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Found 12 papers, 1 papers with code
Social Media as a Sensor: Analyzing Twitter Data for Breast Cancer Medication Effects Using Natural Language Processing
This analysis highlighted not only the utility of NLP techniques in unstructured social media data to identify self-reported breast cancer posts, medication usage patterns, and treatment side effects but also the richness of social data on such clinical questions.
Robust Meta-Model for Predicting the Need for Blood Transfusion in Non-traumatic ICU Patients
This study aims to develop an advanced machine learning-based model to predict the probability of transfusion necessity over the next 24 hours for a diverse range of non-traumatic ICU patients.
Detecting algorithmic bias in medical AI-models
With the growing prevalence of machine learning and artificial intelligence-based medical decision support systems, it is equally important to ensure that these systems provide patient outcomes in a fair and equitable fashion.
Mixed-Integer Projections for Automated Data Correction of EMRs Improve Predictions of Sepsis among Hospitalized Patients
In particular, by using high-dimensional mixed-integer programs that capture physiological and biological constraints on patient vitals and lab values, we can harness the power of mathematical "projections" for the EMR data to correct patient data.
Meta-learning in healthcare: A survey
We then divide the employed meta-learning approaches in the healthcare domain into two main categories of multi/single-task learning and many/few-shot learning and survey the studies.
Transfer Learning for Causal Effect Estimation
We present a Transfer Causal Learning (TCL) framework when target and source domains share the same covariate/feature spaces, aiming to improve causal effect estimation accuracy in limited data.
Causal Graph Discovery from Self and Mutually Exciting Time Series
We present a generalized linear structural causal model, coupled with a novel data-adaptive linear regularization, to recover causal directed acyclic graphs (DAGs) from time series.
ALRt: An Active Learning Framework for Irregularly Sampled Temporal Data
Sepsis is a deadly condition affecting many patients in the hospital.
UnfoldML: Cost-Aware and Uncertainty-Based Dynamic 2D Prediction for Multi-Stage Classification
It achieves within 0. 1% accuracy from the highest-performing multi-class baseline, while saving close to 20X on spatio-temporal cost of inference and earlier (3. 5hrs) disease onset prediction.
Granger Causal Chain Discovery for Sepsis-Associated Derangements via Continuous-Time Hawkes Processes
Modern health care systems are conducting continuous, automated surveillance of the electronic medical record (EMR) to identify adverse events with increasing frequency; however, many events such as sepsis do not have elucidated prodromes (i. e., event chains) that can be used to identify and intercept the adverse event early in its course.
Causal Graph Discovery from Self and Mutually Exciting Time Series
We present a generalized linear structural causal model, coupled with a novel data-adaptive linear regularization, to recover causal directed acyclic graphs (DAGs) from time series.
Machine learning predicts early onset of fever from continuous physiological data of critically ill patients
This study aims to develop novel algorithms that can accurately predict fever onset in critically ill patients by applying machine learning technique on continuous physiological data.
