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Full-Text Articles in Medicine and Health Sciences
Machine Learning By Ultrasonography For Risk Stratification Of Axillary Breast Lymph Nodes, Joshua Yu, John Eisenbrey, Phd, Aylin Tahmasebi, Md
Machine Learning By Ultrasonography For Risk Stratification Of Axillary Breast Lymph Nodes, Joshua Yu, John Eisenbrey, Phd, Aylin Tahmasebi, Md
Phase 1
Introduction: Breast cancer is the most common cancer among women worldwide, and ultrasonography has been an essential tool in the management of breast cancer. In order to improve upon ultrasonography efficacy, establishing a machine learning image analysis model would provide additional prognostic and diagnostic factors in the evaluation, monitoring, and treatment of breast cancer.
Methods: This retrospective study utilizes axillary lymph node ultrasound images of patients at Thomas Jefferson University Hospital who have had axillary lymph node biopsies. Automated machine learning of the images was performed on AutoML Vision; Google LLC which generated custom models for classification. About 80% of …
Readmission Risk Assessment Tool For Stroke Patients, Simran Rahi, Sasha Mitts, Dominick Battistini, Tiffany D’Souza, Bryan Sadler, Krista Mar, Maureen Deprince, Deborah Murphy, Diana Tzeng, Md
Readmission Risk Assessment Tool For Stroke Patients, Simran Rahi, Sasha Mitts, Dominick Battistini, Tiffany D’Souza, Bryan Sadler, Krista Mar, Maureen Deprince, Deborah Murphy, Diana Tzeng, Md
Phase 1
Introduction: Strokes are one of the leading causes of morbidity and mortality in the world and its cost of management has vastly increased; an effective prediction tool that utilizes artificial intelligence to lower the rate of stroke-related readmissions has the potential to lower healthcare costs and increase the quality of provider care. We hypothesize that machine learning techniques are superior to traditional statistics when determining the likelihood of 30-day readmission for Jefferson’s stroke patients.
Methods: Jefferson’s existing data on stroke patients were cleaned, aggregated, and prepared to be split into train and test sets. Using the train sets, machine learning …
Machine Learning Models For 6-Month Survival Prediction After Surgical Resection Of Glioblastoma, Jeffrey Gray, Lohit Velagapudi, Michael Baldassari, Bryan Sadler, David Vuong
Machine Learning Models For 6-Month Survival Prediction After Surgical Resection Of Glioblastoma, Jeffrey Gray, Lohit Velagapudi, Michael Baldassari, Bryan Sadler, David Vuong
Phase 1
Introduction: The role of surgical resection for the treatment of glioblastoma multiforme is well established. Survival analysis after resective surgery in the literature comprises mostly of traditional statistical models. Machine learning models offer powerful predictive and analytical capability for varied datasets and offer improved generalizability and scalability. We analyzed survival data of patients with glioblastoma with various machine learning algorithms and compared it to binary logistic regression.
Methods: We retrospectively identified cases of glioblastoma treated with surgical resection at our institution from 2012-2018. Feature scaling and one-hot encoding was used to better fit the models to the data and used …