Probabilistic Boolean tensor decomposition improves accuracy and scalability.
problem Approximating multi-way binary data with interpretable low-rank factors.
method Scalable sampling-based posterior inference exploiting combinatorial structure.
result Maximum a posteriori decompositions outperform existing techniques.
Machine learning improves early detection of patient deterioration in Brazilian hospitals.
problem Challenges in recognizing clinical deterioration in hospital settings.
method Application of machine learning to analyze EHR data from multiple hospitals.
result Machine learning models outperformed traditional protocols by 25 percentage points in AUC.
Bayesian model forecasts hospital resource use during pandemic.
problem Modeling constrained hospital resources during pandemic.
method Approximate Bayesian Computation for an ACED-HMM.
result Mechanistic approach provides competitive probabilistic forecasts.
SONMF reduces ED crowding by predicting patient dispositions from triage notes.
problem Crowded Emergency Departments and delayed patient admissions.
method Semi-orthogonal Non-negative Matrix Factorization (SONMF) for text mining.
result SONMF improves classification accuracy and interpretability of patient notes.
The Ward error sum of squares hierarchical clustering method has been very widely used since its first description by Ward in a 1963 publication. It has also been generalized in various ways. However there are different interpretations in the literature and there are different implementations of the Ward agglomerative …
The Ward equation, also called the modified 2+1 chiral model, is obtained by a dimension reduction and a gauge fixing from the self-dual Yang-Mills field equation on R2,2. It has a Lax pair and is an integrable system. Ward constructed solitons whose extended solutions have distinct simple poles. He also used a li…
In this paper we make two novel contributions to hierarchical clustering. First, we introduce an anomalous pattern initialisation method for hierarchical clustering algorithms, called A-Ward, capable of substantially reducing the time they take to converge. This method generates an initial partition with a sufficiently…
Conditions for Penrose-Ward transformation on specific manifolds.
problem Conditions for Penrose-Ward transformation on almost G2-manifolds with almost twistorial structures. method Necessary and sufficient conditions derived through Penrose-Ward transformation.
result Conditions for Penrose-Ward transformation on almost G2-manifolds with almost twistorial structures. The moduli space of static finite energy solutions to Ward's integrable chiral model is the space MN of based rational maps from $\CP^1$ to itself with degree N. The Lagrangian of Ward's model gives rise to a Kähler metric and a magnetic vector potential on this space. However, the magnetic field strength vanishes…
Deep neural network predicts diabetic readmission with high accuracy.
problem Predicting 30-day readmission for diabetic patients.
method Categorical embeddings and deep neural network.
result 95.2% accuracy and 97.4% AUROC on diabetic readmission data.
Study predicts future hospitalizations to manage COVID-19 patient surge.
problem Managing hospitalization needs during COVID-19 pandemic.
method Used 4 recurrent neural networks to predict hospitalization changes.
result Sequence to sequence model with attention achieved high accuracy and AUC.
Robust policies improve ICU transfer outcomes by predicting patient deterioration.
problem Higher mortality rates for unplanned ICU transfers.
method Markov Decision Process model to predict patient severity and optimize transfer policies.
result Robust policies are more aggressive in transferring patients than nominal policies, improving overall patient care.
New framework estimates staged tree models using hierarchical clustering on the probability simplex.
problem Estimating staged tree models with context-specific dependencies.
method Hierarchical clustering on the probability simplex, using simplex-based divergences and linkage methods.
result Total Variation divergence with Ward.D2 linkage produces staged trees with better model fit, structure recovery, and computational efficiency.
Boosting models improve pediatric ICU transfer prediction.
problem Predicting transfer of pediatric patients to ICU.
method Adaptive and gradient boosting classifiers combined into an ensemble model.
result Improved accuracy, sensitivity, specificity, and AUROC over baseline.
CNNs trained on one hospital's x-rays perform poorly on x-rays from other hospitals.
problem Generalization of radiological deep learning models across different hospitals.
method Cross-sectional design using x-rays from three hospitals (NIH, Mount Sinai, Indiana).
result CNNs trained on one hospital's x-rays perform significantly worse on x-rays from other hospitals.
Privacy-preserving continual learning for hospitals.
problem Forgetting old data when updating neural networks for privacy.
method Differentially private variational inference and generative models.
result Estimates likelihood of old data given current model.
This work proposes a student-teacher network for predicting hospital admission locations.
problem Accurate prediction of hospital admission locations to optimize resource allocation.
method Reinforcement learning approach where a teacher network selects data batches for a student network.
result The approach outperforms state-of-the-art methods on tabular data and image recognition.
Deep network clusters hospital patients' vital signs.
problem Sparse and irregularly collected vital sign data.
method Deep interpolation network for latent representation extraction.
result Extracted 7 distinct clusters from vital sign data.
Geometric method captures rare topics and temporal alignment in co-author networks.
problem Missing rare topics and smooth temporal alignment in topic modeling.
method Integrates multimodal text and co-author network data using Hellinger distances and Ward's linkage.
result Effective identification of rare topics and visualization of topic drift over time.
Using the `Riemann Problem with zeros' method, Ward has constructed exact solutions to a (2+1)-dimensional integrable Chiral Model, which exhibit solitons with nontrivial scattering. We give a correspondence between what we conjecture to be all pure soliton solutions and certain holomorphic vector bundles on a compact …
Ward2ICU dataset protects patient privacy while generating synthetic ICU transitions data.
problem Protecting patient privacy while creating synthetic ICU transition data.
method Wasserstein Generative Adversarial Network (GAN) to generate synthetic data, class label balancing.
result Quality of synthetic data generation assessed through binary classification task.
We show that, in quaternionic geometry, the Ward transform is a manifestation of the functoriality of the basic correspondence between the ρ-quaternionic manifolds and their twistor spaces. We apply this fact, together with the Penrose transform, to obtain existence results for hypercomplex manifolds and for harmonic…
Adversarial method improves pneumonia classifier's performance across hospitals.
problem Robust classification of pneumonia from chest radiographs across different hospitals.
method Adversarial optimization to learn models invariant to confounders.
result Improved out-of-hospital generalization performance compared to baselines.
Graphical estimation of count time series dependencies.
problem Estimating dependencies between multivariate count time series.
method Parameter-driven generalized linear model with l1-type regularization and MCEM algorithm.
result Characterization of disease spread interdependence and sources/sinks in Greater Mumbai.
FUALA improves Federated Learning for EHR data, enhancing model uncertainty.
problem Applying ML to EHR data while maintaining privacy and accuracy.
method FUALA embeds uncertainty in federated learning, using ensembling and averaging.
result FUALA outperforms FedAvg on out-of-distribution data in EHR model predictions.
We develop a personalized real time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs. Heterogeneity of the patients population is captured via a hierarchical latent class model. The proposed algorithm aims t…
Study predicts AKI risk in rehospitalized patients using EHR data.
problem Predicting AKI in rehospitalized patients to improve outcomes.
method Gradient boosting, logistic regression, and recurrent neural network trained on EHR data.
result Developed a risk score for AKI at hospital re-entry.
The space-time monopole equation is obtained from a dimension reduction of the anti-self dual Yang-Mills equation on R2,2. A family of Ward equations is obtained by gauge fixing from the monopole equation. In this paper, we give an introduction and a survey of the space-time monopole equation. Included are altern…
The conformal invariance and universality results of Chelkak-Smirnov on the two-dimensional Ising model hold for isoradial planar graphs with critical weights. Motivated by the problem of extending these results to a wider class of graphs, we define a generalized notion of s-holomorphicity for functions on arbitrary we…
The Kac-Ward formula allows to compute the Ising partition function on a planar graph G with straight edges from the determinant of a matrix of size 2N, where N denotes the number of edges of G. In this paper, we extend this formula to any finite graph: the partition function can be written as an alternating sum of the…
Experiment evaluates hospital case cost prediction models using Azure ML.
problem Accurate hospital case cost modelling for efficient financial management.
method Azure Machine Learning Studio tool for comparing 14 regression models.
result Robust regression, boosted decision tree, and decision forest models outperformed others.
We use the compactified twistor correspondence for the (2+1)-dimensional integrable chiral model to prove a conjecture of Ward. In particular, we construct the correspondence space of a compactified twistor fibration and use it to prove that the second Chern numbers of the holomorphic vector bundles, corresponding to t…
Bayesian models forecast COVID-19 hospitalizations at single sites.
problem Forecasting daily COVID-19 hospitalizations at a single hospital.
method Hierarchical Bayesian models with generalized Poisson likelihood and autoregressive/Gaussian process latent processes.
result Demonstrated superior performance compared to baselines in public datasets.
Derives stress-energy identities in Liouville theory on compact surfaces.
problem Stress-energy tensor correlation functions on compact Riemann surfaces.
method Varying correlation functions with respect to background metric, treating different types of variations separately.
result Stress-energy correlation functions expressed as differential operators acting on primary field correlation functions.
Improves hierarchical clustering in Euclidean space using autoencoders.
problem Lack of unsupervised methods for learning hierarchical structure in Euclidean space.
method Variational autoencoder with Gaussian mixture prior, rescaling latent space, and Ward's linkage.
result Improved dendrogram purity and Moseley-Wang cost function results.
ClustGeo uses Ward-like clustering with spatial constraints in R.
problem Hierarchical clustering with spatial/geographical constraints.
method Ward-like hierarchical clustering algorithm with two dissimilarity matrices and a mixing parameter.
result Determines optimal spatial contiguity without sacrificing variable quality.
This study compares community detection algorithms for delineating health service areas.
problem Delineating health service areas to improve health care services.
method Comparative analysis of community detection algorithms on hospital-patient discharge networks.
result Infomap algorithm produced the best delineation of health service areas.
Natural language processing improves COVID-19 hospitalization identification.
problem Identifying patients hospitalized due to COVID-19 among those with positive SARS-CoV-2 tests.
method Used natural language processing on provider notes and structured EHR data elements to create classification algorithms.
result Classification algorithms using provider notes outperformed those using only structured EHR data elements, with AUROC of 0.894 compared to 0.841.
Study uses ML to predict cancer patient mortality from FN onset.
problem Predicting mortality in cancer patients with FN to improve survival.
method Multi-domain machine learning models using HCUP data.
result Clinical diagnoses have highest predictive power for FN mortality.
CPAS uses machine learning to plan hospital resources for COVID-19.
problem Forecasting hospital resource demands during the COVID-19 pandemic.
method Combining machine learning algorithms with diverse data sources.
result CPAS successfully managed hospital resource planning in the UK.
Study compares ARIMA, ETS, NNAR and hybrids for COVID-19 hospitalizations.
problem Forecasting the second wave of COVID-19 hospitalizations in Italy.
method ARIMA, ETS, NNAR, and hybrid models.
result Hybrid models outperform single models in capturing epidemic patterns.
Study evaluates predictive models across multiple hospitals.
problem Limited data sharing hinders model performance evaluation.
method Cross-validation using eICU Collaborative Research Database.
result Models trained on multi-center data generalize well to new hospitals.
The ability to accurately forecast and control inpatient census, and thereby workloads, is a critical and longstanding problem in hospital management. Majority of current literature focuses on optimal scheduling of inpatients, but largely ignores the process of accurate estimation of the trajectory of patients througho…
Method clusters molecular systems based on dynamics or structure similarity.
problem Clustering molecular systems based on dynamics or structure similarity.
method Ward's minimum variance clustering using Jensen-Shannon divergence.
result Method avoids overfitting in supervised learning.
The Kac-Ward formula allows to compute the Ising partition function on any finite graph G from the determinant of 2^{2g} matrices, where g is the genus of a surface in which G embeds. We show that in the case of isoradially embedded graphs with critical weights, these determinants have quite remarkable properties. Firs…
Paper presents interpretable models for predicting hospital readmissions.
problem Preventable readmissions in hospitals are costly and need better prediction models.
method Structured sparsity-inducing norms applied to disease history and demographics.
result Interpretable models outperform standard methods and identify new risk factors.
The paper uses SHAP for interpreting machine learning models in hospital data.
problem Interpreting machine learning models in healthcare.
method SHAP for feature importance and feature packing techniques.
result SHAP provides better interpretability of machine learning models in healthcare.
One of the main issues affecting the Italian NHS is the healthcare deficit: according to current agreements between the Italian State and its Regions, public funding of regional NHS is now limited to the amount of regional deficit and is subject to previous assessment of strict adherence to constraint on regional healt…