Forecasting Emergency Department Capacity Constraints for COVID Isolation Beds

• URL: http://arxiv.org/abs/2011.06058v1
• Date: Mon, 9 Nov 2020 19:35:41 GMT
• Title: Forecasting Emergency Department Capacity Constraints for COVID Isolation Beds
• Authors: Erik Drysdale, Devin Singh, Anna Goldenberg
• Abstract summary: New COVID-related capacity constraints placed on our pediatric hospital's emergency department prompted us to develop an hourly forecasting tool. We obtain strong performance for both point predictions and classification accuracy when predicting the ordinal tiers of our hospital's capacity. We are currently working on moving our tool to a real-time setting with the goal of augmenting the capabilities of our healthcare workers.
• Score: 9.358404775024109
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting patient volumes in a hospital setting is a well-studied application of time series forecasting. Existing tools usually make forecasts at the daily or weekly level to assist in planning for staffing requirements. Prompted by new COVID-related capacity constraints placed on our pediatric hospital's emergency department, we developed an hourly forecasting tool to make predictions over a 24 hour window. These forecasts would give our hospital sufficient time to be able to martial resources towards expanding capacity and augmenting staff (e.g. transforming wards or bringing in physicians on call). Using Gaussian Process Regressions (GPRs), we obtain strong performance for both point predictions (average R-squared: 82%) as well as classification accuracy when predicting the ordinal tiers of our hospital's capacity (average precision/recall: 82%/74%). Compared to traditional regression approaches, GPRs not only obtain consistently higher performance, but are also robust to the dataset shifts that have occurred throughout 2020. Hospital stakeholders are encouraged by the strength of our results, and we are currently working on moving our tool to a real-time setting with the goal of augmenting the capabilities of our healthcare workers.

Related papers

• Performative Time-Series Forecasting [71.18553214204978]
  We formalize performative time-series forecasting (PeTS) from a machine-learning perspective. We propose a novel approach, Feature Performative-Shifting (FPS), which leverages the concept of delayed response to anticipate distribution shifts. We conduct comprehensive experiments using multiple time-series models on COVID-19 and traffic forecasting tasks.
  arXiv  Detail & Related papers  (2023-10-09T18:34:29Z)
• 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)
• Patient Outcome Predictions Improve Operations at a Large Hospital Network [1.9399350450208843]
  A large hospital network in the US has been collaborating with academics and consultants to predict short-term and long-term outcomes for all inpatients. We develop machine learning models that predict the probabilities of next 24-hr/48-hr discharge and intensive care unit transfers, end-of-stay mortality and discharge dispositions. We implement an automated pipeline that extracts data and updates predictions every morning, as well as user-friendly software and a color-coded alert system to communicate these patient-level predictions.
  arXiv  Detail & Related papers  (2023-05-25T00:49:27Z)
• Large-Scale Study of Temporal Shift in Health Insurance Claims [25.16487014413502]
  We build an algorithm to test for temporal shift either at the population level or within a discovered sub-population. We create 1,010 tasks by evaluating 242 healthcare outcomes for temporal shift from 2015 to 2020 on a health insurance claims dataset.
  arXiv  Detail & Related papers  (2023-05-08T23:08:18Z)
• Forecast reconciliation for vaccine supply chain optimization [61.13962963550403]
  Vaccine supply chain optimization can benefit from hierarchical time series forecasting. Forecasts of different hierarchy levels become incoherent when higher levels do not match the sum of the lower levels forecasts. We tackle the vaccine sale forecasting problem by modeling sales data from GSK between 2010 and 2021 as a hierarchical time series.
  arXiv  Detail & Related papers  (2023-05-02T14:34:34Z)
• What Should I Know? Using Meta-gradient Descent for Predictive Feature Discovery in a Single Stream of Experience [63.75363908696257]
  computational reinforcement learning seeks to construct an agent's perception of the world through predictions of future sensations. An open challenge in this line of work is determining from the infinitely many predictions that the agent could possibly make which predictions might best support decision-making. We introduce a meta-gradient descent process by which an agent learns what predictions to make, 2) the estimates for its chosen predictions, and 3) how to use those estimates to generate policies that maximize future reward.
  arXiv  Detail & Related papers  (2022-06-13T21:31:06Z)
• Approximate Bayesian Computation for an Explicit-Duration Hidden Markov Model of COVID-19 Hospital Trajectories [55.786207368853084]
  We address the problem of modeling constrained hospital resources in the midst of the COVID-19 pandemic. For broad applicability, we focus on the common yet challenging scenario where patient-level data for a region of interest are not available. We propose an aggregate count explicit-duration hidden Markov model, nicknamed the ACED-HMM, with an interpretable, compact parameterization.
  arXiv  Detail & Related papers  (2021-04-28T15:32:42Z)
• Forecasting COVID-19 Counts At A Single Hospital: A Hierarchical Bayesian Approach [59.318136981032]
  We consider the problem of forecasting the daily number of hospitalized COVID-19 patients at a single hospital site. We develop several candidate hierarchical Bayesian models which directly capture the count nature of data. We demonstrate our approach on public datasets for 8 hospitals in Massachusetts, U.S.A. and 10 hospitals in the United Kingdom.
  arXiv  Detail & Related papers  (2021-04-14T11:58:54Z)
• BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes [53.163089893876645]
  We propose a novel self-attention mechanism that captures the contextual dependency and temporal relationships within a patient's healthcare journey. An end-to-end bidirectional temporal encoder network (BiteNet) then learns representations of the patient's journeys. We have evaluated the effectiveness of our methods on two supervised prediction and two unsupervised clustering tasks with a real-world EHR dataset.
  arXiv  Detail & Related papers  (2020-09-24T00:42:36Z)
• 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)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.