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Table 2 Overview of the basic publication attributes of all included studies in the present scoping review

From: Machine learning in general practice: scoping review of administrative task support and automation

No

Author

Title

Year of publication

Country of origin

Type of publication

Aim of study

1

[15] Abu Lekham L., Wang Y., Hey E., Lam S. S., Khasawneh M. T

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

2021

USA

Journal article

Prediction of missed appointments at outpatient primary care settings in rural areas using machine learning

2

[16] Ahmad M. U., Zhang A., Mhaskar R

A predictive model for decreasing clinical no-show rates in a primary care setting

2021

USA

Journal article

Development of a predictive model for patient no-shows or missed appointments in single physician family medicine practice

3

[17] Cubillas J. J., Ramos M. I., Feito F. R., Ureña T

An Improvement in the Appointment Scheduling in Primary Health Care Centers Using Data Mining

2014

Spain

Journal article

Creation of a model able to predict what kind of task (clinical, a medical certificate and issuing a prescription) patients daily require considering external factors influence

4

[18] López Seguí F., Ander Egg Aguilar R., de Maeztu G., García-Altés A., García Cuyàs F., Walsh S., et al

Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: The Evaluation of Text Classification Algorithms Using Supervised Machine Learning

2020

Spain

Journal article

Evaluation of specific text classification algorithms for eConsulta messages and validate their predictive potential

5

[19] Michalowski, W., Michalowski, M., O'Sullivan, D., Wilk, S. and Carrier, M

AFGuide System to Support Personalized Management of Atrial Fibrillation

2017

Canada, Great Britain,

Poland

Conference workshop technical report

Proposal of a clinical decision support system to educate and support primary care physicians in developing evidence-based and optimal atrial fibrillation therapies that consider multi-morbid conditions and patient preferences

6

[20] Mohammadi I., Mehrabi S., Sutton B., Wu H

Word Embedding and Clustering for Patient-Centered Redesign of Appointment Scheduling in Ambulatory Care Settings

2022

USA

Conference paper

Utilization of information from structured and unstructured electronic health records data to redesign appointment scheduling in community health clinics

7

[21] Mohammadi I., Wu H., Turkcan A., Toscos T., Doebbeling B. N

Data Analytics and Modeling for Appointment No-show in Community Health Centers

2018

USA

Journal article

Using predictive modeling techniques to develop and compare appointment no-show prediction models to better understand appointment adherence in underserved populations

8

[22] Park J., Kotzias D., Kuo P., Logan Iv R. L., Merced K., Singh S., et al

Detecting conversation topics in primary care office visits from transcripts of patient-provider interactions

2019

USA

Journal article

Investigation of the effectiveness of machine learning methods for automated annotation of medical topics in patient-provider dialog transcripts

9

[23] Peito, J. and Han, Q

Incorporating Domain Knowledge into Health Recommender Systems Using Hyperbolic Embeddings

2021

Portugal

Conference paper

Investigation of the possibility of a content-based recommender system for patient-doctor matchmaking by incorporating complex, domain-specific knowledge into the underlying model

10

[24] Schwartz J. L., Tseng E., Maruthur N. M., Rouhizadeh M

Identification of Prediabetes Discussions in Unstructured Clinical Documentation: Validation of a Natural Language Processing Algorithm

2022

USA

Journal article

Development and validation of a NLP pipeline to identify when providers discuss prediabetes management and treatment, which could later be used to determine if care delivered meets evidence-based guidelines and compare outcomes before and after an intervention

11

[25] Spenceley, S. E., Warren, J. R., Mudali, S. K. and Kirkwood, I. D

Intelligent Data Entry for Physicians by Machine Learning of an Anticipative Task Model

1996

Australia

Conference paper

Improve usability of electronic medical record systems by having the computer anticipate physicians’ data entry actions and generate short menus (hot lists) that offer likely selections to the user

12

[26] Williams A., Mekhail A., Williams J., McCord J., Buchan V

Effective resource management using machine learning in medicine: an applied example

2018

New Zealand, UK, USA

Journal article

Improve the efficiency of urgent lap sample processing using a transport scheduling platform applying machine learning techniques and simulate the efficiency and cost impact of the platform using historical data