Topics

Predicting Outpatient Appointment Demand Using Machine Learning and Traditional Methods.

08:00 EDT 19th July 2019 | BioPortfolio

Summary of "Predicting Outpatient Appointment Demand Using Machine Learning and Traditional Methods."

Traditional methods have long been used for clinical demand forecasting. Machine learning methods represent the next evolution in forecasting, but model choice and optimization remain challenging for achieving optimal results. To determine the best method to predict demand for outpatient appointments comparing machine learning and traditional methods, this retrospective study analyzed "appointment requests" at a major outpatient department in a destination medical center. Two separate locations (A and B) were assessed with 20 traditional, hybrid (traditional + machine learning) and machine learning methods to determine the best forecasting outcome (lowest Forecast Standard Error, FSE). Data characteristics from both datasets were examined. 20 forecasting models were then assessed and compared for the best result. Location A's data displayed a cyclical and non-trending pattern while Location B's displayed a cyclical and trending pattern. Both Location A and B yielded the feature engineered XGBoost model (machine learning) with the lowest out-of-sample FSE. It is important to carefully analyze and understand the underlying data set pattern and then test a variety of traditional, machine learning, and hybrid prediction methods to achieve optimal predictive results. Additionally, the use of feature engineering or hybrid methods can augment the usefulness of machine learning methods.

Affiliation

Journal Details

This article was published in the following journal.

Name: Journal of medical systems
ISSN: 1573-689X
Pages: 288

Links

DeepDyve research library

PubMed Articles [10547 Associated PubMed Articles listed on BioPortfolio]

Effect of an Online Appointment Scheduling System on Evaluation Metrics of Outpatient Scheduling System: a before-after MulticenterStudy.

Online appointment scheduling systems have been designed in response to the problems of the traditional ones. In Iran, most outpatient clinics and our study population suffer from high patient' no-sho...

Dynamic multi-outcome prediction after injury: Applying adaptive machine learning for precision medicine in trauma.

Machine learning techniques have demonstrated superior discrimination compared to conventional statistical approaches in predicting trauma death. The objective of this study is to evaluate whether mac...

Improved Interpretability of Machine Learning Model Using Unsupervised Clustering: Predicting Time to First Treatment in Chronic Lymphocytic Leukemia.

Time to event is an important aspect of clinical decision making. This is particularly true when diseases have highly heterogeneous presentations and prognoses, as in chronic lymphocytic lymphoma (CLL...

A Comparative Study of Machine Learning Algorithms in Predicting Severe Complications after Bariatric Surgery.

Severe obesity is a global public health threat of growing proportions. Accurate models to predict severe postoperative complications could be of value in the preoperative assessment of potential cand...

Use of machine learning in predicting clinical response to transcranial magnetic stimulation in comorbid posttraumatic stress disorder and major depression: A resting state electroencephalography study.

Repetitive transcranial magnetic stimulation (TMS) is clinically effective for major depressive disorder (MDD) and investigational for other conditions including posttraumatic stress disorder (PTSD). ...

Clinical Trials [6037 Associated Clinical Trials listed on BioPortfolio]

Machine Learning From Fetal Flow Waveforms to Predict Adverse Perinatal Outcomes

The aim of this study is to get a proof of concept for using a computational model of fetal haemodynamics, combined with machine learning based on Doppler patterns of the fetal cardiovascu...

Personalizing Mediterranean Diet in Children.

Investigating glucose response to Mediterranean and regular diets in healthy children in order to develop specific pediatric machine-learning for predicting the personalized glucose respon...

Machine Learning-Based Risk Profile Classification of Patients Undergoing Elective Heart Valve Surgery

Machine learning methods potentially provide a highly accurate and detailed assessment of expected individual patient risk before elective cardiac surgery. Correct anticipation of this ris...

Online Distance Learning Outcomes Compared to Traditional Classroom Learning in Medicine

To test whether our educational methodology is associated with increasing learning of participants. Our second objective was to test the hypothesis that learning in an online classroom wou...

Telemedicine Notifications With Machine Learning for Postoperative Care

The ODIN-Report study will be a randomized controlled trial of the effect of providing machine learning risk forecasts to providers caring for patients immediately after surgery on serious...

Medical and Biotech [MESH] Definitions

A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of labeled paired input-output training (sample) data.

A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of unlabeled paired input-output training (sample) data.

SUPERVISED MACHINE LEARNING algorithm which learns to assign labels to objects from a set of training examples. Examples are learning to recognize fraudulent credit card activity by examining hundreds or thousands of fraudulent and non-fraudulent credit card activity, or learning to make disease diagnosis or prognosis based on automatic classification of microarray gene expression profiles drawn from hundreds or thousands of samples.

A type of ARTIFICIAL INTELLIGENCE that enable COMPUTERS to independently initiate and execute LEARNING when exposed to new data.

Process in which individuals take the initiative, in diagnosing their learning needs, formulating learning goals, identifying resources for learning, choosing and implementing learning strategies and evaluating learning outcomes (Knowles, 1975)

Quick Search


DeepDyve research library

Searches Linking to this Article