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Electronic Health Records

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Developing and testing a framework for coding general practitioners' free-text diagnoses in electronic medical records - a reliability study for generating training data in natural language processing.

BMC primary care
BACKGROUND: Diagnoses entered by general practitioners into electronic medical records have great potential for research and practice, but unfortunately, diagnoses are often in uncoded format, making them of little use. Natural language processing (N...

A transparent machine learning algorithm uncovers HbA1c patterns associated with therapeutic inertia in patients with type 2 diabetes and failure of metformin monotherapy.

International journal of medical informatics
AIMS: This study aimed to identify and categorize the determinants influencing the intensification of therapy in Type 2 Diabetes (T2D) patients with suboptimal blood glucose control despite metformin monotherapy.

Semi-supervised Double Deep Learning Temporal Risk Prediction (SeDDLeR) with Electronic Health Records.

Journal of biomedical informatics
BACKGROUND: Risk prediction plays a crucial role in planning for prevention, monitoring, and treatment. Electronic Health Records (EHRs) offer an expansive repository of temporal medical data encompassing both risk factors and outcome indicators esse...

Optimizing word embeddings for small dataset: a case study on patient portal messages from breast cancer patients.

Scientific reports
Patient portal messages often relate to specific clinical phenomena (e.g., patients undergoing treatment for breast cancer) and, as a result, have received increasing attention in biomedical research. These messages require natural language processin...

Analyzing pain patterns in the emergency department: Leveraging clinical text deep learning models for real-world insights.

International journal of medical informatics
OBJECTIVE: To determine the incidence of patients presenting in pain to a large Australian inner-city emergency department (ED) using a clinical text deep learning algorithm.

Early diagnosis of HIV cases by means of text mining and machine learning models on clinical notes.

Computers in biology and medicine
Undiagnosed and untreated human immunodeficiency virus (HIV) infection increases morbidity in the HIV-positive person and allows onward transmission of the virus. Minimizing missed opportunities for HIV diagnosis when a patient visits a healthcare fa...

Assessing domain adaptation in adverse drug event extraction on real-world breast cancer records.

International journal of medical informatics
BACKGROUND: Adverse Drug Events (ADE) are key information present in unstructured portions of Electronic Health Records. These pose a significant challenge in healthcare, ranging from mild discomfort to severe complications, and can impact patient sa...

Combining Federated Machine Learning and Qualitative Methods to Investigate Novel Pediatric Asthma Subtypes: Protocol for a Mixed Methods Study.

JMIR research protocols
BACKGROUND: Pediatric asthma is a heterogeneous disease; however, current characterizations of its subtypes are limited. Machine learning (ML) methods are well-suited for identifying subtypes. In particular, deep neural networks can learn patient rep...

Revolutionizing urogynecology: Machine learning application with patient-centric technology: Promise, challenges, and future directions.

European journal of obstetrics, gynecology, and reproductive biology
In an epoch where digital innovation is redefining the medical landscape, electronic health records (EHRs) stand out as a pivotal transformative force. Urogynecology, a discipline anchored in intricate patient histories and meticulous follow-ups, is ...