AIMC Topic: Natural Language Processing

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A Semi-Automated Approach for Multilingual Terminology Matching: Mapping the French Version of the ICD-10 to the ICD-10 CM.

Studies in health technology and informatics
The aim of this study was to develop a simple method to map the French International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10) with the International Classification of Diseases, 10th Revision, Clinical...

Using word embeddings to improve the privacy of clinical notes.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: In this work, we introduce a privacy technique for anonymizing clinical notes that guarantees all private health information is secured (including sensitive data, such as family history, that are not adequately covered by current technique...

Automated Misspelling Detection and Correction in Persian Clinical Text.

Journal of digital imaging
Accurate electronic health records are important for clinical care, research, and patient safety assurance. Correction of misspelled words is required to ensure the correct interpretation of medical records. In the Persian language, the lack of autom...

Interactive Exploration of Longitudinal Cancer Patient Histories Extracted From Clinical Text.

JCO clinical cancer informatics
PURPOSE: Retrospective cancer research requires identification of patients matching both categorical and temporal inclusion criteria, often on the basis of factors exclusively available in clinical notes. Although natural language processing approach...

Efficient and Accurate Extracting of Unstructured EHRs on Cancer Therapy Responses for the Development of RECIST Natural Language Processing Tools: Part I, the Corpus.

JCO clinical cancer informatics
PURPOSE: Electronic health records (EHRs) are created primarily for nonresearch purposes; thus, the amounts of data are enormous, and the data are crude, heterogeneous, incomplete, and largely unstructured, presenting challenges to effective analyses...

Applications of machine learning methods in kidney disease: hope or hype?

Current opinion in nephrology and hypertension
PURPOSE OF REVIEW: The universal adoption of electronic health records, improvement in technology, and the availability of continuous monitoring has generated large quantities of healthcare data. Machine learning is increasingly adopted by nephrology...

Natural language processing of prehospital emergency medical services trauma records allows for automated characterization of treatment appropriateness.

The journal of trauma and acute care surgery
BACKGROUND: Incomplete prehospital trauma care is a significant contributor to preventable deaths. Current databases lack timelines easily constructible of clinical events. Temporal associations and procedural indications are critical to characterize...

Incorporating natural language processing to improve classification of axial spondyloarthritis using electronic health records.

Rheumatology (Oxford, England)
OBJECTIVES: To develop classification algorithms that accurately identify axial SpA (axSpA) patients in electronic health records, and compare the performance of algorithms incorporating free-text data against approaches using only International Clas...

Identification of Individuals at Increased Risk for Pancreatic Cancer in a Community-Based Cohort of Patients With Suspected Chronic Pancreatitis.

Clinical and translational gastroenterology
OBJECTIVES: We lack reliable methods for identifying patients with chronic pancreatitis (CP) at increased risk for pancreatic cancer. We aimed to identify radiographic parameters associated with pancreatic cancer in this population.

A customizable deep learning model for nosocomial risk prediction from critical care notes with indirect supervision.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Reliable longitudinal risk prediction for hospitalized patients is needed to provide quality care. Our goal is to develop a generalizable model capable of leveraging clinical notes to predict healthcare-associated diseases 24-96 hours in a...