An Editorial from JAMA Internal Medicine titled "Sociodemographic Characteristics in Clinical Algorithms" by Teva D. Brender@1, Timothy Anderson@2, and Raegan W. Durant@3, published December 22, 2025.

💬 Editorial: Most US adults were less comfortable with the use of income or zip code vs race when incorporated into clinical algorithms, despite increasing shifts toward race-neutral tools in #RiskPrediction.

ja.ma/4kOJWPM

JAMA Internal Medicine cover. Editorial: Health Equity. "Sociodemographic Characteristics in Clinical Algorithms" by Teva D. Brender, text@1; Timothy Anderson, text@2; Raegan W. Durant, text@3. Published online December 22, 2025.

💬 Editorial: Most US adults were less comfortable with the use of income or zip code vs race when incorporated into clinical algorithms, despite increasing shifts toward race-neutral tools in #RiskPrediction.

bit.ly/3MoACFg

New CV risk model & heart age 🫀📊
Researchers derived and validated a widely accessible cardiovascular risk prediction model and corresponding “heart age” for UK & US populations — a tool to better personalize risk awareness.
👉 https://ow.ly/OMj250YfpAl

#CardioHealth #RiskPrediction #CardioSky

Excited to start the year by planning to share our #riskprediction work at 2 conferences: @upmc.com Digital Health Summit & @cpddorg.bsky.social #cpdd36! At #cpdd26, we'll join colleagues from @brownpublichealth.bsky.social to share lessons learned from our experience doing this work. See you there!

JAMA Internal Medicine editorial, "Sociodemographic Characteristics in Clinical Algorithms" by Teva D. Brender, Timothy Anderson, and Raegan W. Durant. Published online December 22, 2025.

💬 Editorial: Most US adults were less comfortable with the use of income or zip code vs race when incorporated into clinical algorithms, despite increasing shifts toward race-neutral tools in #RiskPrediction.

ja.ma/49rqswj

Regional variations in cardiovascular risk predictions: a comparative analysis of Framingham, SCORE2, and WHO models across 53 countries.
Bian, Wenming et al.
Paper
Details
#CardiovascularRisk #GlobalHealth #RiskPrediction

Can #deeplearning analysis of #preoperative #ECGs improve #riskprediction for perioperative major cardiovascular events?

www.bjanaesthesia.org/article/S0007-0912(25)00...

🧬 New research alert: Comparison of PREVENT Score versus the Pooled Cohort Equation for estimating ASCVD risk in patients with Rheumatoid Arthritis. Explore the full study 👉 https://ow.ly/zrxH50XjT5h

#Cardiology #RA #ASCVD #RiskPrediction #PreventiveCardiology #CardioSky #MedSky

AI is making credit decisions more adaptive, transparent, and fair than ever before.
🔗 Explore the 8 steps for building a deep learning credit scoring system: https://f.mtr.cool/iegmtazfeq

#DeepLearning #FintechInnovation #RiskPrediction #ArbisoftBlogs

IGES 2025: Causality, diversity, and innovation Read about the 2025 International Genetic Epidemiology Society conference and learn why genomics research is entering a new era.

How is genetic epidemiology evolving in the era of big data and AI?
Read our recap of key trends and takeaways from IGES 2025 here: medium.com/zs-associate...
#IGES2025 #GeneticEpidemiology #MultiOmics #AIinGenomics #DiversityInResearch #RiskPrediction #Innovation #ZS

Machine Learning Approaches to Injury Risk Prediction in Sport - Premier Science Injury risk prediction, Machine learning models, Sports analytics, Athlete monitoring, Random Forest and xgboost.

doi.org/10.70389/PJC...

#machinelearning #injury #riskprediction #sport

Congrats to @walidgellad.bsky.social & the mPROVEN study team on a new award to create a playbook for ethical use of #machinelearning based #opioid #riskprediction models, building on years of expertise developing & deploying these tools in a variety of care settings.

www.cp3.pitt.edu/mproven

Clinicians often decide treatment based only on symptoms and clinical history. Our study combines risk prediction and decision models to guide more cost-effective treatment choices.

medrxiv.org/cgi/content/...

#RiskPrediction #DecisionMaking #CostEffectiveness #MachineLearning

Clinical utility of self-reported sleep duration and insomnia symptoms in type 2 diabetes prediction - Diabetologia Aims/hypothesis Suboptimal sleep health is linked to higher risks for incident type 2 diabetes. We aimed to assess the clinical utility of adding self-reported sleep traits to a type 2 diabetes predic...

Sleep duration & insomnia associated with ↑ #T2D risk but addition of these sleep traits to #QDiabetes risk prediction model offers no clinically meaningful predictive gain. #riskprediction #DiabetesResearch link.springer.com/article/10.1... 🔓

Risk Prediction Projects | Center for Pharmaceutical Policy and Prescribing | University of Pittsburgh Our group has conducted pivotal work that addresses the opioid epidemic by developing overdose risk prediction tools using machine learning and building the foundation for implementing these tools in ...

The PROTECT study builds on CP3's extensive experience in creating #RiskPrediction #algorithms and deploying them in practice with paired decision support tools to help clinicians make informed decisions for safer prescribing.

www.cp3.pitt.edu/research/ris...

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Development and recalibration of a multivariable type 1 diabetes prediction model for type 1 diabetes across multiple screening studies - BMC Medicine Background Accurate type 1 diabetes prediction is important to facilitate screening for pre-clinical type 1 diabetes to enable potential early disease-modifying interventions and to reduce the risk of...

🚨Big news! Excited to share my first PhD paper!🎉
We validated & improved a T1D risk model using TrialNet data (originally from TEDDY), boosting accuracy 📈
bmcmedicine.biomedcentral.com/articles/10....
Try the web tool 👉 t1dpredictor.diabetesgenes.org
#T1D #RiskPrediction #PrecisionMedicine #TrialNet

Assessing left main bifurcation anatomy and haemodynamics as a potential surrogate for disease risk in suspected coronary artery disease without stenosis - Scientific Reports Scientific Reports - Assessing left main bifurcation anatomy and haemodynamics as a potential surrogate for disease risk in suspected coronary artery disease without stenosis

🧠 No stenosis? No problem. Left main artery shape alone may act as a surrogate for disease risk.

First study to link anatomy + haemodynamics in patients without overt disease.
👉 www.nature.com/articles/s41...

#RiskPrediction #Cardiology #MedImageAI

Comparative performance of risk prediction indices for mortality or readmission following heart failure hospitalization Comparative performance of 7 risk prediction indices in patients hospitalized for heart failure. In this cohort of 1206 patients, the LENT index offered the greatest discrimination, calibration, and ...

The simple LENT index—using length of stay, prior ED visits, and NT-proBNP—best predicts 30-day death or readmission after #HeartFailure hospitalization. Accurate, easy, and practical! #RiskPrediction @drrajivsankar.bsky.social @mmamas1973.bsky.social onlinelibrary.wiley.com/doi/10.1002/...

The development and evaluation of Polygenic Risk Score reports: A systematised review of the literature The return of polygenic risk scores (PGS) is currently being assessed in research settings for clinical utility and validity, and it is anticipated th…

The development and evaluation of Polygenic Risk Score reports: A systematised review of the literature www.sciencedirect.com/science/arti... #genetics #genomics #PRS #riskprediction

Development and Validation of a #RiskPrediction Model to Identify Women With Chronic Obstructive Pulmonary Disease for Proactive #PalliativeCare
onlinelibrary.wiley.com/doi/10.1111/...

#COPD

Metabolomic and genomic prediction of common diseases in 700,217 participants in three national biobanks - Nature Communications Identifying individuals at high risk for chronic diseases can improve prevention. Here, the authors show that blood metabolomics scores effectively stratify disease risk and compare favorably to genet...

Long awaited peer review finally completed!! Our paper comparing quantitative metabolomics disease risk prediction with PGS is accepted in Nature Comms!! 🥳🥳
@nightingalehealth.com #riskprediction #metabolomics #nmr #genomics #polygenicscore

www.nature.com/articles/s41...

Our #KDIGOCoronaryValve workgroup debates criteria for cardiac eval before #KidneyTransplant, surveillance after listing, novel #RiskPrediction tools, & #perioperative care. Appreciate multidisciplinary brainstorming at reception. Thanks to @wolfgang for welcome address. @goKDIGO

Our #KDIGOCoronaryValve workgroup debates criteria for cardiac eval before #KidneyTransplant, surveillance after listing, novel #RiskPrediction tools, & #perioperative care. Appreciate multidisciplinary brainstorming at reception. Thanks to @wolfgang for welcome address. @goKDIGO

Forgot to hashtag this: #MachineLearning #riskprediction #notmyareaofexpertise