Publications by authors named "Donald J Apakama"
Background: Healthcare reimbursement and coding is dependent on accurate extraction of International Classification of Diseases-tenth revision - clinical modification (ICD-10-CM) codes from clinical documentation. Attempts to automate this task have had limited success. This study aimed to evaluate the performance of large language models (LLMs) in extracting ICD-10-CM codes from unstructured inpatient notes and benchmark them against human coder.
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- Point-of-care ultrasonography (POCUS) is a bedside cardiac imaging technique, but its effectiveness is hampered by inconsistent protocols and image quality, prompting the development of AI models to enhance cardiomyopathy diagnosis.
- Researchers utilized a massive dataset of transthoracic echocardiographic videos to create an AI model that identifies hypertrophic cardiomyopathy (HCM) and transthyretin amyloid cardiomyopathy (ATTR-CM) from POCUS without prior disease knowledge.
- The AI model demonstrated high accuracy in screening for HCM and ATTR-CM, detecting cases about 2 years prior to clinical diagnoses and showing significant prognostic potential for individuals without known cardiomyopathy.