Machine learning builds personalized pediatric abdominal phantoms.
problem Lack of individualized phantoms for radiation therapy studies.
method Combining imaging and machine learning to automatically build phantoms.
result Automatically constructed phantoms more accurately represent patient anatomy.
Study identifies subphenotypes of pediatric sepsis to improve ML predictive performance.
problem Enhance machine learning predictive performance in pediatric sepsis.
method Latent profile analysis of clinical data to identify subphenotypes, followed by ML experiments.
result Improved predictive performance of ML models targeting specific subphenotypes of pediatric sepsis.
Bayesian model predicts oncology demand trends with high accuracy.
problem Accurate forecasting of oncology demand for resource planning.
method Boosting-based Bayesian conjugate models for Poisson process.
result Model outperforms other methods in trend detection 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.
problem Diagnosing pediatric obstructive sleep apnea using an overnight sleep study is often impractical.
method Combines persistent homology, geometric shape analysis, and convolutional neural networks to classify facial images.
result Facial features associated with obstructive sleep apnea can be recognized for diagnosis.
Survey of techniques for diagnosing pediatric sleep apnea from inexpensive data.
problem Diagnosing pediatric sleep apnea from limited and variable data.
method Exploratory data analysis using correlation networks, Mapper, SVD; supervised and unsupervised learning techniques.
result Analysis of various learning techniques applied to pediatric sleep apnea data.
New framework for managing medical risks using convex responses.
problem Medical risk management and dosing optimization.
method Analyzes convex and concave dose-response functions, defines antifragility.
result Proposes a mathematical framework for integrating nonlinearities in oncology.
Enhances early risk assessments for pediatric outcomes using contrastive learning.
problem Improving risk assessments in early stages of pediatric development.
method Contrastive multi-modal framework that treats each time window as a distinct modality, training on all available data.
result Consistent improvements in early-stage risk assessments validated on real-world tasks.
New CNN architecture improves pediatric image segmentation by homogenizing pose and size.
problem Challenges in segmenting pediatric images due to pose and size heterogeneity.
method Spatial Transformer Network (STN) for pose and scale invariance, combined with UNet for segmentation.
result Improved pediatric segmentation, especially renal tumor delineation, with accelerated processing.
Study benchmarks contextual bandit algorithms for precision oncology using in vitro data.
problem Designing effective protocols for individual treatment assignment in precision oncology.
method Proposed a benchmark dataset of in vitro drug responses to evaluate contextual bandit algorithms.
result Bayesian bandit algorithms performed better than a rule-based baseline in minimizing regret.
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.
problem Improving glioma diagnosis and prognosis using imaging biomarkers.
method Search PubMed and MEDLINE for articles applying machine learning to high-grade glioma biomarkers.
result Machine learning enables accurate classification of glioma biomarkers.
Deep learning predicts asthma ED visits better than traditional methods.
problem Predicting asthma-related ED visits to improve patient management.
method Deep learning (Artificial Neural Networks) compared to Lasso logistic regression.
result Deep learning model (ANN) outperforms traditional Lasso logistic regression (AUC = 0.845 vs. AUC = 0.842).
Deep neural network improves cancer mutation calls with confidence.
problem Improving accuracy and confidence in somatic variant calls from cancer sequencing.
method Deep Bayesian Recurrent Neural Network (RNN) with flexible priors.
result Enhanced confidence in mutation calls without performance degradation.
Bayesian model estimates ACT impact on pediatric AML survival.
problem Estimating ACT impact on survival in AML patients with treatment timing issues.
method Generative Bayesian semi-parametric model with Gamma Process priors.
result Posterior inference for ACT efficacy under dynamic rules.
The CHAMPION study clusters multi-dimensional accelerometer data to understand health links.
problem Clustering multi-dimensional data from pediatric longitudinal studies.
method Developed a finite mixture of multidimensional arrays model for clustering 4-dimensional accelerometer data.
result Demonstrated the feasibility and utility of clustering higher order data.
AI framework uses multi-omics data to personalize cancer treatment suggestions.
problem Leveraging AI for personalized cancer treatment based on complex patient characteristics.
method Modular machine learning framework trained on diverse multi-omics technologies.
result Superior performance in personalized counterfactual 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.
problem Reconstructing detailed 3D dose distributions for childhood cancer survivors using limited 2D radiographs.
method Surrogate-free ML approach using 142 abdominal planning CTs, 300 artificial plans, and evolutionary algorithm.
result Accurate 3D dose reconstructions with MAEs ≤ 1.7 Gy for edge organs, validated on independent dataset.
New methods use RL and DA to improve dosing precision and reduce side effects.
problem Improving precision and safety of anticancer drug dosing.
method Bayesian data assimilation and reinforcement learning.
result Significantly reduces incidence of severe side effects.
Generative AI models improve clinical trial data by generating survival outcomes.
problem Generating valid survival outcomes for clinical trials with synthetic data.
method A variational autoencoder (VAE) that jointly generates mixed-type covariates and survival outcomes.
result The method outperforms GAN baselines on fidelity, utility, and privacy metrics.
Deep Bayesian neural networks improve somatic variant calling accuracy.
problem Improving accuracy in pinpointing somatic variants from next-gen sequencing data.
method Deep Bayesian Recurrent Neural Networks (RNNs) for somatic variant calling.
result Deep Bayesian RNNs provide more reliable confidence intervals for variant calls.
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…
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.
problem Traditional diagnosis of sleep apnea is insufficient for capturing its multi-faceted outcomes.
method Fuzzy clustering in time and frequency domains, and persistent homology for topological analysis.
result Phenotyping patients improves understanding of sleep apnea.
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.
problem Handling censored time-to-event data in healthcare.
method Open-source package for survival regression, adjustment, counterfactual estimation, phenotyping, and treatment effects.
result Demonstrates auton-survival's ability to support complex health and epidemiological questions.
Develops regression trees for estimating cumulative incidence curves in competing risks.
problem Estimating cumulative incidence functions in competing risks settings.
method Uses augmented estimators of the Brier score risk to build and prune regression trees.
result Demonstrates the utility of the proposed methods through simulation studies and real data.
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.
problem Improving survival prediction for HL patients.
method Compared multiple ML algorithms to CoxPH model.
result ML models outperform CoxPH in predicting HL survival.
Proposes dynamic borrowing method for historical data in clinical trials.
problem Insufficient statistical power in rare and pediatric disease clinical trials.
method Dynamic borrowing method based on frequentist approach using similarity measures.
result Demonstrates usefulness of dynamic borrowing in reanalyzing clinical trial data.
Extends HMM to topological spaces for modeling complex data.
problem Modeling complex, continuous data in infinite-dimensional spaces.
method Use of Onsager-Machlup functional and Cameron-Martin space.
result Demonstrates versatility in identifying sleep states and snowfall patterns.
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.
problem Anomaly detection in EEG signals for epilepsy.
method Extends signature theory to detect changes in topological structure of EEG signals.
result Detection of precursor phenomena to epileptic seizures.
New method provides reliable probabilistic bounds for VUR detection.
problem Detect VUR in children without radiation exposure.
method Machine learning with probabilistic bounds for conditional probability.
result Guaranteed bounds contain well-calibrated probabilities.
3D CNN accurately classifies infant neurodevelopmental age from MRI scans.
problem Estimating neurodevelopmental age in infants from MRI data.
method 3D Convolutional Neural Network (3D CNN) trained on MRI images of 112 infants.
result 3D CNN achieves 99% sensitivity and 98.3% specificity in age classification.
Challenge aims to develop automated meningioma MRI segmentation models.
problem Lack of automated, objective tools for meningioma assessment.
method Develop and evaluate models on largest annotated dataset.
result Improved care of patients with meningioma through automated segmentation.
Paper tackles cancer mutation data challenges by creating useful low-dimensional representations.
problem Challenges in analyzing and using cancer mutation data for classification and clustering.
method Flatsomatic: variational autoencoders (VAEs) to create latent representations of somatic profiles.
result VAE embeddings perform better than PCA for clustering and equally well for classification.
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 (100,0002 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.
problem Challenges in achieving valid inferences due to distribution shifts and privacy concerns.
method Derives efficient influence functions, incorporates machine learning, and proposes data-adaptive strategies.
result Achieves parametric rates of convergence to nominal coverage probabilities for prediction intervals.
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.
problem Interim decisions based on current dose data may overlook signals from neighboring doses.
method Bayesian model-assisted design using Beta kernel process with kernel-weighted pseudo-counts.
result Significant improvements in identifying maximum tolerated dose and safety.