AIMC Topic: Natural Language Processing

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Deep-Learning Language-Modeling Approach for Automated, Personalized, and Iterative Radiology-Pathology Correlation.

Journal of the American College of Radiology : JACR
PURPOSE: Radiology-pathology correlation has long been foundational to continuing education, peer learning, quality assurance, and multidisciplinary patient care. The objective of this study was to determine whether modern deep-learning language-mode...

Supervised methods to extract clinical events from cardiology reports in Italian.

Journal of biomedical informatics
Clinical narratives are a valuable source of information for both patient care and biomedical research. Given the unstructured nature of medical reports, specific automatic techniques are required to extract relevant entities from such texts. In the ...

Detecting adverse drug reactions in discharge summaries of electronic medical records using Readpeer.

International journal of medical informatics
BACKGROUND: Hospital discharge summaries offer a potentially rich resource to enhance pharmacovigilance efforts to evaluate drug safety in real-world clinical practice. However, it is infeasible for experts to read through all discharge summaries to ...

Natural Language Processing for Identification of Incidental Pulmonary Nodules in Radiology Reports.

Journal of the American College of Radiology : JACR
PURPOSE: To develop natural language processing (NLP) to identify incidental lung nodules (ILNs) in radiology reports for assessment of management recommendations.

Similarity corpus on microbial transcriptional regulation.

Journal of biomedical semantics
BACKGROUND: The ability to express the same meaning in different ways is a well-known property of natural language. This amazing property is the source of major difficulties in natural language processing. Given the constant increase in published lit...

A population health perspective on artificial intelligence.

Healthcare management forum
The burgeoning field of Artificial Intelligence (AI) has the potential to profoundly impact the public's health. Yet, to make the most of this opportunity, decision-makers must understand AI concepts. In this article, we describe approaches and field...

Automating Ischemic Stroke Subtype Classification Using Machine Learning and Natural Language Processing.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVE: The manual adjudication of disease classification is time-consuming, error-prone, and limits scaling to large datasets. In ischemic stroke (IS), subtype classification is critical for management and outcome prediction. This study sought to...

Smoothing dense spaces for improved relation extraction between drugs and adverse reactions.

International journal of medical informatics
BACKGROUND AND OBJECTIVE: This work aims at extracting Adverse Drug Reactions (ADRs), i.e. a harm directly caused by a drug at normal doses, from Electronic Health Records (EHRs). The lack of readily available EHRs because of confidentiality issues a...