Latest AI and machine learning research in practice management for healthcare professionals.
Postoperative complications (PCs) rates are crucial quality metrics in surgery, as they reflect both patient outcomes, perioperative care effectiveness and healthcare resource strain. Despite their importance, efficient, accurate, and affordable methods for tracking PCs are lacking. This study aimed to evaluate whether natural language processing (NLP) models could detect eleven PCs from surgical ...
Rare diseases collectively affect 5% of the population. However, fewer than 50% of rare disease patients receive a molecular diagnosis after whole genome sequencing. Supervised machine Learning is a valuable approach for the pathogenicity scoring of human genetic variants. However, existing methods are often trained on curated but limited central repositories, resulting in poor accuracy when teste...
Objective: (1) develop and test a novel, open-source, supervised machine learning model to detect toddlers’ physical activity (PA) and sedentary time ...
Significant diagnostic delays are common in primary ciliary dyskinesia (PCD), a rare disease that is significantly underdiagnosed. Scalable screening ...
Prior authorization (PA) rules are neither regulated nor standardized. To quantify the variation in PA rules of four US health insurers and examine th...
Cerebrospinal fluid (CSF) culture is the diagnostic gold standard for neuroinfectious diseases such as bacterial meningitis, but its sensitivity is li...
The identification of non-coding somatic cancer-driver mutations remains challenging due to difficulties in interpreting rare and ultra-rare variants....
Dementia, especially Alzheimer’s disease (AD), is a major global health challenge marked by progressive cognitive impairment, behavioral changes, and ...
Differentiating pseudopapilloedema from papilloedema is challenging, but critical for prompt diagnosis and to avoid unnecessary invasive procedures. F...
Impulse control disorders (ICD) in Parkinson’s disease (PD) patients mainly occur as adverse effects of dopamine replacement therapy. Despite several ...
Manual assignment of International Classification of Diseases (ICD) codes is error-prone. Transformer-based large language models (LLMs) have been pro...
Bicuspid aortic valve (BAV) is the most common congenital heart defect but often evades timely diagnosis due to variable clinical presentations. Prior...
Prompt engineering has the potential to enhance large language models’ (LLM) ability to solve tasks through improved in-context learning. In clinical ...
While large language models (LLMs) have shown promise in medical text processing, their real-world application in self-hosted clinical settings remain...
Motor neuron disease (MND) is a rapidly progressive and fatal neurodegenerative condition, making early diagnosis critical for optimizing patient outc...
Clinical coding is a vital yet complex component of healthcare practice. While automated coding systems have advanced significantly, they still rely o...
Artificial intelligence (AI) is increasingly leveraged in mental healthcare for early detection, monitoring, and personalized intervention. However, m...
Operative notes in electronic health records contain critical information for understanding surgical care, yet manual coding is time-consuming, costly...
A paper from Goh et al found that a large language model (LLM) working alone outperformed American clinicians assisted by the same LLM in diagnostic r...
Understanding the biological processes that precede death is critical for making informed clinical decisions and facilitating care transitions. Here, ...