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Likelihood Functions

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Characterising the nationwide burden and predictors of unkept outpatient appointments in the National Health Service in England: A cohort study using a machine learning approach.

PLoS medicine
BACKGROUND: Unkept outpatient hospital appointments cost the National Health Service £1 billion each year. Given the associated costs and morbidity of unkept appointments, this is an issue requiring urgent attention. We aimed to determine rates of un...

AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data.

Journal of biomedical informatics
BACKGROUND: Scoring systems are highly interpretable and widely used to evaluate time-to-event outcomes in healthcare research. However, existing time-to-event scores are predominantly created ad-hoc using a few manually selected variables based on c...

Estimation of Parameters on Probability Density Function Using Enhanced GLUE Approach.

Computational intelligence and neuroscience
The most essential process in statistical image and signal processing is the parameter estimation of probability density functions (PDFs). The estimation of the probability density functions is a contentious issue in the domains of artificial intelli...

A Survey of Blind Modulation Classification Techniques for OFDM Signals.

Sensors (Basel, Switzerland)
Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communic...

Evaluating the robustness of targeted maximum likelihood estimators via realistic simulations in nutrition intervention trials.

Statistics in medicine
Several recently developed methods have the potential to harness machine learning in the pursuit of target quantities inspired by causal inference, including inverse weighting, doubly robust estimating equations and substitution estimators like targe...

Missing data imputation in clinical trials using recurrent neural network facilitated by clustering and oversampling.

Biometrical journal. Biometrische Zeitschrift
In clinical practice, the composition of missing data may be complex, for example, a mixture of missing at random (MAR) and missing not at random (MNAR) assumptions. Many methods under the assumption of MAR are available. Under the assumption of MNAR...

A LASSO-based approach to sample sites for phylogenetic tree search.

Bioinformatics (Oxford, England)
MOTIVATION: In recent years, full-genome sequences have become increasingly available and as a result many modern phylogenetic analyses are based on very long sequences, often with over 100 000 sites. Phylogenetic reconstructions of large-scale align...

Efficient targeted learning of heterogeneous treatment effects for multiple subgroups.

Biometrics
In biomedical science, analyzing treatment effect heterogeneity plays an essential role in assisting personalized medicine. The main goals of analyzing treatment effect heterogeneity include estimating treatment effects in clinically relevant subgrou...

CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database.

Nucleic acids research
The Comprehensive Antibiotic Resistance Database (CARD; card.mcmaster.ca) combines the Antibiotic Resistance Ontology (ARO) with curated AMR gene (ARG) sequences and resistance-conferring mutations to provide an informatics framework for annotation a...

Data-Driven Identification of Clinical Real-World Expressions Linked to ICD.

Studies in health technology and informatics
A semi-structured clinical problem list containing ∼1.9 million de-identified entries linked to ICD-10 codes was used to identify closely related real-world expressions. A log-likelihood based co-occurrence analysis generated seed-terms, which were i...