Assessing Total Hip Arthroplasty Outcomes and Generating an Orthopedic Research Outcome Database via a Natural Language Processing Pipeline: Development and Validation Study.

Background: Processing data from electronic health records (EHRs) to build research-grade databases is a lengthy and expensive process. Modern arthroplasty practice commonly uses multiple sites of care, including clinics and ambulatory care centers. However, most private data systems prevent obtaining usable insights for clinical practice.

eGFR slope as predictor of mortality in heart failure patients.

Aims: Heart failure (HF) leads to an imbalance between heart and kidney function, resulting in poor outcomes. However, the prognostic significance of the estimated glomerular filtration rate (eGFR) trajectory in HF patients remains unclear. We analysed electronic health records (EHRs) of real-world HF patients, assessing eGFR trajectories and their impact on mortality.

Detection of transthyretin amyloid cardiomyopathy by automated data extraction from electronic health records.

Aims: Transthyretin amyloid cardiomyopathy (ATTR-CM), a progressive and fatal cardiomyopathy, is frequently misdiagnosed or entails diagnostic delays, hindering patients from timely treatment. This study aimed to generate a systematic framework based on data from electronic health records (EHRs) to assess patients with ATTR-CM in a real-world population of heart failure (HF) patients. Predictive factors or combinations of predictive factors related to ATTR-CM in a European population were also assessed.

Validation of an Artificial Intelligence driven framework to automatically detect red flag symptoms in screening for rare diseases in electronic health records: hereditary transthyretin amyloidosis polyneuropathy as a key example.

Rare life-threatening conditions, such as multisystemic hereditary transthyretin amyloidosis (ATTRv) polyneuropathy, are often underdiagnosed or diagnosed late in the disease course, although early diagnosis is crucial for treatment success. Red flag symptoms have been identified, but manual screening of multidisciplinary medical records on this set of symptoms is time-consuming. This study aimed to validate a Natural Language Processing (NLP) algorithm to perform such a search in an automated manner, in order to improve early diagnosis and treatment.