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.
Machine learning builds personalized pediatric abdominal phantoms.
Study identifies subphenotypes of pediatric sepsis to improve ML predictive performance.
Bayesian model predicts oncology demand trends with high accuracy.
Our work focuses on the problem of predicting the transfer of pediatric patients from the general ward of a hospital to the pediatric intensive care unit. Using data collected over 5.5 years from the electronic health records of two medical facilities, we develop classifiers based on adaptive boosting and gradient tree…
Develops a fast non-invasive tool for diagnosing pediatric sleep apnea.
Survey of techniques for diagnosing pediatric sleep apnea from inexpensive data.
New framework for managing medical risks using convex responses.
Enhances early risk assessments for pediatric outcomes using contrastive learning.
New CNN architecture improves pediatric image segmentation by homogenizing pose and size.
Study benchmarks contextual bandit algorithms for precision oncology using in vitro data.
Viewing the trajectory of a patient as a dynamical system, a recurrent neural network was developed to learn the course of patient encounters in the Pediatric Intensive Care Unit (PICU) of a major tertiary care center. Data extracted from Electronic Medical Records (EMR) of about 12000 patients who were admitted to the…
Machine learning improves glioma diagnosis and prognosis.
Deep learning predicts asthma ED visits better than traditional methods.
Deep neural network improves cancer mutation calls with confidence.
Bayesian model estimates ACT impact on pediatric AML survival.
AI framework uses multi-omics data to personalize cancer treatment suggestions.
Over the past decades, both critical care and cancer care have improved substantially. Due to increased cancer-specific survival, we hypothesized that both the number of cancer patients admitted to the ICU and overall survival have increased since the millennium change. MIMIC-III, a freely accessible critical care data…
Objective: Predict patient-specific vitals deemed medically acceptable for discharge from a pediatric intensive care unit (ICU). Design: The means of each patient's hr, sbp and dbp measurements between their medical and physical discharge from the ICU were computed as a proxy for their physiologically acceptable state …
A ML-based method reconstructs 3D organ doses from 2D radiographs for pediatric abdominal radiotherapy.
New methods use RL and DA to improve dosing precision and reduce side effects.
Generative AI models improve clinical trial data by generating survival outcomes.
Deep Bayesian neural networks improve somatic variant calling accuracy.
Objective: Predict individual septic children's personalized physiologic responses to vasoactive titrations by training a Recurrent Neural Network (RNN) using EMR data. Materials and Methods: This study retrospectively analyzed EMR of patients admitted to a pediatric ICU from 2009 to 2017. Data included charted time se…
We demonstrate a simple strategy to cope with missing data in sequential inputs, addressing the task of multilabel classification of diagnoses given clinical time series. Collected from the pediatric intensive care unit (PICU) at Children's Hospital Los Angeles, our data consists of multivariate time series of observat…
Physical activity levels are an important predictor of cardiovascular health and increasingly being measured by sensors, like accelerometers. Accelerometers produce rich multivariate data that can inform important clinical decisions related to individual patients and public health. The CHAMPION study, a study of youth …
There is growing interest in applying machine learning methods to Electronic Medical Records (EMR). Across different institutions, however, EMR quality can vary widely. This work investigated the impact of this disparity on the performance of three advanced machine learning algorithms: logistic regression, multilayer p…
We have applied a little-known data transformation to subsets of the Surveillance, Epidemiology, and End Results (SEER) publically available data of the National Cancer Institute (NCI) to make it suitable input to standard machine learning classifiers. This transformation properly treats the right-censored data in the …
New method phenotypes sleep apnea patients using time series analysis.
Kidney function evaluation using dynamic contrast-enhanced MRI (DCE-MRI) images could help in diagnosis and treatment of kidney diseases of children. Automatic segmentation of renal parenchyma is an important step in this process. In this paper, we propose a time and memory efficient fully automated segmentation method…
Reinforcement learning algorithms are gaining popularity in fields in which optimal scheduling is important, and oncology is not an exception. The complex and uncertain dynamics of cancer limit the performance of traditional model-based scheduling strategies like Optimal Control. Motivated by the recent success of mode…
auton-survival simplifies survival analysis for healthcare data.
Develops regression trees for estimating cumulative incidence curves in competing risks.
The ability to accurately classify disease subtypes is of vital importance, especially in oncology where this capability could have a life saving impact. Here we report a classification between two subtypes of non-small cell lung cancer, namely Adeno- carcinoma vs Squamous cell carcinoma. The data consists of approxima…
Study uses ML to predict HL survival, outperforming CoxPH.
Proposes dynamic borrowing method for historical data in clinical trials.
Extends HMM to topological spaces for modeling complex data.
Flow cytometry is often used to characterize the malignant cells in leukemia and lymphoma patients, traced to the level of the individual cell. Typically, flow cytometric data analysis is performed through a series of 2-dimensional projections onto the axes of the data set. Through the years, clinicians have determined…
Detects changes in brain signal topology to predict epileptic seizures.
New method provides reliable probabilistic bounds for VUR detection.
3D CNN accurately classifies infant neurodevelopmental age from MRI scans.
Challenge aims to develop automated meningioma MRI segmentation models.
Paper tackles cancer mutation data challenges by creating useful low-dimensional representations.
Analysis of histopathology slides is a critical step for many diagnoses, and in particular in oncology where it defines the gold standard. In the case of digital histopathological analysis, highly trained pathologists must review vast whole-slide-images of extreme digital resolution ( pixels) across multiple…
Graphical models have gained a lot of attention recently as a tool for learning and representing dependencies among variables in multivariate data. Often, domain scientists are looking specifically for differences among the dependency networks of different conditions or populations (e.g. differences between regulatory …
The paper tackles distribution-free prediction intervals for multi-source data.
Despite great advances, molecular cancer pathology is often limited to the use of a small number of biomarkers rather than the whole transcriptome, partly due to computational challenges. Here, we introduce a novel architecture of Deep Neural Networks (DNNs) that is capable of simultaneous inference of various properti…
Magnetic resonance image (MRI) reconstruction is a severely ill-posed linear inverse task demanding time and resource intensive computations that can substantially trade off {\it accuracy} for {\it speed} in real-time imaging. In addition, state-of-the-art compressed sensing (CS) analytics are not cognizant of the imag…
Shared Keyboard design improves phase I clinical trials by borrowing information across doses.