Related papers: Deep Learning-Based Forecasting of Boarding Patient Counts to Address ED Overcrowding

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An Artificial Intelligence-Based Framework for Predicting Emergency Department Overcrowding: Development and Evaluation Study [2.3933684192993177]
This study develops machine learning models to predict Emergency Department (ED) waiting room occupancy at two time scales.<n>The hourly model forecasts the waiting count six hours ahead (e.g., a 1 PM prediction for 7 PM), while the daily model estimates the average waiting count for the next 24 hours.<n>These tools support staffing decisions and enable earlier interventions to reduce overcrowding.
arXiv Detail & Related papers (2025-04-23T00:23:13Z)
Predicting Emergency Department Visits for Patients with Type II Diabetes [10.522637839993807]
Over 30 million Americans are affected by Type II diabetes (T2D), a treatable condition with significant health risks.<n>This study aims to develop and validate predictive models using machine learning (ML) techniques to estimate emergency department (ED) visits among patients with T2D.
arXiv Detail & Related papers (2024-12-12T06:37:32Z)
Enhancing PTSD Outcome Prediction with Ensemble Models in Disaster Contexts [0.9249657468385778]
Post-traumatic stress disorder (PTSD) is a significant mental health challenge that affects individuals exposed to traumatic events. Early detection and effective intervention for PTSD are crucial, as it can lead to long-term psychological distress if untreated.
arXiv Detail & Related papers (2024-11-16T01:44:43Z)
Analyzing the Variations in Emergency Department Boarding and Testing the Transferability of Forecasting Models across COVID-19 Pandemic Waves in Hong Kong: Hybrid CNN-LSTM approach to quantifying building-level socioecological risk [0.2985164765599213]
Emergency department's (ED) boarding has been linked to poor patient outcomes and health system performance. Yet, effective forecasting models is rare before COVID-19, lacking during the peri-COVID era. Here, a hybrid convolutional neural network (CNN)-Long short-term memory (LSTM) model was applied to public-domain data sourced from Hong Kong's Hospital Authority, Department of Health, and Housing Authority.
arXiv Detail & Related papers (2024-03-17T11:48:40Z)
Efficient adjustment for complex covariates: Gaining efficiency with DOPE [56.537164957672715]
We propose a framework that accommodates adjustment for any subset of information expressed by the covariates. Based on our theoretical results, we propose the Debiased Outcome-adapted Propensity Estorimator (DOPE) for efficient estimation of the average treatment effect (ATE) Our results show that the DOPE provides an efficient and robust methodology for ATE estimation in various observational settings.
arXiv Detail & Related papers (2024-02-20T13:02:51Z)
Long-term drought prediction using deep neural networks based on geospatial weather data [75.38539438000072]
High-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance. We tackle drought data by introducing an end-to-end approach that adopts a systematic end-to-end approach. Key findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts.
arXiv Detail & Related papers (2023-09-12T13:28:06Z)
Forecasting Emergency Department Crowding with Advanced Machine Learning Models and Multivariable Input [8.294560133196807]
Emergency department (ED) crowding is a significant threat to patient safety and it has been repeatedly associated with increased mortality. Despite active research on the subject, several gaps remain: 1) proposed forecasting models have become outdated due to quick influx of advanced machine learning models (ML), 2) amount of multivariable input data has been limited and 3) discrete performance metrics have been rarely reported. We show that N-BEATS and LightGBM outpeform benchmarks with 11 % and 9 % respective improvements and that DeepAR predicts next day crowding with an AUC of 0.76 (95 % CI 0.69-0.84)
arXiv Detail & Related papers (2023-08-31T08:34:20Z)
Length of Stay prediction for Hospital Management using Domain Adaptation [0.2624902795082451]
Inpatient length of stay (LoS) is an important managerial metric which if known in advance can be used to efficiently plan admissions, allocate resources and improve care. Using historical patient data and machine learning techniques, LoS prediction models can be developed. Ethically, these models can not be used for patient discharge in lieu of unit heads but are of utmost necessity for hospital management systems in charge of effective hospital planning.
arXiv Detail & Related papers (2023-06-29T09:58:21Z)
Adherence Forecasting for Guided Internet-Delivered Cognitive Behavioral Therapy: A Minimally Data-Sensitive Approach [59.535699822923]
Internet-delivered psychological treatments (IDPT) are seen as an effective and scalable pathway to improving the accessibility of mental healthcare. This work proposes a deep-learning approach to perform automatic adherence forecasting, while relying on minimally sensitive login/logout data. The proposed Self-Attention Network achieved over 70% average balanced accuracy, when only 1/3 of the treatment duration had elapsed.
arXiv Detail & Related papers (2022-01-11T13:55:57Z)
SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier [68.8204255655161]
Implantable devices that record neural activity and detect seizures have been adopted to issue warnings or trigger neurostimulation to suppress seizures. For an implantable seizure detection system, a low power, at-the-edge, online learning algorithm can be employed to dynamically adapt to neural signal drifts. SOUL was fabricated in TSMC's 28 nm process occupying 0.1 mm2 and achieves 1.5 nJ/classification energy efficiency, which is at least 24x more efficient than state-of-the-art.
arXiv Detail & Related papers (2021-10-01T23:01:20Z)
Imputation-Free Learning from Incomplete Observations [73.15386629370111]
We introduce the importance of guided gradient descent (IGSGD) method to train inference from inputs containing missing values without imputation. We employ reinforcement learning (RL) to adjust the gradients used to train the models via back-propagation. Our imputation-free predictions outperform the traditional two-step imputation-based predictions using state-of-the-art imputation methods.
arXiv Detail & Related papers (2021-07-05T12:44:39Z)
Modeling patient flow in the emergency department using machine learning and simulation [0.0]
This paper presents a novel application of machine learning (ML) within the simulation to improve patient flow within an emergency department (ED) An ML model used within a real ED simulation model to quantify the effect of detouring a patient out of the ED on the length of stay (LOS) and door-to-doctor time (DTDT) The used policy combined with adding specific ED resources achieve 9.39% and 8.18% reduction in LOS and DTDT, respectively.
arXiv Detail & Related papers (2020-11-22T17:42:53Z)
UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
arXiv Detail & Related papers (2020-10-22T02:28:11Z)
Predicting Training Time Without Training [120.92623395389255]
We tackle the problem of predicting the number of optimization steps that a pre-trained deep network needs to converge to a given value of the loss function. We leverage the fact that the training dynamics of a deep network during fine-tuning are well approximated by those of a linearized model. We are able to predict the time it takes to fine-tune a model to a given loss without having to perform any training.
arXiv Detail & Related papers (2020-08-28T04:29:54Z)
A unified machine learning approach to time series forecasting applied to demand at emergency departments [1.7119367122421556]
There were 25.6 million attendances at Emergency Departments (EDs) in England in 2019 corresponding to an increase of 12 million attendances over the past ten years. We develop a novel ensemble methodology that combines the outcomes of the best performing time series and machine learning approaches. Our approach is able to predict attendances one day in advance up to a mean absolute error of +/- 14 and +/- 10 patients corresponding to a mean absolute percentage error of 6.8% and 8.6% respectively.
arXiv Detail & Related papers (2020-07-13T07:59:24Z)
Towards a Fast Steady-State Visual Evoked Potentials (SSVEP) Brain-Computer Interface (BCI) [46.83815094477545]
We propose a training-free method by combining spatial-filtering and temporal alignment (CSTA) to recognize SSVEP responses in sub-second response time. CSTA exploits linear correlation and non-linear similarity between steady-state responses and stimulus templates with complementary fusion to achieve desirable performance improvements.
arXiv Detail & Related papers (2020-02-04T08:48:36Z)
DeepEnroll: Patient-Trial Matching with Deep Embedding and Entailment Prediction [67.91606509226132]
Clinical trials are essential for drug development but often suffer from expensive, inaccurate and insufficient patient recruitment. DeepEnroll is a cross-modal inference learning model to jointly encode enrollment criteria (tabular data) into a shared latent space for matching inference.
arXiv Detail & Related papers (2020-01-22T17:51:25Z)

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