AIMC Topic: Natural Language Processing

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Machine Learning for Automation of Radiology Protocols for Quality and Efficiency Improvement.

Journal of the American College of Radiology : JACR
PURPOSE: The aim of this study was to enhance multispecialty CT and MRI protocol assignment quality and efficiency through development, testing, and proposed workflow design of a natural language processing (NLP)-based machine learning classifier.

Natural Language Processing to Extract Meaningful Information from Patient Experience Feedback.

Applied clinical informatics
BACKGROUND: Due to reimbursement tied in part to patients' perception of their care, hospitals continue to stress obtaining patient feedback and understanding it to plan interventions to improve patients' experience. We demonstrate the use of natural...

Extracting Structured Genotype Information from Free-Text HLA Reports Using a Rule-Based Approach.

Journal of Korean medical science
BACKGROUND: Human leukocyte antigen (HLA) typing is important for transplant patients to prevent a severe mismatch reaction, and the result can also support the diagnosis of various disease or prediction of drug side effects. However, such secondary ...

Optic Disc and Cup Image Segmentation Utilizing Contour-Based Transformation and Sequence Labeling Networks.

Journal of medical systems
Optic disc (OD) and optic cup (OC) segmentation are important steps for automatic screening and diagnosing of optic nerve head abnormalities such as glaucoma. Many recent works formulated the OD and OC segmentation as a pixel classification task. How...

Early short-term prediction of emergency department length of stay using natural language processing for low-acuity outpatients.

The American journal of emergency medicine
BACKGROUND: Low-acuity outpatients constitute the majority of emergency department (ED) patients, and these patients often experience an unpredictable length of stay (LOS). Effective LOS prediction might improve the quality of ED care and reduce ED c...

Matching patients to clinical trials using semantically enriched document representation.

Journal of biomedical informatics
Recruiting eligible patients for clinical trials is crucial for reliably answering specific questions about medical interventions and evaluation. However, clinical trial recruitment is a bottleneck in clinical research and drug development. Our goal ...

Understanding Patient Anxieties in the Social Media Era: Qualitative Analysis and Natural Language Processing of an Online Male Infertility Community.

Journal of medical Internet research
BACKGROUND: Couples struggling with infertility are increasingly turning to the internet for infertility-related content and to connect with others. Most of the published data on infertility and the internet only address the experiences of women, wit...

Natural Language Processing for Mimicking Clinical Trial Recruitment in Critical Care: A Semi-Automated Simulation Based on the LeoPARDS Trial.

IEEE journal of biomedical and health informatics
Clinical trials often fail to recruit an adequate number of appropriate patients. Identifying eligible trial participants is resource-intensive when relying on manual review of clinical notes, particularly in critical care settings where the time win...

Concept based auto-assignment of healthcare questions to domain experts in online Q&A communities.

International journal of medical informatics
BACKGROUND: Healthcare consumers are increasingly turning to the online health Q&A communities to seek answers for their questions because current general search engines are unable to digest complex health-related questions. Q&A communities are platf...

Evaluating sentence representations for biomedical text: Methods and experimental results.

Journal of biomedical informatics
Text representations ar one of the main inputs to various Natural Language Processing (NLP) methods. Given the fast developmental pace of new sentence embedding methods, we argue that there is a need for a unified methodology to assess these differen...