Machine Learning
Every article we have published: research summaries, product thinking and developments in health AI.
Machine Learning Β· 2,184 articles
PharmaBench: Enhancing ADMET benchmarks with large language models.
PharmaBench improves ADMET benchmarks using large language models, enhancing drug development accuracy. π§¬π Comprehensive dataset aids AI model creation.
A Machine Learning Approach for Predicting Biochemical Outcome After PSMA-PET-Guided Salvage Radiotherapy in Recurrent Prostate Cancer After Radical Prostatectomy: Retrospective Study.
A recent study explores machine learning for predicting outcomes in recurrent prostate cancer after salvage radiotherapy. ππ¬
Deep radiomics-based prognostic prediction of oral cancer using optical coherence tomography.
New research highlights the role of optical coherence tomography in predicting oral cancer prognosis using deep learning. ππ¦·
Patient Perspectives on AI for Mental Health Care: Cross-Sectional Survey Study.
A recent PubMed article explores patient views on AI in mental health care. Key findings highlight benefits, concerns, and values. π€π
Using Large Language Models to Detect Depression From User-Generated Diary Text Data as a Novel Approach in Digital Mental Health Screening: Instrument Validation Study.
Large language models can enhance depression detection through diary text analysis. ππ€ Early intervention is crucial for mental health.
Salivary detection of Chikungunya virus infection using a portable and sustainable biophotonic platform coupled with artificial intelligence algorithms.
Salivary detection of Chikungunya virus shows promise. A portable platform with AI algorithms offers non-invasive testing. π¦ π§
Identification of Disulfidptosis-Related Genes in Ischemic Stroke by Combining Single-Cell Sequencing, Machine Learning Algorithms, and In Vitro Experiments.
Recent research highlights disulfidptosis-related genes in ischemic stroke. Key findings reveal their role in immune response and potential therapies. π§ π¬
Classification of cervical vertebral maturation stages with machine learning models: leveraging datasets with high inter- and intra-observer agreement.
Machine learning models effectively classify cervical vertebral maturation stages, achieving up to 77.4% accuracy. ππ¦΄
Machine Learning-Based Approach for Identifying Research Gaps: COVID-19 as a Case Study.
A machine learning approach identifies COVID-19 research gaps, highlighting six key topics for future exploration. ππ
Enhancing reginal wall abnormality detection accuracy: Integrating machine learning, optical flow algorithms, and temporal convolutional networks in multi-view echocardiography.
Recent research enhances RWMA detection using machine learning and multi-view echocardiography. Promising results for early MI diagnosis! π«π
Supporting Trustworthy AI Through Machine Unlearning.
Machine unlearning can enhance trustworthy AI by supporting ethical principles. However, it poses ethical risks that need addressing. π€π
Fall risk prediction using temporal gait features and machine learning approaches.
AI enhances fall risk prediction through gait analysis. π€πΆββοΈ Promising results with 96% accuracy using machine learning techniques. π
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