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PhysChem property prediction modules in Percepta offer: 🏆 Deeper insights 🏆 Trainability with experimental data 🏆 Expandability to third-party models — all in a single, easy-to-use suite. Learn more ⬇️

We want the machines to understand what we need and ready to hgive us the output in a domain so that we could find a lot of insights in the medical research It should be a. Twoway communication building trust in the system Verify for deciding Diagnostic or forensic analysis Counter fact, reason New medical insights

(( TAKING ON THE MEDICAL MACHINE )) I believe change is required at every level of the U.S. healthcare system. This will require the work of not only hospital administrators and providers, but also insurers, policymakers, lobbyists, researchers, pharmaceutical companies, patients— EVERYONE. Ripple challenge: Learn more in our book and podcast: Ripple of Change Everyone can play their part in positive change. What ripple are you working on?

Presenting a reasoning support system for medical diagnosing We present “ASCODI”, an XAI-based interactive reasoning support system for justifiable medical diagnosing, in a paper accepted at the MAI-XAI Workshop at the ECAI 2024 conference. The paper describes work carried out in Project C05 of the TRR 318 Constructing Explainability. - https://lnkd.in/eySQcgza

EASY UNDERSTANDING OF LABORATORY DATASET SDTM #CLINICALSASPROGRAMMER #SASPROGRAMMER

🚨New Pre-print out! MemControl: Mitigating Memorization in Medical Diffusion Models via Automated Parameter Selection. A new strategy to mitigate memorization in Diffusion models We target the problem of memorization in diffusion models and introduce a new framework that identifies the optimal set of parameters to be fine-tuned for two objectives: 1. Improving image generation performance 2. Reducing memorization Our results show that fine-tuning just 0.019% of the model parameters can enable high-quality image generation with minimal memorization! Arxiv Link: https://lnkd.in/giWr2_2X Here is a Twitter thread explaining further details and results: https://lnkd.in/gUaxTk3j Work done jointly with Pedro P. Sanchez, Ondrej Bohdal, Sotirios (Sotos) Tsaftaris and Timothy Hospedales

Hot off the press: A Principled Approach to Characterize and Analyze Partially Observed Confounder Data from Electronic Health Records https://lnkd.in/e6HWzgY2 Missing data in important prognostic factors is one of the most frequently articulated criticisms of real-world data studies. At the same time, systematic assessments for potentially underlying missingness mechanisms are lacking. In this wonderful collaboration with FDA Sentinel Innovation Center, Duke, Kaiser Permanente Washington, and Harvard Pilgrim, we propose a principled approach to characterize and diagnose partially observed confounders. Using comprehensive plasmode simulations, the proposed diagnostics successfully differentiated between different simulated missingness mechanisms and informed the downstream analytic approach to mitigate bias in an applied case example. If you want to implement the proposed approach, you can simply install the 'smdi' R package via install.packages('smdi'). We also have tons of documentation and vignettes to illustrate the use of our approach at https://lnkd.in/ejeZzqHq. Rishi Desai Sudha Raman Pamela A Shaw Hana Lee Massimiliano Russo Brad Hammill Sengwee Darren Toh John Connolly Kimberly J. Dandreo Gegear Fang Tian Wei Liu Jie (Jenni) Li  José J Hernández-Muñoz Robert J Glynn

Digital PCR, a meticulously crafted technique, unveils a captivating realm where each cluster holds unique significance and interpretation. Embark on a journey into this fascinating scientific domain and discover the profound insights it offers. https://lnkd.in/gfcSWZFg #DigitalPCR #DQ24

Introducing our new paper: "Generative Psychometrics via AI-GENIE: Automatic Item Generation and Validation via Network-Integrated Evaluation." Item development AND validation fully automated, in-silica.

We have a new article‼️, which discusses the need to look into statistics when measuring with LIBS. It has been shown, that characteristic data have certain behaviors in relation to the temperature of the plasma plume and, therefore experiment itself. Congratulations 🎉 to Jakub Buday and the whole #LIBS team (Jozef Kaiser, Pavel Pořízka, and Daniel Holub). Good job! 👏 You can read about the article here: https://lnkd.in/e_x9fPR5 #LIBS #LIBSlab #CEITEC #CEITECscience