AI Medical Compendium Topic

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Translational Research, Biomedical

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Translating big data to better treatment in bipolar disorder - a manifesto for coordinated action.

European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology
Bipolar disorder (BD) is a major healthcare and socio-economic challenge. Despite its substantial burden on society, the research activity in BD is much smaller than its economic impact appears to demand. There is a consensus that the accurate identi...

Gut microbiome, big data and machine learning to promote precision medicine for cancer.

Nature reviews. Gastroenterology & hepatology
The gut microbiome has been implicated in cancer in several ways, as specific microbial signatures are known to promote cancer development and influence safety, tolerability and efficacy of therapies. The 'omics' technologies used for microbiome anal...

Realistic simulation of virtual multi-scale, multi-modal patient trajectories using Bayesian networks and sparse auto-encoders.

Scientific reports
Translational research of many disease areas requires a longitudinal understanding of disease development and progression across all biologically relevant scales. Several corresponding studies are now available. However, to compile a comprehensive pi...

Ontologies, Knowledge Representation, and Machine Learning for Translational Research: Recent Contributions.

Yearbook of medical informatics
OBJECTIVES: To select, present, and summarize the most relevant papers published in 2018 and 2019 in the field of Ontologies and Knowledge Representation, with a particular focus on the intersection between Ontologies and Machine Learning.

sureLDA: A multidisease automated phenotyping method for the electronic health record.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: A major bottleneck hindering utilization of electronic health record data for translational research is the lack of precise phenotype labels. Chart review as well as rule-based and supervised phenotyping approaches require laborious expert...

Character level and word level embedding with bidirectional LSTM - Dynamic recurrent neural network for biomedical named entity recognition from literature.

Journal of biomedical informatics
Named Entity Recognition is the process of identifying different entities in a given context. Biomedical Named Entity Recognition (BNER) is the task of extracting chemical names from biomedical texts to support biomedical and translational research. ...

Precision Medicine, AI, and the Future of Personalized Health Care.

Clinical and translational science
The convergence of artificial intelligence (AI) and precision medicine promises to revolutionize health care. Precision medicine methods identify phenotypes of patients with less-common responses to treatment or unique healthcare needs. AI leverages ...

Use of Machine Learning to Re-Assess Patterns of Multivariate Functional Recovery after Fluid Percussion Injury: Operation Brain Trauma Therapy.

Journal of neurotrauma
Traumatic brain injury (TBI) is a leading cause of death and disability. Yet, despite immense research efforts, treatment options remain elusive. Translational failures in TBI are often attributed to the heterogeneity of the TBI population and limite...

Application of Artificial Intelligence for the Diagnosis and Treatment of Liver Diseases.

Hepatology (Baltimore, Md.)
Modern medical care produces large volumes of multimodal patient data, which many clinicians struggle to process and synthesize into actionable knowledge. In recent years, artificial intelligence (AI) has emerged as an effective tool in this regard. ...