arXiv ·
Researchers studied user lifetime prediction in the location-based social network Jodel within Saudi Arabia, leveraging its disjoint communities. Machine learning models, particularly Random Forest, were trained to predict user lifetime as a regression and classification problem. A single countrywide model generalizes well and performs similarly to community-specific models.
MBZUAI ·
Researchers at MBZUAI have developed a new machine learning method called survival rank-n-contrast (SurvRNC) to improve survival models for cancer prognoses. The method is designed to predict survival times for head and neck cancer patients using multimodal data while accounting for censored data (missing values). Numan Saeed presented the team’s work at the 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Why it matters: Accurate prognoses can significantly improve patient outcomes, and this research contributes to advancements in machine learning techniques for handling complex and incomplete medical data.
MBZUAI ·
Søren Brunak presented deep learning approaches for analyzing disease trajectories using data from 7-10 million patients in Denmark and the USA. The models predict future outcomes like mortality and specific diagnoses, such as pancreatic cancer, using 15-40 years of patient data. Disease trajectories and explainable AI can generate hypotheses for molecular-level investigations into causal aspects of disease progression. Why it matters: This research demonstrates the potential of large-scale patient data and AI to improve disease prediction and generate hypotheses for further investigation into disease mechanisms relevant to regional healthcare systems.
KAUST ·
KAUST researchers developed a new model integrating SIR compartment modeling in time and a point process modeling approach in space-time, also considering age-specific contact patterns. They used a two-step framework to model infectious locations over time for different age groups. The model demonstrated improved predictive accuracy in simulations and a COVID-19 case study in Cali, Colombia, compared to existing models. Why it matters: This model can assist decision-makers in identifying high-risk locations and vulnerable populations for better disease control strategies in the region and globally.
MBZUAI ·
The InterText project, funded by the European Research Council, aims to advance NLP by developing a framework for modeling fine-grained relationships between texts. This approach enables tracing the origin and evolution of texts and ideas. Iryna Gurevych from the Technical University of Darmstadt presented the intertextual approach to NLP, covering data modeling, representation learning, and practical applications. Why it matters: This research could enable a new generation of AI applications for text work and critical reading, with potential applications in collaborative knowledge construction and document revision assistance.
KAUST ·
KAUST researchers from statistics and earth science collaborated to improve earthquake source modeling. They developed a statistical ranking tool to classify 2D fields, applicable to geoscience models like temperature or precipitation. The tool helps compare different 2D fields describing the earthquake source process and quantify inter-event variability. Why it matters: This cross-disciplinary approach enhances the reliability of earthquake rupture models, contributing to better hazard assessment and risk management in seismically active regions.
KAUST ·
KAUST Professor Raul Tempone, an expert in Uncertainty Quantification (UQ), has been appointed as an Alexander von Humboldt Professor at RWTH Aachen University in Germany. This professorship will enable him to further his research on mathematics for uncertainty quantification with new collaborators. Tempone believes the KAUST Strategic Initiative for Uncertainty Quantification (SRI-UQ) contributed to this award. Why it matters: This appointment enhances KAUST's visibility and facilitates cross-fertilization between European and KAUST research groups, benefiting both institutions and attracting talent.
KAUST ·
KAUST professor David Ketcheson uses mathematical modeling to understand COVID-19 transmission. He applies differential equations to explain the progression of SARS-CoV-2, utilizing the SIR model to predict the spread. Ketcheson's analysis suggests that the reproduction number for COVID-19 could be as high as 5, emphasizing the need for social distancing. Why it matters: This highlights the role of mathematical modeling and data analysis in understanding and predicting the spread of infectious diseases, particularly in the context of pandemic response.