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Found 14 papers, 3 papers with code
Forecasting from Clinical Textual Time Series: Adaptations of the Encoder and Decoder Language Model Families
In this work, we introduce the forecasting problem from textual time series, where timestamped clinical findings -- extracted via an LLM-assisted annotation pipeline -- serve as the primary input for prediction.
Reconstructing Sepsis Trajectories from Clinical Case Reports using LLMs: the Textual Time Series Corpus for Sepsis
Clinical case reports and discharge summaries may be the most complete and accurate summarization of patient encounters, yet they are finalized, i. e., timestamped after the encounter.
Temporal Supervised Contrastive Learning for Modeling Patient Risk Progression
To solve this problem, we propose a supervised contrastive learning framework that learns an embedding representation for each time step of a patient time series.
Fair Decision-making Under Uncertainty
There has been concern within the artificial intelligence (AI) community and the broader society regarding the potential lack of fairness of AI-based decision-making systems.
Learning Clinical Concepts for Predicting Risk of Progression to Severe COVID-19
With COVID-19 now pervasive, identification of high-risk individuals is crucial.
Longitudinal Fairness with Censorship
Recent works in artificial intelligence fairness attempt to mitigate discrimination by proposing constrained optimization programs that achieve parity for some fairness statistic.
Fairness Amidst Non-IID Graph Data: A Literature Review
The growing importance of understanding and addressing algorithmic bias in artificial intelligence (AI) has led to a surge in research on AI fairness, which often assumes that the underlying data is independent and identically distributed (IID).
FARF: A Fair and Adaptive Random Forests Classifier
This algorithm, called FARF (Fair and Adaptive Random Forests), is based on using online component classifiers and updating them according to the current distribution, that also accounts for fairness and a single hyperparameters that alters fairness-accuracy balance.
Neural Topic Models with Survival Supervision: Jointly Predicting Time-to-Event Outcomes and Learning How Clinical Features Relate
We present a neural network framework for learning a survival model to predict a time-to-event outcome while simultaneously learning a topic model that reveals feature relationships.
Predicting Mortality Risk in Viral and Unspecified Pneumonia to Assist Clinicians with COVID-19 ECMO Planning
Finally, the PEER score is provided in the form of a nomogram for direct calculation of patient risk, and can be used to highlight at-risk patients among critical care patients eligible for ECMO.
Topic Models with Survival Supervision: Archetypal Analysis and Neural Approaches
The two approaches we propose differ in the generality of topic models they can learn.
Harmonic Mean Point Processes: Proportional Rate Error Minimization for Obtundation Prediction
We focus on this problem in point processes, a popular modeling technique for the analysis of the temporal event sequences in electronic health records (EHR) data with applications in risk stratification and risk score systems.
Clinical Risk: wavelet reconstruction networks for marked point processes
Timestamped sequences of events, pervasive in domains with data logs, e. g., health records, are often modeled as point processes with rate functions over time.
Survival-Supervised Topic Modeling with Anchor Words: Characterizing Pancreatitis Outcomes
For example, by seeing "gallstones" in a document, we are fairly certain that the document is partially about medicine.
