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 |
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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 |