Computational Biology Facility (CBF), University of Liverpool’s Post

📣 📣 🔊 DEADLINE EXTENDED!!! - 16th July ❔ Would like to exploit Machine Learning to develop a new biomarker? ❓ Interested in multi-omics integration and elucidation of mechanisms? ❔ Want to improve data integration processes and statistical frameworks? ⁉ How to exploit large language models to improve healthcare? ❔ working with omics and struggling with batches, missing values and reproducibility? Do you want a hand? If you work in #industry and want help with your #datascience or #bioinformatics problems, this is your gig. You can submit a question and we will do our best to select a team of specialist academics and research technical professionals to help you shape a plan on how to address your problem. If your question is selected you would also have access to funding to collaborate together. Join us! #healthcare #datascience #bioinformatics #freesupport #bettertogether #biomedicine #omics #dataanalysis

How maths and AI are fighting health inequalities Luke Natali, Advisor at Health Innovation East, discusses how maths and AI are helping to ensure resources are available, where they can have the biggest impact, to tackle health inequalities. Read more in our latest blog: https://lnkd.in/dtxRMpR2

📄 This study demonstrated the efficacy of machine learning (ML) techniques in predicting the birthweight range of infants, an essential factor in ensuring safe deliveries and minimizing health risks for both mother and baby. Implementing these predictive models can enhance the decision-making process, reducing the likelihood of unnecessary medical interventions and improving overall maternal and fetal health outcomes. #machinelearning; #sustainableapproach; #birthweightrange; #classification; #fetalweight "Birthweight Range Prediction and Classification: A Machine Learning-Based Sustainable Approach" By Dr.Dina Alabbad, Shahad Alotaibi, Raghad Alotaibi, Noura Alsaqer, Rahaf A. Alqahtani, Noor Felemban, Dr. Atta ur Rahman, Dr. Sumayh Aljameel, Dr B Mohammed Imran and Mustafa Youldash 🔗 https://lnkd.in/gz4eWCxe

🌐 Navigating the Future of AI in Healthcare: Key Insights from the RAISE HEALTH Symposium 🌐 As #AI in #healthcare evolves at a rapid pace, the imperative to move swiftly yet thoughtfully has never been clearer. We're at a crucial juncture where #academia, #policy, and #industry must align rapidly to ensure that AI innovations are both #transformative, #safe and #equitably beneficial. I recently virtually participated in the RAISE HEALTH Symposium, co-hosted by Stanford University School of Medicine and Stanford Institute for Human-Centered Artificial Intelligence (HAI), which convened over 5,000 experts, innovators, stakeholders, thought leaders globally to discuss the current state of AI in healthcare. 📌 Symposium Highlights: - #Ethical Groundwork in AI: Enlightening #perspectives from Michael Howell, MD, and Lisa Lehmann, MD, PhD, moderated by Euan Ashley, MD, PhD, on integrating ethics deeply into AI development (safety is top of mind). - #Strategic #Collaborations & #Partnerships: @Laura Adams and @Ken Harris discussed enhancing global health through robust public-private partnerships, further elaborated by Nigam Shah, PhD. - #Equity and #Accessibility: David Magnus, PhD, Natalie Pageler, MD, and Randall Rutta offered critical views on AI’s role in addressing or exacerbating healthcare disparities. AI could either bridge or widen health disparities - the panel advocated for technologies that are accessible to all. - #Real-World #Applications: Jesse Ehrenfeld MD MPH, MD, MPH shared tangible #challenges and #successes in AI deployment, providing a roadmap for future implementations. (Include patients at the onset of the planning and engage them throughout the process and beyond). This symposium reinforced the need for a #proactive approach to ensure AI is #responsibly woven into the fabric of #healthcare. The current journey is a sprint, and our collective engagement is crucial for the duration of the ironman triathlon. 🔗 Stay at the forefront of this #transformation—subscribe to the RAISE Health newsletter for ongoing insights and developments. Join me in this discussion. How do you see AI shaping the future of healthcare? What steps should we (patients, consumers, professionals) take to ensure these advancements benefit all? #leadership #RAISEHealth #StanfordMedicine #HealthcareInnovation #AIHealthcare #PatientCenteredCare #ResponsibleAI #TechForGood #WomensHealth Stanford University School of Medicine Stanford Health Care https://lnkd.in/dEjtkbE9

a2 Collective #awardee University of Massachusetts Amherst is developing an advanced AI project titled "Chronic Pain Monitoring and Assessment for LTC Residents with ADRD by AI Sensing." This #cohort3 initiative focuses on using #AI and #ComputerVision to enhance the quality of care for long-term care residents with Alzheimer's and related dementias (#ADRD). Led by esteemed PIs Xian Du, PhD, Joohyun Chung, PhD, MStat, RN, and Shishir Prasad, PhD, the project aims to monitor and assess chronic pain in LTC residents through innovative technologies, including environmental sensors, wearables, and smartphone hardware. By addressing cognition, chronic/comorbid conditions, and treatment and care, the project seeks to improve outcomes for individuals and caregivers in long-term care settings. Partnering with #massaitc, this project underscores the power of interdisciplinary research in addressing the complexities of aging and dementia care. CC: College of Engineering, UMass Amherst MassAITC University of Massachusetts Amherst Brigham and Women's Hospital Massachusetts General Hospital Brandeis University a2 Collective | a2PilotAwards.ai National Institute on Aging (NIA) Post Link: https://lnkd.in/gHVuXTE2

Today, I'm speaking at the #WTT4HC organised by the ITTF Foundation. My speech today is not about #STATON. It's about the use of objective tools in the evaluation of therapies, and specially in #TableTennis. So far, any therapy, pharmacological or not, depends on the evaluation of subjective questionnaires or diaries, which leads to biased outcomes. There is a lot of literature about the really low correlation between diaries and medical opinion, or the high variability inter-intra rater in questionnaires. The use of objective tools is necessary if we want a better health system. The whole community in clinical studies, including physicians and medication agencies, should take a step forward and eventually jump into the XXI century and adopt great technologies that have been recommended by important institutes such as NICE - National Institute for Health and Care Excellence, among others. Register on the following link if you want to hear my presentation at 14:15! https://lnkd.in/eJnJWV33 Sense4Care #realscience #innovation #digitalhealth #medtech #AI #healthtech #technology #neurotech #neurotechnology #medicaldevices #research #healthcare #TableTennis #parkinson #parkinsonsdisease #clinicalstudies #objectivemeasurements Chiara Capra Joan Calvet Joan Cabestany andreu catala mallofre Léa Huré

📚🌟 Thrilled to share that our research on Obesity detection using machine learning has been published in IEEE! 🎉 This milestone represents the culmination of months of dedicated effort and collaboration with an incredible team. Together, we've explored innovative approaches to leveraging machine learning for early detection and intervention in obesity. I extend my heartfelt thanks to Ashfaque Khan for their unwavering support and expertise throughout this journey. Their insights have been instrumental in shaping our findings and contributing to the advancement of healthcare technology. I'd like to express my gratitude to my co-authors Shraddha Borase , Umesh Patel , Vaishnavi Patil for their invaluable contributions and collaboration. Our paper dives into The system leverages patient data including demographics, medical history, lifestyle habits, and potentially physiological data. By training ML algorithms on labeled datasets, the system can learn to identify patterns associated with obesity. It underscores the potential of machine learning to transform healthcare by enabling proactive and personalized interventions. 🔗 For those interested in delving deeper, you can access the full paper here:https://lnkd.in/dcDDZUNa I am honored to have this work recognized by IEEE, a beacon of excellence in our field. This achievement motivates us to continue pushing boundaries and innovating for a healthier future. #IEEE #PublishedPaper #MachineLearning #ObesityDetection #HealthcareInnovation #Research #Teamwork #Gratitude

Transformative technologies like #AI and #synthetic_biology could take health sciences research to the next level, leading to breakthroughs in public health and medicine. But what can funders, researchers, and policymakers do to ensure these innovative technologies are developed and used to unlock knowledge in ways that are equitable and ultimately benefit everyone? The National Academy of Medicine’s report “Toward Equitable Innovation in Health and Medicine” helps answer that question, serving as a framework for continuously orienting our future health sciences knowledge system towards health equity. I found the report’s eight dimensions of equity super useful in thinking through how equity considerations can arise across all stages of the innovation life cycle (see NAM’s image below for details) and look forward to implementing some of these concepts in my work at RWJF. For those of us who are #funders, here are a few of my favorite recommendations shared in the NAM framework: 🙌Support ongoing efforts to broaden views on who innovators are and where innovation occurs  🥇Prioritize investment in technologies that focus on access and usefulness for populations who have been historically underserved, overlooked, or marginalized  📝Fund alternative designs and delivery systems that might better or more proactively address any inequities and barriers to access that have been identified  🤝Adopt and require best practices for community engagement in research conception, codesign, and implementation #Innovation #Research #Equity 

#Telehealth and #Medicine Today (THMT) peer reviewed journal is pleased to announce the article titled "#Telehealth in Medicine: Predictions 2023–2024," published in Volume 8, 2023, has been selected for the THMT #BestEditorial Award. DOI: https://lnkd.in/egB-fNAx This recognition is a testament to the significance in sharing near term views and prognostication for #digitalhealth advances, market opportunities, and gaps in #healthcare. The selection criteria for THMT Annual Editors' Best Editorial is based on several factors, including online views, downloads, citations, and the editors' assessments of market impact in the industry. ***Congratulations to authors on this achievement!*** > Dr. Jiang L., Vivalink, USA > Prof. Dr. Ingrid Vasiliu-Feltes-Feltes, University of Miami, USA > Kathleen McGrow, DNP, MS, RN, PMP, FHIMSS, FAAN, Microsoft Health & Life Sciences, USA > Brendan Smith, The MITRE Corporation, USA > Paul Barach, B.Med.Sci, MD, MPH, Maj (Ret.), AUA, Pegwin, USA > Sweta Sneha, PhD, Kennesaw University, USA > Francis Campion, MD, FACP X. Campion, The MITRE Corporation, USA Submit your #orignal #research to THMT at https://lnkd.in/egEpQJJ6 #scholarlyjournals #peerreviewed #scienceandtechnology #datascience #digitaltwins #artificialintelligence #futureofhealth #techinnovation #healthinnovation #telehealth #predictions #telemedicine #virualcare #hospitaltohome #AI #healthcaretransformation

Two years ago, with Hossein Zahedi Nezhad we collaborated on a lab project of ICT for health that applied advanced linear regression techniques to predict the severity of Parkinson's disease. Parkinson’s is a progressive neurological disorder with symptoms that can significantly impact a person’s life, from tremors to cognitive challenges. Our project focused on using voice parameters to predict the Unified Parkinson’s Disease Rating Scale (UPDRS) scores, an essential measure of disease severity. By analyzing voice data from patients, we aimed to create a predictive model to improve the monitoring and management of the disease. We explored and compared various regression algorithms, such as the Steepest Descent and Local Linear Steepest Descent methods. The findings were enlightening, with the Local Steepest Descent method showing promising accuracy and correlation with actual UPDRS scores. This project was a great learning experience, deepening our understanding of how data-driven approaches can make a significant impact on healthcare, particularly in improving outcomes for Parkinson’s patients. #DataScience #MachineLearning #Healthcare #ParkinsonsDisease #RegressionAnalysis #VoiceData #HealthTech #polito