Results 41 to 50 of about 39,390 (254)

CEREC: Causality Extraction from Requirements Artifacts

2020 IEEE Seventh International Workshop on Artificial Intelligence for Requirements Engineering (AIRE), 2020
The cause-effect recognition (CEREC) system provides an API for causality extraction tailored to the requirements engineering context. The library is written in Java and is released under the MIT open source license. In this paper, the underlying algorithm is described, and a demonstration of the active learning component for causality extraction is ...
openaire   +3 more sources

The Role of “Adult‐Onset” Cancer Predisposition Genes in Pediatric Cancer: A Comprehensive Review

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Current literature estimates that 10% of pediatric cancers are caused by pathogenic or likely pathogenic (P/LP) germline variants in cancer predisposition genes (CPGs). Variants in CPGs thought to increase cancer risk exclusively during adulthood are referred to as “adult‐onset” CPGs (aoCPGs).
Maria Rozo, Miriam A. Bornhorst, Angela J. Waanders, Sara Zarnegar‐Lumley, Ashwin Lakshman Koppayi, Lucy A. Godley   +5 more
wiley   +1 more source

Health Literacy, Self‐Efficacy and Knowledge of Sickle Cell Disease Among Caregivers

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Sickle cell disease (SCD) is a hereditary blood disorder in which abnormal haemoglobin leads to severe anaemia, painful crises and organ failure. Caregivers’ health literacy (HL) – their ability to assess, understand and apply information, and interact with healthcare professionals – is crucial for managing children with SCD, yet ...
Melanie Bruinooge, Maite E. Houwing, Rowena L. Grohssteiner, Ferdows Atiq, Sonja A. M. C. Teuben, Mirjam P. Fransen, Marjon H. Cnossen   +6 more
wiley   +1 more source

A Multitask Learning Framework for Multigranular Causality Detection and Extraction in Indonesian EMRs

IEEE Access
Extracting causal relations from Electronic Medical Records (EMRs) is important for transforming unstructured clinical narratives into structured knowledge that supports decision making. However, most existing approaches split causality modeling into two
Enny Dwi Oktaviyani, Pusadee Seresangtakul, Pongsathon Janyoi, Chitsutha Soomlek   +3 more
doaj   +1 more source

Multivariate Time Series Forecasting with Transfer Entropy Graph

Tsinghua Science and Technology, 2023
Multivariate Time Series (MTS) forecasting is an essential problem in many fields. Accurate forecasting results can effectively help in making decisions. To date, many MTS forecasting methods have been proposed and widely applied.
Ziheng Duan, Haoyan Xu, Yida Huang, Jie Feng, Yueyang Wang   +4 more
doaj   +1 more source

Using Noisy Extractions to Discover Causal Knowledge

CoRR, 2017
Knowledge bases (KB) constructed through information extraction from text play an important role in query answering and reasoning. In this work, we study a particular reasoning task, the problem of discovering causal relationships between entities, known as causal discovery. There are two contrasting types of approaches to discovering causal knowledge.
Dhanya Sridhar, Jay Pujara, Lise Getoor
openaire   +2 more sources

Leukemia and Exposure to Potential Benzene Sources in Children From the Mexico City Metropolitan Area, 2010–2021: A Geospatial Analysis

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Leukemia is the most common childhood cancer in Mexico, and acute lymphoblastic leukemia (ALL) is the most frequent subtype. Exposure to high concentrations of benzene has been associated with ALL incidence, particularly in urban areas. This study evaluated the relationship between distance to benzene emission sources and the number
Orlando Rivera Zurita, Norma Candelaria López Santiago, Claudia Maria García Cuellar, Karla Maldonado Silva, Nancy Reynoso Noverón, Leticia Hernández Cadena   +5 more
wiley   +1 more source

Semantic Causality Knowledge Graph with Ontology Integration for Financial Analysis [PDF]

ITM Web of Conferences
In recent years, knowledge graphs have become vital for financial decision support by allowing structured representation and reasoning through complex textual data.
Manjunath Chinthakunta, H Lakshmi, Maken Pooja, M Manohar, Shaju Samuel   +4 more
doaj   +1 more source

Association Between Dinutuximab Beta Exposure and Post‐End‐of‐Treatment Survival in Neuroblastoma: A Weighted Patient‐Level Analysis of Three Clinical Studies

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Objectives The association between exposure to dinutuximab beta (DB) and event‐free survival (EFS) or overall survival (OS) of neuroblastoma patients was assessed using data collected during three clinical trials (five cohorts). Methods A systematic review (March 2026) was conducted to identify relevant studies (prospective; registered DB ...
Przemysław Holko, Holger N. Lode, Alberto Garaventa, Carla Manzitti, Roberto Luksch, Dominique Valteau‐Couanet, Marion Gambart, John Anderson, Juliet Gray, Adela Canete, Lucas Moreno, Aleksandra Wieczorek, Cormac Owens, Shifra Ash, Isaac Yaniv, Genevieve Laureys, Hans Loibner, Katarzyna Śladowska, Ruth Ladenstein, Paweł Kawalec   +19 more
wiley   +1 more source

Large Language Models (LLMs) and Causality Extraction from Text

Proceedings of the International Florida Artificial Intelligence Research Society Conference
This tutorial explores the application of Large Language Models (LLMs), such as BERT, LLAMA, and GPT-3.5/4, to the extraction of causality from text documents, including identifying causes, effects, and actions in diverse texts, such as business ...
Wlodek Zadrozny
doaj   +1 more source