Machine-Learning-Based Prediction Modelling in Primary Care: State-of-the-Art Review

Primary care has the potential to be transformed by artificial intelligence (AI) and, in particular, machine learning (ML). This review summarizes the potential of ML and its subsets in influencing two domains of primary care: pre-operative care and screening. ML can be utilized in preoperative trea...

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Bibliographic Details
Published in:AI (Basel) 2023-05, Vol.4 (2), p.437-460
Main Authors: El-Sherbini, Adham H., Hassan Virk, Hafeez Ul, Wang, Zhen, Glicksberg, Benjamin S., Krittanawong, Chayakrit
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Anomalies
Artificial intelligence
Collaboration
deep learning
Delirium
Electronic health records
Heart surgery
Hypertension
Machine learning
Medical screening
Mortality
Neural networks
Pneumonia
Prediction models
Preventive medicine
Primary care
Pulmonary embolisms
Risk management
Severe acute respiratory syndrome coronavirus 2
Signs and symptoms
State-of-the-art reviews
Support vector machines
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Summary:Primary care has the potential to be transformed by artificial intelligence (AI) and, in particular, machine learning (ML). This review summarizes the potential of ML and its subsets in influencing two domains of primary care: pre-operative care and screening. ML can be utilized in preoperative treatment to forecast postoperative results and assist physicians in selecting surgical interventions. Clinicians can modify their strategy to reduce risk and enhance outcomes using ML algorithms to examine patient data and discover factors that increase the risk of worsened health outcomes. ML can also enhance the precision and effectiveness of screening tests. Healthcare professionals can identify diseases at an early and curable stage by using ML models to examine medical pictures, diagnostic modalities, and spot patterns that may suggest disease or anomalies. Before the onset of symptoms, ML can be used to identify people at an increased risk of developing specific disorders or diseases. ML algorithms can assess patient data such as medical history, genetics, and lifestyle factors to identify those at higher risk. This enables targeted interventions such as lifestyle adjustments or early screening. In general, using ML in primary care offers the potential to enhance patient outcomes, reduce healthcare costs, and boost productivity.
ISSN:2673-2688
2673-2688
DOI:10.3390/ai4020024