AIMC Topic: Causality

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An effective neural model extracting document level chemical-induced disease relations from biomedical literature.

Journal of biomedical informatics May 8, 2018
Since identifying relations between chemicals and diseases (CDR) are important for biomedical research and healthcare, the challenge proposed by BioCreative V requires automatically mining causal relationships between chemicals and diseases which may...
Publications Data Mining MEDLINE Chemically-Induced Disorders Neural Networks, Computer Causality
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Understanding and diagnosing the potential for bias when using machine learning methods with doubly robust causal estimators.

Statistical methods in medical research May 2, 2018
Data-adaptive methods have been proposed to estimate nuisance parameters when using doubly robust semiparametric methods for estimating marginal causal effects. However, in the presence of near practical positivity violations, these methods can produ...
Models, Statistical Bias Probability Propensity Score Humans Machine Learning Causality
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Heterogeneity Analysis and Diagnosis of Complex Diseases Based on Deep Learning Method.

Scientific reports Apr 18, 2018
Understanding genetic mechanism of complex diseases is a serious challenge. Existing methods often neglect the heterogeneity phenomenon of complex diseases, resulting in lack of power or low reproducibility. Addressing heterogeneity when detecting ep...
Epistasis, Genetic Algorithms Deep Learning Computational Biology Humans Cluster Analysis Gene Expression Regulation Causality Disease Susceptibility
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Comorbidity Scoring with Causal Disease Networks.

IEEE/ACM transactions on computational biology and bioinformatics Mar 6, 2018
In recent years, there has been numerous studies constructing a disease network with diverse sources of data. Many researchers attempted to extend the usage of the disease network by employing machine learning algorithms on various problems such as p...
Algorithms Databases, Factual Comorbidity Computational Biology Humans Risk Causality Epidemiology Models, Biological Supervised Machine Learning
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Machine learning in cardiovascular medicine: are we there yet?

Heart (British Cardiac Society) Jan 19, 2018
Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing sev...
Diagnostic Imaging Registries Datasets as Topic Genomics Genetic Predisposition to Disease Causality Clinical Trials as Topic Cardiology Phenotype Big Data Humans Machine Learning
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Big Data in Public Health: Terminology, Machine Learning, and Privacy.

Annual review of public health Dec 20, 2017
The digital world is generating data at a staggering and still increasing rate. While these "big data" have unlocked novel opportunities to understand public health, they hold still greater potential for research and practice. This review explores se...
Public Health Humans Population Surveillance Big Data Machine Learning Confidentiality Causality Research Terminology as Topic
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Collaborative targeted learning using regression shrinkage.

Statistics in medicine Nov 2, 2017
Causal inference practitioners are routinely presented with the challenge of model selection and, in particular, reducing the size of the covariate set with the goal of improving estimation efficiency. Collaborative targeted minimum loss-based estima...
Propensity Score Likelihood Functions Biostatistics Regression Analysis Outcome Assessment, Health Care Pregnancy Complications Asthma Causality Models, Statistical Humans Machine Learning Algorithms Female Computer Simulation Infant, Newborn Pregnancy
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Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies.

American journal of epidemiology Dec 9, 2016
Estimation of causal effects using observational data continues to grow in popularity in the epidemiologic literature. While many applications of causal effect estimation use propensity score methods or G-computation, targeted maximum likelihood esti...
Humans Machine Learning Depression Causality Computer Simulation Exercise Propensity Score Observational Studies as Topic Likelihood Functions Confounding Factors, Epidemiologic Data Interpretation, Statistical Epidemiologic Research Design Bias
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An algorithm for direct causal learning of influences on patient outcomes.

Artificial intelligence in medicine Nov 5, 2016
OBJECTIVE: This study aims at developing and introducing a new algorithm, called direct causal learner (DCL), for learning the direct causal influences of a single target. We applied it to both simulated and real clinical and genome wide association ...
Patient Outcome Assessment Causality Bayes Theorem Alzheimer Disease Female Humans Breast Neoplasms Algorithms Machine Learning Genome-Wide Association Study Prognosis
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Combining machine learning and propensity score weighting to estimate causal effects in multivalued treatments.

Journal of evaluation in clinical practice Jul 15, 2016
RATIONALE, AIMS AND OBJECTIVES: Interventions with multivalued treatments are common in medical and health research; examples include comparing the efficacy of competing interventions and contrasting various doses of a drug. In recent years, there ha...
Treatment Outcome Causality Regression Analysis Algorithms Aged Humans Female Male Propensity Score Machine Learning Middle Aged Observation Empirical Research
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