View clinical trials related to Patient Outcome Assessment.
Filter by:The goal of this study is to measure the impact of fairness-aware algorithms on physician predictions of ED patient admission. Using an experimentally validated machine learning model tuned for equitable outcomes, the investigators quantify the impact of model recommendations on ED physician assessments of admission risk in a silent, prospective study. The investigators survey ED physicians who are not currently caring for patients using live site data. To quantify the impact of the model on ED physician assessments of admission risk, the investigators collect physician assessments before and after consulting the (original or updated) model prediction. The investigators measure ED physician adherence to model suggestions, along with the predictive accuracy and equity of downstream patient outcomes. The outcome of this study is an empirical measure of the extent to which fair ML models may influence admission decisions to mitigate health care disparities.
This study has two stages and the aims are as follows: Aim 1: In Stage 1 of this study, the investigators aim to recruit first-time diagnosed lymphoma patients, to understand the changes of metabolites before and after treatment, and to evaluate the ability of hyperpolarized 13C-labeled pyruvate from dynamic nuclear polarization (DNP) magnetic resonance spectroscopy (MRS) for detecting early treatment response in these patients. The pre-treatment metabolic imaging biomarker levels will be compared to the followings: 1. Post-treatment metabolites from 13C-pyruvate DNP MRS after the first week of chemotherapy 2. Interval change in tumor size 3. ADC values from diffusion weighted imaging (DWI), SUV values from 18F-FDG PET/CT before and after the first week of chemotherapy 4. Pre-treatment and interim follow up SUV values from 18F-FDG PET/CT 5. Post-treatment outcome and to understand the change of metabolites before and after treatment and if possible, evaluate treatment outcome using the above imaging biomarkers Aim 2: In Stage 2 of this study, the investigators aim to recruit lymphoma patients with proven relapse after treatment, to understand the changes of metabolites before and after treatment, to compare the metabolite changes with Stage 1 patients and to evaluate the ability of hyperpolarized 13C-labeled pyruvate from DNP MRS for detecting early treatment response in these patients.