AI Medical Compendium Topic:
Electronic Health Records

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Automated Mapping of Real-world Oncology Laboratory Data to LOINC.

AMIA ... Annual Symposium proceedings. AMIA Symposium
In this study we seek to determine the efficacy of using automated mapping methods to reduce the manual mapping burden of laboratory data to LOINC(r) on a nationwide electronic health record derived oncology specific dataset. We developed novel encod...

Towards more patient friendly clinical notes through language models and ontologies.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Clinical notes are an efficient way to record patient information but are notoriously hard to decipher for non-experts. Automatically simplifying medical text can empower patients with valuable information about their health, while saving clinicians ...

Understanding Heart Failure Patients EHR Clinical Features via SHAP Interpretation of Tree-Based Machine Learning Model Predictions.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Heart failure (HF) is a major cause of mortality. Accurately monitoring HF progress and adjusting therapies are critical for improving patient outcomes. An experienced cardiologist can make accurate HF stage diagnoses based on combination of symptoms...

Extraction of Active Medications and Adherence Using Natural Language Processing for Glaucoma Patients.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Accuracy of medication data in electronic health records (EHRs) is crucial for patient care and research, but many studies have shown that medication lists frequently contain errors. In contrast, physicians often pay more attention to the clinical no...

Mixed-methods evaluation of three natural language processing modeling approaches for measuring documented goals-of-care discussions in the electronic health record.

Journal of pain and symptom management
CONTEXT: Documented goals-of-care discussions are an important quality metric for patients with serious illness. Natural language processing (NLP) is a promising approach for identifying goals-of-care discussions in the electronic health record (EHR)...

Structure-aware siamese graph neural networks for encounter-level patient similarity learning.

Journal of biomedical informatics
Patient similarity learning has attracted great research interest in biomedical informatics. Correctly identifying the similarity between a given patient and patient records in the database could contribute to clinical references for diagnosis and me...

Can natural language processing models extract and classify instances of interpersonal violence in mental healthcare electronic records: an applied evaluative study.

BMJ open
OBJECTIVE: This paper evaluates the application of a natural language processing (NLP) model for extracting clinical text referring to interpersonal violence using electronic health records (EHRs) from a large mental healthcare provider.

Deep learning model for multi-classification of infectious diseases from unstructured electronic medical records.

BMC medical informatics and decision making
PURPOSE: Predictively diagnosing infectious diseases helps in providing better treatment and enhances the prevention and control of such diseases. This study uses actual data from a hospital. A multiple infectious disease diagnostic model (MIDDM) is ...