AIMC Topic: Electronic Health Records

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A Trust-Aware Architecture for Personalized Digital Health: Integrating Blueprint Personas and Ontology-Based Reasoning.

Journal of medical systems
This paper presents a trust-aware architecture for personalized digital health that combines user modeling, symbolic reasoning, and adaptive trust mechanisms. The proposed system uses Blueprint Personas to capture detailed patient profiles, including...

Testing regular expression searches and machine learning models to determine housing instability and low income status from primary care electronic medical record data in Toronto, Ontario.

BMC public health
BACKGROUND: Housing and income are important social determinants of health (SDoH). Primary care providers often do not have information about these determinants, which could be used to support equitable health system planning and care delivery. The a...

Clinical Risk Computation by Large Language Models Using Validated Risk Scores.

Journal of medical systems
Recent advances in artificial intelligence have propelled Large Language Models (LLMs) in natural language understanding, enabling new healthcare applications. While LLMs can analyze health data, directly predicting patient risk scores can be unrelia...

The Impact of Comorbidity Patterns on Clinical Outcomes in Heart Failure: A Machine Learning-Based Cluster Analysis.

The American journal of cardiology
Heart failure (HF) is a major global health burden, and complex comorbidity patterns can worsen clinical outcomes and complicate patient care. This study aimed to identify distinct comorbidity-based clusters among HF patients and evaluate their assoc...

InfEHR: Clinical phenotype resolution through deep geometric learning on electronic health records.

Nature communications
Electronic health records contain multimodal data that can inform clinical decisions but are often unsuited for advanced machine learning analyses due to lack of labeled data. Here, we present InfEHR, a framework to automatically compute clinical lik...

Identifying and Reducing Stigmatizing Language in Home Health Care With a Natural Language Processing-Based System (ENGAGE): Protocol for a Mixed Methods Study.

JMIR research protocols
BACKGROUND: Stigmatizing language is common in clinical notes and can adversely affect the quality of patient care. Natural language processing (NLP) is a promising technology for identifying such language across large volumes of clinical notes in el...

Harnessing the Power of Technology to Transform Delirium Severity Measurement in the Intensive Care Unit: Protocol for a Prospective Cohort Study.

JMIR research protocols
BACKGROUND: Delirium, an acute brain dysfunction, is a complication in up to 50% of patients in the intensive care unit (ICU). Measuring and mitigating delirium severity can reduce associated morbidity and improve long-term health outcomes post disch...

Mortality prediction for ICU patients with mental disorders using large language models ensemble and unstructured medical notes.

PloS one
Assessing mortality risk in the intensive care unit (ICU) is crucial for improving clinical outcomes and management strategies. Conventional artificial intelligence studies often neglect vital clinical information contained in unstructured medical no...

Predicting Unplanned Readmission Risk in Patients With Cirrhosis: Complication-Aware Dynamic Classifier Selection Approach.

JMIR medical informatics
BACKGROUND: Cirrhosis is a leading cause of noncancer deaths in gastrointestinal diseases, resulting in high hospitalization and readmission rates. Early identification of high-risk patients is vital for proactive interventions and improving health c...