Lily Clifford

In this week's blog post, Data Science Fellow Sharon H. Green, PhD, MPH writes about how to create powerful visualizations for propensity score matching in R. If you're new to causal inference and trying to learn what tools are available to you, this is a great blog post to check out! Subscribe to our Medium for weekly blog posts on all topics data science, social science, and social justice. https://lnkd.in/epgPtduB

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DEADLINE APPROACHING! 🌟 Are you headed to EDM 2024 in Atlanta? Consider submitting to the workshop on Causal Inference in Educational Data Mining (EDM) research! Join SEERNet members Neil Heffernan and Avery Closser as they explore the critical role of causality in enhancing student learning through intelligent tutoring systems, educational games, MOOCs, and more. In today's digital learning landscape, understanding causality goes beyond predicting learner behaviors to unraveling the underlying factors impacting educational outcomes. The workshop aims to stimulate discussion and share insights on examining causality within EDM contexts and leveraging EDM to support causal inference methodologies. 📝 Call for Papers/Presentations: To participate, please submit on topics including A/B testing, Bayesian networks, analyzing data from randomized experiments, multi-armed bandits, causal mechanism analysis, and more. Whether you're presenting completed work or exploring open problems in the field, you are invited to contribute to the conversation! - Submission Deadline: June 10, 2024 [Link: https://bit.ly/3WfbSC2]

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Sign up now for our Introduction to Causal Inference course, where Mulusew J. Gerbaba Associate Research Scientist – Impact Evaluation at APHRC will dive into the methods of causal inference using observational data. You'll explore topics like potential outcomes, DAGs and causal graphs, target trial emulation, matching techniques, managing confounding and bias, causal structure learning, and quasi-experimental methods. Don't miss this opportunity to acquaint yourself with modern causal modeling methodology. Register today! https://lnkd.in/dcMnHVzX #MentalHealthDataPrizeAfrica #MentalHealthAwareness #DataForChange

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Stay up to date with the latest research in Measurement, Evaluation, Statistics, & Assessment with the latest ESM MAD Blog! #ELPS Check out our latest blog by Austin Boyd on how to critically consume quantitative research here: https://lnkd.in/gh8NaDVW

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Geographical joinpoint regression provides a finer modeling of temporal trends. Analyzing temporal trends outside a spatial framework is unsatisfactory because significant variation, even within a single State, is not accounted for. Vesta is the only software that allows exploring how characteristics of time series vary geographically. Learn more and claim your free trial: https://lnkd.in/garHyyXV

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Commonly used terms in #data preprocessing prior to machine learning are rescaling, normalizing, and standardizing.  These terms are used more or less interchangeably depending on the customs within various fields. 'Normalizing' a vector most often means dividing by a norm of the vector, for example, to make the Euclidean length of the vector equal to one (unit norm). 'Normalizing' also refers to rescaling by the range of the vector (minimum & maximum), to make all the elements lie between 0 and 1. ⭐ https://lnkd.in/dsXS_sey 'Rescaling' a vector means adding or subtracting a constant, then multiplying or dividing by a constant, as one would do to change the units of measurement of the #data. 'Rescaling' also refers to scaling with median and quantiles, which is called robust scaling (rescaled #data becomes robust to outliers). ⭐ https://lnkd.in/deSb2yb8 'Standardizing' a vector most often means subtracting a measure of location and dividing by a measure of scale. For example, if the vector contains random values (with Gaussian distribution), one subtracts the mean and divides by the standard deviation, thereby obtaining a "standard normal" random variable with zero mean and unity standard deviation. ⭐ https://lnkd.in/dEpyrsGx And these essential-to-know feature scaling methods have been nicely elaborated by Soledad Galli in one of her blogs at Train in Data. Thanks to her! Other methods (apart from above) for #data preprocessing with #scikit-learn are available ➡ https://lnkd.in/dtnbhpbt ✅ https://lnkd.in/dfM8W3mQ

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Social network analysis is now a core component of computational social science. D-Lab Data Science Fellow Christian Caballero introduces concepts of network analysis in this week's D-Lab Blog Post. Check out his post to gain a foundational understanding of networks and how they can be used for social science research: https://lnkd.in/g5vk9uks

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Synthetic Data –an Overview, a Taxonomy & some FAQs - the title of my latest blog https://lnkd.in/exWfryJS One of my key points, based on recent research by #NewMR is that whatever your views on Synthetic Data, you are in a minority. The current range of views encompasses lots of different views and none of them represents a majority view. I think this means we all need to be a little but cautious in our pronouncements and recommendations. I hope you find my post useful and I'd love to hear your take on it? Judith Passingham, Dharmendra Jain, Agus Elissondo, Colin Strong, Christian Walsh, Barry Jennings, Sue York, Karine Pepin, Alexandra Kuzmina, Ben Leet, StJohn "Singe" Deakins, Yogesh Chavda, Hans Zijlstra, Sidi Lemine, Jonathan (JD) Deitch, Adriana R., Frank Pica, Febronia Ruocco, Scott Swigart, Jerome Sopocko, Paige Schoenfeld, Jean-Marc Leger, Leonardo Valente, Gabriela Kusters, Germaine Reyes, Ravinder Roopra, Melanie Courtright, Renee Smith, Kathy Joe, Elise Misse, Lisa Salas

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Join Hasham Ul Haq, our Senior Machine Learning Engineer presenting ‘Running State-of-the-art #MedicalLLMs on Databricks’ at the Data + AI Databricks Summit on June 12, 2024, at 2:50 pm PDT. Healthcare-specific #largelanguagemodels provide superior accuracy to general-purpose LLMs. This session shares current benchmarks of John Snow Labs’ Medical Large Language Models in three areas – question answering, information extraction, and chat quality – as evaluated on a combination of widely used medical LLM benchmarks and a blind evaluation by human doctors. Then, the recommended architecture for running these models on Databricks will be shared, in a way that complies with the strict security and privacy requirements of healthcare, while still enabling a fully managed, scalable, and highly performant environment.   Book a meeting with our Team: https://lnkd.in/dD_RBEtr #Databricks #LLMs #HealthcareLLMs #nocode #GenerativeAI #HealthcareAI #MedicalLLMs #nlp #naturallanguageprocessing #DataScience #DataEngineering #DataAnalysis #DataInsights #TextProcessing #DeepLearning #DataScience

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Check out this Neo4j Podcast episode where we talk about the future of graph data science & other fun topics! #neo4j #graphdatascience #datascience #AI https://lnkd.in/e3nTQitA

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👉🏼 RDguru: A Conversational Intelligent Agent for Rare Diseases 🤓 Jian Yang 👇🏻 https://lnkd.in/eSCm_MkQ 🔍 Focus on data insights: - 📈 RDguru outperforms ChatGPT in generating authoritative descriptions, highlighting its enhanced accuracy in knowledge Q&A tasks. - 🔍 A novel multi-source fusion diagnostic model shows a significant 5.47% improvement in diagnostic recall compared to PheLR. - 🧬 The use of advanced algorithms facilitates more effective automated phenotype annotation and differential diagnosis in rare diseases. 💡 Main outcomes and implications: - 🏥 RDguru demonstrates the potential to significantly improve clinical decision-making processes in rare disease diagnostics. - 💬 The conversational agent can enhance patient interactions through evidence-based Q&A, leading to better-informed medical consultations. - ⚙️ Integration of multi-source diagnostic strategies could pave the way for the development of more robust AI tools in healthcare settings. 📚 Field significance: - 🚀 This study positions RDguru as a promising solution for addressing the challenges of diagnosing rare diseases, particularly given the limitations of traditional methods. - 🌍 The findings may inspire further research into the implementation of LLMs in clinical environments, promoting wider acceptance and use of AI technologies in healthcare. - 🔒 Ensuring accuracy and reliability in AI outputs is crucial for safeguarding patient safety and enhancing trust in AI applications within medicine. 🗄️: [#RareDiseases #AIAssistedDiagnosis #ClinicalDecisionSupport #MachineLearning #HealthTech #NaturalLanguageProcessing #DiagnosticAccuracy]

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👉🏼 Does using artificial intelligence take the person out of personal statements? We can't tell 🤓 Jenna N Whitrock 👇🏻 https://lnkd.in/eQ-PpF7U 🔍 Focus on data insights: - 📊 The ability to identify AI-generated personal statements by residency reviewers was only 59% accurate. - 🤔 Only 10% of participants correctly identified all AI-generated statements, indicating a significant challenge in detection. - 🏆 Participants rated AI-generated statements as "best" 60% of the time, suggesting that these statements are perceived favorably despite being machine-generated. 💡 Main outcomes and implications: - 🚫 A majority

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As simple as they are, confusion matrices are an essential tool for building and training a classification model with imbalanced datasets. #datascience #AI #artificialintelligence #opensource https://hubs.li/Q02WQFY70 If you enjoyed this post, follow me for more insights on AI and data science!

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At ODSC East, Dr. Andre Franca addressed the shortfall of prediction-focused AI in a thorough talk about casual AI that outlined key principles that should guide data scientists’ approach to causality. #AI #datascience #artificialintelligence https://hubs.li/Q02C17Xv0

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👉🏼 Evaluation of Rhinoplasty Information from ChatGPT, Gemini, and Claude for Readability and Accuracy 🤓 Monica K Rossi Meyer 👇🏻 https://lnkd.in/e-6j2hpj 🔍 Focus on data insights: - 📊 ChatGPT demonstrated superior accuracy and overall quality in responses compared to its counterparts Gemini and Claude. - ✍️ All three chatbots provided responses rated as neutral to incomplete, indicating a gap in exhaustive information dissemination. - 🏷️ Use of medical jargon was prevalent across all platforms, with readability scores assessed at a college level. 💡 Main outcomes and implications: - ⚖️ Surgeons must critically evaluate the information provided by chatbot platforms due to potential incompleteness and accuracy issues. - 📚 There is an opportunity for improving chatbot technology through training on evidence-based medical recommendations to enhance educational effectiveness. - 🧩 The findings highlight a need for continuous monitoring and development of AI tools in healthcare to ensure reliable patient education resources. 📚 Field significance: - 🔬 The study underscores the importance of accurate medical information accessibility in the age of AI and digital resources. - 📋 It prompts further exploration into integrating AI into medical education while maintaining rigorous quality control standard practices. - 🛠️ The technology's potential for contribution in education suggests innovative pathways for enhancing surgeon-patient communication through AI-enabled platforms. 🗄️: [#rhinoplasty #AIinHealthcare #medicaleducation #ChatGPT #Gemini #Claude #readability #accuracy #patienteducation #digitalhealth]

We're excited to announce that the Causal Pathways Resource Hub is now live at Better Evaluation. Causal Map is now included as a tool for causal mapping. 🗺 The hub is more than just a collection of resources—it's been a fascinating journey negotiating common goals to approaching complex evaluations. Our Causal Map app is featured as a tool for causal mapping - a method that visualises and analyses the intricate web of factors influencing outcomes in complex scenarios. This highlights the growing importance of causal analysis in evidence-based decision-making. Check our recent blog post to know more about the hub: https://lnkd.in/e2Gt623b Explore the Causal Pathways Resource Hub to discover different ways to make causal thinking a bigger part of your evaluation processes and join the community: https://lnkd.in/gmzrPtU6 #CausalMapping #Evaluation #ImpactAnalysis #CausalPathways #AIinEvaluation #EvidenceBasedDecisionMaking

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