MLwell lab

We held a mini symposium to discuss the guide for Risk Management and Responsible Use of Artificial Intelligence in the Public Sector. This was a collaboration of the Knowledge Center for AI Policy (KNAIP) and the Shamgar Center for Digital Law and Innovation.

Together with the Ministry of Innovation, Science and Technology of Israel we have established a National Knowledge Center for the Implementation of Artificial Intelligence Applications in Government Ministries

We are honored to have received a grant from SPARK@TAU. This collaboration provides us with a unique opportunity to expand the reach of the algorithms we have developed, enabling us to bring them to a wider audience of potential users. We would like to express our sincere gratitude to SPARK@TAU for their support and partnership

This paper provides a comprehensive framework for reporting the use of Natural Language Processing (NLP) methods in scientific research, focusing on transparency and reproducibility. It highlights the critical role of clear documentation in processes such as data collection, preprocessing, model selection, and performance evaluation.

In this work, we address the challenge of ensuring data quality in Electronic Health Records (EHRs), where the focus often shifts to model development rather than the underlying data itself. To fill this gap, we introduce the Medical Data Pecking Tool (MDPT), an innovative solution that utilizes unit-testing techniques to evaluate EHR data quality and its suitability for specific research questions. By combining a dataframe testing tool with a Large Language Model (LLM), MDPT can automatically generate and execute customized evaluations based on predefined criteria such as population traits and regional health patterns, ensuring that the data aligns with expected patterns for various diseases and geographic regions.

The Graph Neural Additive Network (GNAN) is the first interpretable-by-design graph neural network. It extends Generalized Additive Models (GAMs) to graph data, offering both high performance and transparency. As a result, GNAN is well-suited for high-stakes applications

This paper examines whether eXplainable AI (XAI) tools can effectively support the legal "right to explanation" by analyzing explanation's role across different stakeholders - decision subjects, decision makers, and the broader ecosystem. While XAI proves effective in strengthening system authority from an ecosystem perspective, it falls short in serving both decision subjects' and makers' needs, potentially making it an inadequate and possibly harmful tool for protecting human rights rather than the guardian it was intended to be.

The xgbGAMView allows learning GAMs with a familiar scikit-learn interface. The GAMs use xgboost as the underlying engine to learn the model and offer visualization as well as graph smoothing options. The library can be installed from PyPi (pip install xgbGAMView).

This paper explores the influence of body mass index (BMI) and examination type on the utilization of screening programs, leveraging big data analytics to uncover patterns and disparities. The study highlights how BMI and the nature of medical examinations impact participation rates, shedding light on potential barriers to equitable healthcare access. By analyzing large-scale data, the authors provide valuable insights into optimizing screening programs, aiming to improve their effectiveness and accessibility for diverse populations.

The impact of long-term Coronavirus disease 2019 (COVID-19) on the pediatric population is still not well understood. This study was designed to estimate the magnitude of COVID-19 long-term morbidity 3–6 months after the date of diagnosis.

When dealing with tabular data, models based on decision trees are a popular choice due to their high accuracy on these data types, their ease of application, and explainability properties. However, when it comes to graph-structured data, it is not clear how to apply them effectively, in a way that in- corporates the topological information with the tabular data available on the vertices of the graph. To address this challenge, we introduce TREE-G. TREE-G modifies standard decision trees, by introducing a novel split function that is specialized for graph data. Not only does this split function incorporate the node features and the topological information, but it also uses a novel pointer mechanism that allows split nodes to use information computed in previous splits. Therefore, the split function adapts to the predictive task and the graph at hand. We analyze the theoretical properties of TREE-G and demonstrate its benefits empirically on multiple graph and vertex prediction benchmarks. In these experiments, TREE-G consistently outperforms other tree-based models and often outperforms other graph-learning algorithms such as Graph Neural Networks (GNNs) and Graph Kernels, sometimes by large margins. Moreover, TREE-Gs models and their predic tions can be explained and visualized.

Congratulations to Belle Kriger, Hagar Rosenblatt, Chaya Ben-Yehuda, and Yarin Udi for completing their Master's degrees