Ricardo Valentim’s Post

Healthcare Applications: 🖥 😷 🏥 💊 Predictive Modeling, Computational Phenotyping, and Patient Similarity! In the realm of healthcare, cutting-edge applications are revolutionizing the way we approach patient care and decision-making. Let's delve into three transformative concepts: Predictive Modeling, Computational Phenotyping, and Patient Similarity. 1️⃣ Predictive Modeling Harnessing historical data to create models that predict future outcomes is a game-changer. However, with vast amounts of data and numerous modeling options, the challenges are evident. From constructing cohorts to selecting features and performing classification, navigating through predictive modeling pipelines requires meticulous attention to detail and expertise. 2️⃣ Computational Phenotyping Converting complex electronic health records into meaningful clinical concepts is vital for extracting actionable insights. From demographic data to clinical notes, the journey involves transforming raw data into medical concepts or phenotypes. Yet, amidst this process, challenges like missing data, duplicates, and irrelevant information must be addressed to ensure the accuracy and relevance of the derived phenotypes. 3️⃣ Patient Similarity Leveraging computer algorithms to analyze a patient's medical data and identify similar cases within a vast database holds immense promise. When a patient seeks medical advice, the physician conducts an examination, initiating a similarity search through the database. The physician then refines the results, identifying truly similar patients within the clinical context. This enables the grouping of patients based on treatments and outcomes, facilitating the recommendation of the most effective treatment to the current patient. #HealthcareInnovation #PredictiveModeling #Phenotyping #PatientSimilarity #GT #CSE6250 #BigDataforHealthcare #LectureSummary

Foundation models for healthcare, or healthcare foundation models, are frameworks upon which advanced healthcare systems and technologies are built. They serve as the basis for developing innovative solutions and strategies to improve patient care, streamline #healthcareoperations, and enhance overall health outcomes. Healthcare foundation models (#HFM) have emerged as powerful tools across various domains within healthcare #AI, including language, #vision, bioinformatics, and #multimodality. ✴ Models, such as the Language Foundation Model (#LFM) and Vision Foundation Model (#VFM), have demonstrated exceptional capabilities in processing medical text, analyzing medical images, and deciphering biological data. ✴ By leveraging large-scale #medicallanguage data, LFMs have significantly enhanced medical text processing and dialogue tasks. VFMs have shown promising potential in various medical imaging scenarios, adapting to different modalities and organ-specific tasks with remarkable performance. ✴ Bioinformatics Foundation Models (BFM) have revolutionized research in areas like protein sequences and genetic data, unlocking insights into the complexities of life. ✴ Multimodal Foundation Models (#MFM) integrate information from diverse modalities, enabling them to interpret various #medicaldata types and perform modality-dependent tasks effectively. Overall, these foundation models provide a robust framework for addressing complex clinical challenges, driving efficiency and effectiveness in healthcare practices, thus propelling advancements in the healthcare field.  However the current lack of development in data, #algorithms, and computing infrastructures is still the root of various challenges in HFMs. The ethics, diversity, heterogeneity, and cost of healthcare data make it extremely challenging to construct a large enough dataset to train a generalizable HFM in wide healthcare practices. The demand of adaptability, capacity, reliability, and responsibility in #AIalgorithms further makes it difficult to be applied to real. This paper first presents a comprehensive overview and analysis of #HFMs, including the methods, data, and applications that help to understand the current progress of HFM. It also gives a glimpse of the future directions in role, implementation, application, and emphasis, highlighting the future perspectives that hold promise for advancing the field. Source: Foundation Model for Advancing Healthcare: Challenges, Opportunities and Future Directions Link: https://lnkd.in/edX2Ru8Q #healthcarefoundationmodels #aihealth #visionfoundationmodel #languagefoundationmodel #Bioinformaticsfoundationmodels

**The Role of Data Science in Revolutionizing Healthcare** In recent years, data science has emerged as a transformative force in healthcare, reshaping how medical professionals diagnose, treat, and manage diseases. By leveraging vast amounts of data, sophisticated algorithms, and advanced computational techniques, data science is paving the way for more personalized, efficient, and effective healthcare solutions. **1. Enhancing Diagnostic Accuracy** Data science is significantly improving diagnostic accuracy through machine learning models that analyze medical images, patient records, and genetic information. For instance, algorithms trained on thousands of medical images can now detect anomalies such as tumors or fractures with remarkable precision. These models assist radiologists by highlighting potential issues, thus reducing human error and speeding up the diagnostic process. **2. Personalizing Treatment Plans** Personalized medicine is one of the most promising applications of data science. By analyzing patient data, including genetic profiles, lifestyle factors, and past medical history, data scientists can help create tailored treatment plans. This approach enhances the effectiveness of treatments and minimizes adverse reactions by considering the unique characteristics of each patient. **3. Predicting Disease Outbreaks** Data science is crucial in epidemiology for predicting and tracking disease outbreaks. By analyzing patterns in health data, social media trends, and environmental factors, predictive models can forecast potential outbreaks and spread patterns. This capability enables timely interventions and better preparedness for public health crises. Despite its benefits, the integration of data science in healthcare faces challenges such as data privacy concerns, the need for high-quality data, and the integration of new technologies with existing systems. Addressing these issues requires ongoing research, robust data governance policies, and collaboration between technology developers and healthcare providers. Looking ahead, the future of data science in healthcare is promising. Advancements in artificial intelligence, real-time data processing, and integration of various data sources will likely drive further innovations. As data science continues to evolve, it holds the potential to make healthcare more predictive, preventive, and personalized, ultimately leading to better health outcomes and a more efficient healthcare system. #snsinstitution #snsdesignthinking #snsdesignthinker

GenAI is already transforming our health. 3 ways it’s already changing the healthcare industry… 70% of healthcare leaders are already testing or using GenAI. They're starting small — most are still in the proof-of-concept stages. But the impact is undeniable. Here are 3 ways GenAI is transforming healthcare: 1/ Medical imaging analysis: ↳ Creates synthetic medical images – mimicking X-rays, MRIs, & CT scans 2/ Personalized treatment plans: ↳ Analyzes medical history, genetics, and real-time data to create customized treatments 3/ Administrative efficiency: ↳ Assists with clinical documentation and operational tasks GenAI has already begun to have a massive impact on healthcare. It will be vital in every industry soon. The key is adopting it in the right way. Is your organization ready? Enjoy this? Follow Aleksandr Sheremeta for tips on GenAI adoption and data engineering!

DATA IS THE NEW MEDICINE The integration of structured and unstructured medical data, along with the inclusion of genomic and microbiomic information, is pivotal in transforming healthcare. Structured data, such as blood pressure readings or lab results, are easily searchable and analyzable. Unstructured data, which includes doctor's notes and imaging reports, provides a depth of context and nuance that structured data cannot. Combining these data types enables a comprehensive understanding of a patient's health. The addition of genomic and microbiomic data into medical records introduces a new frontier in personalized medicine. Genomic data can predict susceptibility to certain diseases and influence drug metabolism, while microbiomic information can offer insights into immune system function and chronic disease risk. This level of personalization ensures that treatments are not just effective, but also highly tailored to the individual. The implementation of medical Large Language Models (LLMs) like Medpalm2 further enhances the potential of integrated health data. These models can analyze vast datasets, identifying patterns and connections that would be impossible for humans to find. They can assist in diagnostics, predict outcomes, and suggest treatments, thereby increasing the accuracy and efficacy of medical care. The ultimate goal is a digital Universal Healthcare platform that leverages this integrated, comprehensive data and advanced analytical tools. Such a platform can democratize access to high-quality healthcare, making it affordable and accessible to all. By streamlining diagnostics, personalizing treatments, and predicting health trends, we can save lives and significantly reduce healthcare costs. This vision for healthcare represents a shift towards proactive and preventative care, powered by the latest in data science and artificial intelligence. #uhc #healthcareai #healthcaredata #healthforall Ted Herbosa Beverly Lorraine Ho David Almirol

Data science is revolutionizing healthcare in ways we could only imagine a few years ago. From predictive models that anticipate patient outcomes to personalized treatment plans tailored to individual needs, the integration of data analytics is reshaping patient care and operational efficiency. A powerful example of this is in early diagnostics—recent studies have shown that artificial intelligence and healthcare data science, particularly deep learning models, can detect COVID-19 in chest scans and even differentiate it from other types of pneumonia. This breakthrough offers a level of diagnostic precision that doctors alone couldn’t achieve, helping speed up diagnosis and potentially improving patient outcomes. It’s an exciting time for anyone working at the intersection of technology and health! 

Happy to share our new preprint, "Sample Selection Bias in Machine Learning for Healthcare." Sample Selection Bias (SSB) occurs when the study population is less representative of the target population, leading to biased and potentially harmful decisions for underrepresented subpopulations. In our work, we propose a new approach to address SSB in healthcare. Instead of relying on traditional bias correction techniques, we focus on identifying the target population by modeling the differences between the study and target populations. Our proposed techniques demonstrate robustness across various settings, including different dataset sizes, event rates, and selection rates, outperforming existing bias correction techniques. Read more here: https://lnkd.in/gAnabbBt A big thank you to my collaborators, Lei Clifton, Achille Salaün, Huiqi Yvonne Lu, Kim Branson, Patrick Schwab, Dr Gaurav Bhaskar Nigam and Prof. David Clifton, for their invaluable contributions. #MachineLearning #HealthcareAI #BiasCorrection #DataScience #Research

The main goal of machine learning models in healthcare is to accurately estimate patient risk. For a model to be practical, it should not only have good discriminative performance but also be well-calibrated so that the predicted probabilities are meaningful and interpretable. So model development starts by defining the right probability to be estimated. When making predictions for patients, we often encounter outcomes that depend on other conditions. For example, we may want to predict the quality of life two years after survivorship, but this measurement is conditional on the patient surviving for those two years. Traditionally, patients who did not survive are excluded from the model development, which can introduce bias. In the #BD4QoL project, we developed a methodology based on conformal predictions that includes all patients in the model development process, making the algorithm less biased and more fair. This approach allows us to predict not only the probability of having a poor quality of life in the future but also the probability of surviving, and the joint probability of experiencing any adverse event for all patients. This new method can be valuable for informing patients, clinicians, and hospital management. By knowing which patients will need help to improve their quality of life or require other specialised care, resources can be allocated more efficiently. This result was only made possible by the joint effort of partners who shared their valuable data across borders, including Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Johannes Gutenberg University Mainz and University of Bristol, for researchers at the University of Oslo and University of Deusto. The peer-reviewed publication will soon be available to the whole community.

🚀 Unlocking Healthcare's Future with Synthetic Data! 🚀 🔍 Privacy-Preserving Data Sharing Synthetic data holds massive promise for healthcare R&D by providing a privacy-first approach to data sharing. Unlike other privacy-enhancing techniques like federated learning, synthetic data can act as a seamless stand-in for real data, enabling broader applications without modification. 🧠 Streamlining Clinical Model Development In our recent work, we explored synthetic data's potential in clinical model development by creating a synthetic version of ever-smokers in the UK Biobank. Using cutting-edge privacy-preserving generators, we built prognostic models for lung cancer—proving that synthetic data can be used effectively across the medical modeling pipeline, even when the real data is inaccessible. 🏥 Impact on Healthcare Deployment Our findings highlight that synthetic data, under various data release strategies, could significantly benefit biobank data sharing in healthcare, fueling advancements in diagnostics and personalized treatments. 🔗 Synthetic data isn’t just a placeholder—it’s an empowering tool for transforming healthcare while safeguarding patient privacy. #HealthcareInnovation #SyntheticData #PrivacyPreserving #DataSharing #ClinicalModels #BiobankData #AIinHealthcare #LungCancerResearch #FutureOfHealthcare https://lnkd.in/eWHbMeE4

In healthcare, access to data is tricky. Especially since we’re focused on finding the rare and the abnormal. So we use data augmentation. This field has developed so much that older techniques are now called traditional data augmentation 🫠 To keep abreast of the advancements and exchange opinions of the real value of these (and other) techniques, Data Scientists at Quibim get together every month or so. One presents a paper and everyone else shares their thoughts. Last week Gemma delivered a super insightful presentation on LesionMix 🤩 Normally, you apply transformations to the whole image. With LesionMix, the transformations are specifically applied to, you guessed it, lesions. In medical imaging you see lesions of all shapes and sizes and their segmentation is a critical step in most radiology workflows. Increasing the diversity of lesion shape, location, intensity and load distribution in our dataset can lead to improved performance. In the advent of #GenAI, we found it really refreshing that this methodology uses a non-deep learning based approach. Like Arnau sharply pointed out during our discussion, we can fall into a loop of using #DL for everything even if it’s not the best approach. And in a field that spits out a new paper every other minute, we need to actively avoid a pro-innovation bias and not see everything that glitters as gold. The newest technology is not always the best technology which is what we’re always aiming for 🎯