BACKGROUND: Machine learning (ML) has emerged as a vital asset for researchers to analyze and extract valuable information from complex datasets. However, developing an effective and robust ML pipeline can present a real challenge, demanding consider...
SUMMARY: We present Coracle, an artificial intelligence (AI) framework that can identify associations between bacterial communities and continuous variables. Coracle uses an ensemble approach of prominent feature selection methods and machine learnin...
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 1, 2024
Annotated language resources are essential for supervised machine learning methods. In the clinical domain, such data sets can boost use-case specific natural language processing services. In this work, we have analyzed a clinical problem list table ...
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 1, 2024
Antibiotics have been crucial in advancing medical treatments, but the growing threat of antibiotic resistance challenges these achievements and emphasizes the need for innovative stewardship strategies. In this study, we developed machine learning m...
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 1, 2024
This study evaluates the utility of machine learning (ML) algorithms in early forecasting of total symptom score changes from daily self-reports of 339 chemotherapy patients. The dataset comprised 12 specific symptoms, with severity and distress for ...
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 1, 2024
Antimicrobial resistance is a significant public health concern. The use of selective serotonin reuptake inhibitors (SSRIs), medications commonly prescribed to treat depression, anxiety, and other psychiatric disorders, is increasing. Previous in vit...
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 1, 2024
This study introduces a groundbreaking approach to early cancer detection through the analysis of cell-free DNA (cfDNA), utilizing machine learning algorithms to navigate the complexities of low circulating tumor DNA (ctDNA) fractions and genetic het...
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 1, 2024
To aid in the transparency of state-of-the-art machine learning models, there has been considerable research performed in uncertainty quantification (UQ). UQ aims to quantify what a model does not know by measuring variation of the model under stocha...
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 1, 2024
Integrating Electronic Health Records (EHR) and the application of machine learning present opportunities for enhancing the accuracy and accessibility of data-driven diabetes prediction. In particular, developing data-driven machine learning models c...
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 1, 2024
Electronic Health Records (EHRs) are valuable healthcare data, aiding researchers and doctors in improving diagnosis accuracy. Researchers have developed several predictive models by learning disease representations to forecast the potential diagnosi...
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