to simultaneously analyze multiple types of healthcare data streams, such as medications and diagnoses, by finding the attentions within and across these data streams. Our model tested on the data from 392,492 patients with long-term back pain problems showed significantly better performance than the traditional models and recently developed deep learning models.
Imagine getting medical checkups, drugs and other therapies tailored to your own genetic makeup. The concept of personalized medicine is based on the idea that medical treatment should not be one size fits all. But this requires analysis of enormous amounts of data, something only a supercomputer can do efficiently. In Japan, scientists are harnessing the world's most powerful supercomputer, Fugaku, to discover new customized treatments and drug therapies. Kamada Mayumi is a researcher in Kyoto University's Graduate School of Medicine.