A Multi-Stage predictive model for missed appointments at outpatient primary care settings serving rural areas

Lekham, Laith Abu; Wang, Yong; Hey, Ellen; Lam, Sarah S.; Khasawneh, Mohammad T.

doi:10.1080/24725579.2020.1858210

Cited by 15 publications

(30 citation statements)

References 27 publications

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“…In addition, three regular machine learning classifiers that included linear discriminant analysis (LDA), K-nearest neighbor (K-NN), and Gaussian process (GP) are used. Before the training process, cross-validated hyperparameter tuning is done using the ‘Random Search Optimization’ approach [ 31 ] to find the best parameters for each classifier, if any. The tuning process is done independently from training, validation, and testing.…”

Section: Methodsmentioning

confidence: 99%

Multi-criteria text mining model for COVID-19 testing reasons and symptoms and temporal predictive model for COVID-19 test results in rural communities

Lekham

Wang

Hey

et al. 2022

Neural Comput & Applic

Self Cite

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This study is conducted to build a multi-criteria text mining model for COVID-19 testing reasons and symptoms. The model is integrated with a temporal predictive classification model for COVID-19 test results in rural underserved areas. A dataset of 6895 testing appointments and 14 features is used in this study. The text mining model classifies the notes related to the testing reasons and reported symptoms into one or more categories using look-up wordlists and a multi-criteria mapping process. The model converts an unstructured feature to a categorical feature that is used in building the temporal predictive classification model for COVID-19 test results and conducting some population analytics. The classification model is a temporal model (ordered and indexed by testing date) that uses machine learning classifiers to predict test results that are either positive or negative. Two types of classifiers and performance measures that include balanced and regular methods are used: (1) balanced random forest and (2) balanced bagged decision tree. The balanced or weighted methods are used to address and account for the biased and imbalanced dataset and to ensure correct detection of patients with COVID-19 (minority class). The model is tested in two stages using validation and testing sets to ensure robustness and reliability. The balanced classifiers outperformed regular classifiers using the balanced performance measures (balanced accuracy and G-score), which means the balanced classifiers are better at detecting patients with positive COVID-19 results. The balanced random forest achieved the best average balanced accuracy (86.1%) and G-score (86.1%) using the validation set. The balanced bagged decision tree achieved the best average balanced accuracy (83.0%) and G-score (82.8%) using the testing set. Also, it was found that the patient history, age, testing reasons, and time are the key features to classify the testing results.

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Section: Methodsmentioning

confidence: 99%

Multi-criteria text mining model for COVID-19 testing reasons and symptoms and temporal predictive model for COVID-19 test results in rural communities

Lekham

Wang

Hey

et al. 2022

Neural Comput & Applic

Self Cite

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Section: Basic Publication Attributes Of Included Studiesmentioning

confidence: 99%

“…Table 2 shows the six basic publication attributes of the complete set of 12 studies included following the completion of the identi cation and screening processes described above (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26). The years of publication in the included studies range from 1996 to 2022 with 10 studies published between 2017 and 2022 (15,16,(18)(19)(20)(21)(22)(23)(24)26). Collectively, the studies cross eight countries (USA, Spain, Canada, Great Britain, Poland, Portugal, Australia, and New Zealand) with seven studies originating primarily from the USA (15,16,(19)(20)(21)(22)26).…”

Section: Basic Publication Attributes Of Included Studiesmentioning

confidence: 99%

“…The variety of current general practice problems identi ed concern appointment scheduling (15-17, 20, 21), teleconsultation (18), care management (19,24), communication (22), healthcare recommender systems (23), user interaction with electronic medical records (25), and resource management through scheduling (26), with the most frequently occurring problem being appointment scheduling. Similarly, the data reportedly used in all studies differs both across all identi ed problems and within the same problem from different researchers, with sources largely consisting of proprietary data taken from a variety of domains, including actual general practice clinics (15,17,(20)(21)(22)(23)(24), published clinical guidelines (19), electronic healthcare databases (16), and teleconsultation recordings (18), that differ considerably in their features. In looking at the level of involvement of GPs across all studies, it is not always clearly stated to what extent they participate in the actual research and only two studies clearly state involvement of GPs (18, 26).…”

Section: Journal Articlementioning

confidence: 99%

“…In looking at the type of machine learning used, ten of the studies (15-19, 21, 22, 24-26) modelled their respective administrative tasks as supervised machine learning problems while one study (20) modelled its task as an unsupervised machine learning problem, and a nal study (23) investigated the use of representation learning and transfer learning. The speci c machine learning methods used to solve these problems varied widely from study to study with the single most frequently used technique being regression (15,16,21,24,26). Roughly half of the studies include a "catch-all" approach in which the performances of several different machine learning methods are compared, however, few studies (21 − 13) employed modern data-driven methods of AI based on arti cial neural networks.…”

Section: Journal Articlementioning

confidence: 99%

See 2 more Smart Citations

Machine Learning in General Practice: Scoping Review of Administrative Task Support and Automation

Sørensen

Bemman

Jensen

et al. 2022

Preprint

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Background Artificial intelligence (AI) is increasingly used to support general practice in the early detection of disease and treatment recommendations. However, AI systems aimed at alleviating time-consuming administrative tasks currently appear limited. This scoping review thus aims to summarize the research that has been carried out in methods of machine learning applied to the support and automation of administrative tasks in general practice. Methods Databases covering the fields of health care and engineering sciences (PubMed, Embase, CINAHL with full text, Cochrane Library, Scopus, and IEEE Xplore) were searched. Screening for eligible studies was completed using Covidence, and data was extracted along nine research-based attributes concerning general practice, administrative tasks, and machine learning. Results 1439 records were identified and 1158 were screened for eligibility criteria. A total of 12 studies were included. The extracted attributes indicate that most studies concern various scheduling tasks using supervised machine learning methods with relatively low GP involvement. Importantly, few studies employed the latest available machine learning methods and the data used frequently varied in terms of setting, type, and availability. Conclusion The limited field of research developing in the application of machine learning to administrative tasks in general practice indicates that there is a great need and high potential for such methods. However, there is currently a lack of research likely due to the unavailability of open-source data and a prioritization of diagnostic-based tasks. Future research would benefit from open-source data, cutting-edge methods of machine learning, and clearly stated GP involvement, so that improved and replicable scientific research can done.

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A Robust Deep Learning Techniques for No-Show Prediction in Hospital Appointments

Nguyen,

Dang,

Nguyen

2023

Proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics 2023

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A Multi-Stage predictive model for missed appointments at outpatient primary care settings serving rural areas

Cited by 15 publications

References 27 publications

Multi-criteria text mining model for COVID-19 testing reasons and symptoms and temporal predictive model for COVID-19 test results in rural communities

Multi-criteria text mining model for COVID-19 testing reasons and symptoms and temporal predictive model for COVID-19 test results in rural communities

Machine Learning in General Practice: Scoping Review of Administrative Task Support and Automation

A Robust Deep Learning Techniques for No-Show Prediction in Hospital Appointments

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