Results 51 to 60 of about 47,050 (138)
With the acceleration in population migration and urbanization, accurate population density prediction has become increasingly important for regional planning and resource management.
Chenxi Cui +3 more
doaj +1 more source
Persian Causality Corpus (PerCause) and the Causality Detection Benchmark
Recognizing causal elements and causal relations in the text is among the challenging issues in natural language processing (NLP), specifically in low-resource languages such as Persian. In this research, we prepare a causality human-annotated corpus for
Zeinab Rahimi, Mehrnoush ShamsFard
doaj
Background Systemic lupus erythematosus (SLE) is a complex autoimmune disease with unclear pathogenesis. Recent studies suggest that immune cell phenotypes may play a causal role.
Luofei Huang +3 more
doaj +1 more source
fairadapt: Causal Reasoning for Fair Data Preprocessing
Machine learning algorithms are useful for various prediction tasks, but they can also learn how to discriminate, based on gender, race or other sensitive attributes.
Drago Plečko +2 more
doaj +1 more source
Intestinal flora and inflammatory bowel disease: Causal relationships and predictive models
Background: Inflammatory bowel disease (IBD), including Crohn's disease and ulcerative colitis, is significantly influenced by intestinal flora. Understanding the genetic and microbiotic interplay is crucial for IBD prediction and treatment.
Guan-Wei Bi +6 more
doaj +1 more source
Real-World Evidence, Causal Inference, and Machine Learning [PDF]
The current focus on real world evidence (RWE) is occurring at a time when at least two major trends are converging. First, is the progress made in observational research design and methods over the past decade. Second, the development of numerous large observational healthcare databases around the world is creating repositories of improved data assets
openaire +2 more sources
Both machine learning and mechanistic modelling approaches have been used independently with great success in systems biology. Machine learning excels in deriving statistical relationships and quantitative prediction from data, while mechanistic ...
Ben Noordijk +10 more
doaj +1 more source
Causal Machine Learning for Moderation Effects
It is valuable for any decision maker to know the impact of decisions (treatments) on average and for subgroups. The causal machine learning literature has recently provided tools for estimating group average treatment effects (GATE) to understand treatment heterogeneity better.
Bearth, Nora, Lechner, Michael
openaire +2 more sources
Background Modeling causality through graphs, referred to as causal graph learning, offers an appropriate description of the dynamics of causality.
Hang Wu, Wenqi Shi, May D. Wang
doaj +1 more source

