AIMC Topic: Electronic Health Records

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Automatic Extraction of Risk Factors for Dialysis Patients from Clinical Notes Using Natural Language Processing Techniques.

Studies in health technology and informatics
Studies have shown that mental health and comorbidities such as dementia, diabetes and cardiovascular diseases are risk factors for dialysis patients. Extracting accurate and timely information associated with these risk factors in the patient health...

A Semantic Similarity Evaluation for Healthcare Ontologies Matching to HL7 FHIR Resources.

Studies in health technology and informatics
Healthcare 4.0 demands healthcare data to be shaped into a common standardized and interoperable format for achieving more efficient data exchange. Most of the techniques addressing this domain are dealing only with specific cases of data transformat...

[Digital data for more efficient prevention: ethical and legal considerations regarding potentials and risks].

Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz
Digitization offers considerable potential for strengthening prevention in the healthcare system. Data from various clinical and nonclinical sources can be collected in a structured way and systematically processed using algorithms. Prevention needs ...

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...

Development and validation of phenotype classifiers across multiple sites in the observational health data sciences and informatics network.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Accurate electronic phenotyping is essential to support collaborative observational research. Supervised machine learning methods can be used to train phenotype classifiers in a high-throughput manner using imperfectly labeled data. We dev...

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...

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...

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...