#HappyValentine'sDay and #thankyou for 2000 #followers!

In #AMBROSIAproject we #love #food and we want it to be #safe for our #beloved #people. With #predictivemodels and #AI #platforms we tackle #food_safety issues through #climate #change! #HorizonEurope #EUFunded #EuropeanResearchExecutiveAgency

Perception of AI Symptom Models in Oncology Nursing: Mixed Methods Evaluation Study Background: Patients undergoing cancer treatment experience significant symptom burden. The standard process of symptom management includes patient reporting and clinical response following symptom escalation. Emerging predictive symptom models utilize AI components of machine learning and deep learning to identify the risk of symptom deterioration, facilitating earlier intervention to prevent downstream effects. However, integrating predictive symptom models into clinical practice will require oncology #nurses to adopt innovative approaches. Objective: The object of our evaluation was to explore oncology #nurses' perceptions of the use of predictive symptom models in cancer care and the factors influencing the adoption of this symptom care innovation. Methods: The evaluation was guided by Rogers’ Diffusion of Innovation Theory, which describes the process of how individuals adopt new technologies. The investigators developed an interview guide that asked oncology #nurses to rate their perceptions of AI symptom models on a Likert scale. Participants were also asked to provide qualitative comments to support their ratings for each question, in order to better understand the key factors that would influence AI predictive model adoption. Investigators analyzed demographic data and Likert ratings with descriptive statistics. Qualitative analysis of participant comments included content analysis and inductive coding to identify themes. #nurses’ perception of factors that would influence the adoption of AI symptom models, based on Rogers’ theory, included relative advantage, compatibility, complexity, trialability, and observability. Results: Responses of 15 oncology #nurses with greater than one year of experience in oncology were analyzed. There was high agreement among #nurse participants that an AI model could improve symptom management for oncology patients (n = 10, 67%) and increase early intervention to prevent the escalation of symptoms (n = 12, 86%). All participants (n= 15, 100%) agreed that receiving symptom information would be helpful. Nearly three-quarters of participants (n = 11, 73%) endorsed that the information would save time. Most (n = 12, 80%) recommended that clinicians receive information about predicted symptom deterioration of their patients. Among open-ended responses, key themes were consistent with factors identified in the Diffusion of Innovation theory including: 1) perceptions related to the AI model (compatibility/ complexity), 2) #nurses’ perception of patients benefit, (observability) 3) improved clinical processes, (relative advantage/ observability) 4) apprehension over model accuracy and impact, (compatibility/ trialability/observability) and 5) implementation/ adoption (trialability/ complexity/observability). Conclusions: Oncology #nurses agree that predictive symptom models could help improve symptom management for patients undergoing cancer treatment. However, #nurses noted that transparency in the factors included in the AI model was essential, that #nurses should be involved in the development and testing of models, and that the observability of the benefit in symptom care would need to be evident for ultimate adoption.

New in JMIR Nursing: Perception of AI Symptom Models in Oncology Nursing: Mixed Methods Evaluation Study #OncologyNursing #AIMedicine #SymptomManagement #CancerCare #PredictiveModels

Brain cancer digital twin predicts treatment outcomes – BioNews Central A simulation of an individual patient's brain cancer, kept up to date with readily available data, can identify whether dietary treatments and drugs are likely to work. ...

A #simulation of an individual patient's #BrainCancer, kept up to date with readily available data, can identify whether #DietaryTreatments and #drugs are likely to work. 
#BrainCancerTreatment #DigitalTwin #PredictiveModels #TreatmentOutcomes

Predictive models and new technologies are becoming increasingly important in the real estate market, Economic News.
#realestatemarket #predictivemodels #newtechnologies #investment #dataspaces #economicnews #art #illustration #photomontage #collage

Turning Data into Action: Empowering frontline leaders in patient support services Key takeaways: Five Ways to Strengthen Data’s Impact | Bridge the gap between analytics and patient care with data-driven strategies for better outcomes in patient support programs.

#AI #patientsupportservices #datagap #analytics #deliveryinsights #insights #qualitativeassessmentdata #predictivemodels #realtimedata #AIassistants #datastrategy #DataDisconnect #machinelearning #Qualitativedata #Qualitativedatacapture #patiettsupportstrategy
zurl.co/0DoT3

A herd of animals, featuring different species, moving together across an open plain.

To truly protect #biodiversity, we must harness #PredictiveModels to reveal which #ConservationBiology strategies succeed—and which fail. Read the PNAS News Opinion: https://ow.ly/iVsF50Xl1BL

#GlobalBiodiversityFramework #GBF #conservation

A herd of animals, featuring different species, moving together across an open plain.

To preserve #biodiversity on a global scale, conservationists must more routinely use #PredictiveModels to assess which strategies succeed. Read the PNAS opinion: https://ow.ly/tkcj50XhElh

#ConservationBiology #GBF #ConventionOnBiologicalDiversity

Without forward-looking models, biodiversity policy risks missing the road to recovery.
Our new PNAS article calls for predictive tools and a World Biodiversity Research Programme to guide global action.

👉 doi.org/10.1073/pnas...
#Biodiversity #Conservation #PredictiveModels #PNAS

Led by the #EcoCode group of @geobon.org, we call for a World Biodiversity Research Programme to coordinate global modelling and support the Kunming–Montreal Global Biodiversity Framework. #GBF #PredictiveModels
@mark-urban.bsky.social @gretabocedi.bsky.social @sjevelazco.bsky.social

Turning data into insights takes more than dashboards. A strong analytics platform blends strategy, architecture, governance, and culture to turn raw information into real value. taxodiary.com?p=56225 #DataAnalytics #PredictiveModels

Stock Market Predictions and AI The Future of Investment Intelligence The financial world is undergoing a revolution powered by artificial intelligence (AI). At the heart of this transformati

The Future of Investment Intelligence
The financial world is undergoing a revolution powered by artificial intelligence (AI). #algorithms #dataanalysis #machinelearning #predictivemodels #SAVINGSUKLtd #stockforecasting
www.stockexchange.eu/stock-market...

Early suPAR levels as a predictor of COVID-19 severity: A new tool for efficient patient triage Following several waves of the COVID-19 pandemic, we are now facing a lower but persistent rate of SARS-CoV-2 infections, with seasonal resurgences of…

[Publication] The article "Early suPAR levels as a predictor of COVID-19 severity: A new tool for efficient patient triage" is now available.

www.sciencedirect.com/science/arti...

#IDsky #suPAR #SARSCoV2Primoinfection #PredictiveModels

Please join a few of our students for the Responsible AI Symposium poster session on Friday, Feb 28th, from 12 to 1 pm at the Duke Karsh Alumni Center! Our students will present on 2 main topics: demographic data reporting in #predictivemodels and using #LLMs to assist literature reviews

Climate Change Is Driving Dangerous Bacteria Farther North
#Health #Environment #ClimateChange #Bacteria #CCN #Vibrio #VibrioBacteria #OceanWarming #PublicHealth #SeafoodSafety #InfectiousDiseases #PredictiveModels #EnvironmentalImpact #VibriosisInfections
the-14.com/climate-chan...

The MAEASaM #RemoteSensing Working Group worked on several #predictivemodels during Phase 1 of the project.

Link 👇 to explore more about predictive models for Stone Age sites in #Tanzania and shell middens in #Senegal.

🔗 maeasam.org/phase-1-of-t...

#ChatGPT create #PredictiveModels

What do you think?

www.youtube.com/live/QZYLwyt...

Machine Learning Boosts Earthquake Prediction Accuracy in Los Angeles Researchers have enhanced earthquake prediction accuracy in Los Angeles using advanced machine learning models, achieving 97.97% accuracy by comparing 16 algorithms.

Machine Learning Boosts Earthquake Prediction Accuracy in Los Angeles 🌍📊⚠️ www.azoai.com/news/2024102... #EarthquakePrediction #MachineLearning #LosAngeles #Seismic #RandomForest #NeuralNetworks #ArtificialIntelligence #Seismology #PredictiveModels

Enhance Machine Learning Models With Causal Inference Combining machine learning with causal inference improves accuracy and provides deeper insights into data, enhancing predictive power and decision-making.

What Makes Causal Inference So Powerful?


Causal inference, at its core, is about determining the "why" behind things. #AIinsights #AIpredictions #causalinference #causalrelationships #DataScience #MachineLearning #MLandcausality #MLModels #PredictiveModels
aicompetence.org/enhance-mach...

I recommend all the talks. They have great insights from a variety of perspectives into #AI, automation, #NLP, and #predictivemodels in global development. Kudos to sponsors World Bank DIME, UChicago Development Impact Lab, and UC Berkeley's Center for Effective Global Action (CEGA).