A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Novel IRL method identifies suboptimal medical decisions in ICU data.
Causal ML predicts treatment outcomes, aiding personalized medicine.
Pairwise ranking aligns subjective clinical evaluations with objective indicators.
The paper integrates AI and expert knowledge to optimize radiotherapy decisions.
Framework improves health by planning actionable treatment processes.
FRESH combines patient-level and aggregate-level data for better clinical decision making.
Unified framework for human-like decision making in various sequential tasks.
Examines fairness in ML for health, highlighting its importance and challenges.
Machine learning is bringing a paradigm shift to healthcare by changing the process of disease diagnosis and prognosis in clinics and hospitals. This development equips doctors and medical staff with tools to evaluate their hypotheses and hence make more precise decisions. Although most current research in the literatu…
Hybrid Bayesian-conformal framework improves uncertainty quantification in healthcare predictions.
Paper uses ML to classify liver diseases from clinical data.
Method predicts biomarker trajectories with uncertainty bands for Alzheimer's disease.
Optimizes treatment duration to maximize quality-adjusted lifetime.
System predicts respiratory failure up to 8 hours early.
Exponential growth in Electronic Healthcare Records (EHR) has resulted in new opportunities and urgent needs for discovery of meaningful data-driven representations and patterns of diseases in Computational Phenotyping research. Deep Learning models have shown superior performance for robust prediction in computational…
Dynamic treatment regimes are of growing interest across the clinical sciences as these regimes provide one way to operationalize and thus inform sequential personalized clinical decision making. A dynamic treatment regime is a sequence of decision rules, with a decision rule per stage of clinical intervention; each de…
CASCADE improves uncertainty communication in Parkinson's disease medication management.
CoI framework models clinical feature interactions, revealing temporal dependencies and enhancing transparency.
The paper studies how noisy labels impact decision-making in machine learning.
The method learns to partition event time space for better prediction.
Clinical decision making is challenging because of pathological complexity, as well as large amounts of heterogeneous data generated as part of routine clinical care. In recent years, machine learning tools have been developed to aid this process. Intensive care unit (ICU) admissions represent the most data dense and t…
Review of methods enabling causal predictions under hypothetical interventions.
The morphometry of a kidney tumor revealed by contrast-enhanced Computed Tomography (CT) imaging is an important factor in clinical decision making surrounding the lesion's diagnosis and treatment. Quantitative study of the relationship between kidney tumor morphology and clinical outcomes is difficult due to data scar…
Machine learning aids in diagnosing Parkinson's disease with higher accuracy.
In this work, we investigate unsupervised representation learning on medical time series, which bears the promise of leveraging copious amounts of existing unlabeled data in order to eventually assist clinical decision making. By evaluating on the prediction of clinically relevant outcomes, we show that in a practical …
Decision making from data involves identifying a set of attributes that contribute to effective decision making through computational intelligence. The presence of missing values greatly influences the selection of right set of attributes and this renders degradation in classification accuracies of the classifiers. As …
Batched Neural Bandits reduces policy updates in sequential decision-making.
Clinical decision support systems (CDSS) will play an in-creasing role in improving the quality of medical care for critically ill patients. However, due to limitations in current informatics infrastructure, CDSS do not always have com-plete information on state of supporting physiologic monitor-ing devices, which can …
Owe to the recent advancements in Artificial Intelligence especially deep learning, many data-driven decision support systems have been implemented to facilitate medical doctors in delivering personalized care. We focus on the deep reinforcement learning (DRL) models in this paper. DRL models have demonstrated human-le…
Deep learning, an area of machine learning, is set to revolutionize patient care. But it is not yet part of standard of care, especially when it comes to individual patient care. In fact, it is unclear to what extent data-driven techniques are being used to support clinical decision making (CDS). Heretofore, there has …
Patient journeys are compared to find clusters of similar disease trajectories.
Method predicts ODX scores for breast cancer patients based on clinical data.
In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (…
PKB framework boosts genomic data analysis by integrating pathway knowledge.
The paper introduces a new algorithm for fair decision-making in outcome control tasks.
Much attention has been devoted recently to the development of machine learning algorithms with the goal of improving treatment policies in healthcare. Reinforcement learning (RL) is a sub-field within machine learning that is concerned with learning how to make sequences of decisions so as to optimize long-term effect…
AI predicts medical specialty diagnostic choices from EHR records.
Precision oncology, the genetic sequencing of tumors to identify druggable targets, has emerged as the standard of care in the treatment of many cancers. Nonetheless, due to the pace of therapy development and variability in patient information, designing effective protocols for individual treatment assignment in a sam…
Analyzes COVID-19 data to predict mortality, forecast spread, and optimize resource allocation.
AI enhances personalized drug development and decision-making in pharma.
Drawing an inspiration from behavioral studies of human decision making, we propose here a more general and flexible parametric framework for reinforcement learning that extends standard Q-learning to a two-stream model for processing positive and negative rewards, and allows to incorporate a wide range of reward-proce…
Estimation of individual treatment effects is commonly used as the basis for contextual decision making in fields such as healthcare, education, and economics. However, it is often sufficient for the decision maker to have estimates of upper and lower bounds on the potential outcomes of decision alternatives to assess …
PNNs improve personalized healthcare policies using mixed integer programming.
FIGS improves prediction performance while maintaining interpretability, especially in medical domains.
Variable selection for optimal treatment regime in a clinical trial or an observational study is getting more attention. Most existing variable selection techniques focused on selecting variables that are important for prediction, therefore some variables that are poor in prediction but are critical for decision-making…
Study predicts antimicrobial resistance in ICU patients quickly.
Enforcing safety is a key aspect of many problems pertaining to sequential decision making under uncertainty, which require the decisions made at every step to be both informative of the optimal decision and also safe. For example, we value both efficacy and comfort in medical therapy, and efficiency and safety in robo…
Paper uses stats to predict treatment choice based on illness probability.