AI Medical Compendium Topic:
Data Mining

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Evaluating semantic relations in neural word embeddings with biomedical and general domain knowledge bases.

BMC medical informatics and decision making
BACKGROUND: In the past few years, neural word embeddings have been widely used in text mining. However, the vector representations of word embeddings mostly act as a black box in downstream applications using them, thereby limiting their interpretab...

RDE: A novel approach to improve the classification performance and expressivity of KDB.

PloS one
Bayesian network classifiers (BNCs) have demonstrated competitive classification performance in a variety of real-world applications. A highly scalable BNC with high expressivity is extremely desirable. This paper proposes Redundant Dependence Elimin...

Deep neural models for extracting entities and relationships in the new RDD corpus relating disabilities and rare diseases.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: There is a huge amount of rare diseases, many of which have associated important disabilities. It is paramount to know in advance the evolution of the disease in order to limit and prevent the appearance of disabilities and ...

A review of statistical and machine learning methods for modeling cancer risk using structured clinical data.

Artificial intelligence in medicine
Advancements are constantly being made in oncology, improving prevention and treatment of cancers. To help reduce the impact and deadliness of cancers, they must be detected early. Additionally, there is a risk of cancers recurring after potentially ...

A sequence-to-sequence model-based deep learning approach for recognizing activity of daily living for senior care.

Journal of biomedical informatics
Ensuring the health and safety of independent-living senior citizens is a growing societal concern. Researchers have developed sensor based systems to monitor senior citizens' Activity of Daily Living (ADL), a set of daily activities that can indicat...

A deep learning approach to bilingual lexicon induction in the biomedical domain.

BMC bioinformatics
BACKGROUND: Bilingual lexicon induction (BLI) is an important task in the biomedical domain as translation resources are usually available for general language usage, but are often lacking in domain-specific settings. In this article we consider BLI ...

Using preference learning for detecting inconsistencies in clinical practice guidelines: Methods and application to antibiotherapy.

Artificial intelligence in medicine
Clinical practice guidelines provide evidence-based recommendations. However, many problems are reported, such as contradictions and inconsistencies. For example, guidelines recommend sulfamethoxazole/trimethoprim in child sinusitis, but they also st...

Using data-driven sublanguage pattern mining to induce knowledge models: application in medical image reports knowledge representation.

BMC medical informatics and decision making
BACKGROUND: The use of knowledge models facilitates information retrieval, knowledge base development, and therefore supports new knowledge discovery that ultimately enables decision support applications. Most existing works have employed machine lea...

Extending PubMed searches to ClinicalTrials.gov through a machine learning approach for systematic reviews.

Journal of clinical epidemiology
OBJECTIVES: Despite their essential role in collecting and organizing published medical literature, indexed search engines are unable to cover all relevant knowledge. Hence, current literature recommends the inclusion of clinical trial registries in ...