An Application of Machine Learning Techniques to Analyze Patient Information to Improve Oral Health Outcomes

Abstract

ObjectiveVarious health-related fields have applied Machine learning (ML) techniques such as text mining, topic modeling (TM), and artificial neural networks (ANN) to automate tasks otherwise completed by humans to enhance patient care. However, research in dentistry on the integration of these techniques into the clinic arena has yet to exist. Thus, the purpose of this study was to: introduce a method of automating the reviewing patient chart information using ML, provide a step-by-step description of how it was conducted, and demonstrate this method's potential to identify predictive relationships between patient chart information and important oral health-related contributors.MethodsA secondary data analysis was conducted to demonstrate the approach on a set of anonymized patient charts collected from a dental clinic. Two ML applications for patient chart review were demonstrated: (1) text mining and Latent Dirichlet Allocation (LDA) were used to preprocess, model, and cluster data in a narrative format and extract common topics for further analysis, (2) Ordinal logistic regression (OLR) and ANN were used to determine predictive relationships between the extracted patient chart data topics and oral health-related contributors. All analysis was conducted in R and SPSS (IBM, SPSS, statistics 22).ResultsData from 785 patient charts were analyzed. Preprocessing of raw data (data cleaning and categorizing) identified 66 variables, of which 45 were included for analysis. Using LDA, 10 radiographic findings topics and 8 treatment planning topics were extracted from the data. OLR showed that caries risk, occlusal risk, biomechanical risk, gingival recession, periodontitis, gingivitis, assisted mouth opening, and muscle tenderness were highly predictable using the extracted radiographic and treatment planning topics and chart information. Using the statistically significant predictors obtained from OLR, ANN analysis showed that the model can correctly predict >72% of all variables except for bruxism and tooth crowding (63.1 and 68.9%, respectively).ConclusionOur study presents a novel approach to address the need for data-enabled innovations in the field of dentistry and creates new areas of research in dental analytics. Utilizing ML methods and its application in dental practice has the potential to improve clinicians' and patients' understanding of the major factors that contribute to oral health diseases/conditions.

Inclusión de procedimientos de salud oral financiados por el estado colombiano y sus posibles efectos en el ejercicio de la odontología

“…Diagram of the SGSSS and Proposal to Establish Oral Health Networks (in Spanish) Additionally, and as a differentiating factor to give them a competitive advantage, all administrative and clinical processes must be conducted through ICTs that allow data digitalization and, ideally, automation through articial intelligence, machine learning, and data mining. In this way, better results would also be achieved in diagnostic processes (52)(53) and operational and personnel costs would be reduced in order to have a nancial balance.…”

Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review

“…Furthermore, AI models can be trained to detect subtle changes in images over time, which may be indicative of the disease progression. Ameli et al [ 145 ] used ordinal logistic regression and artificial neural networks to determine predictive relationships between the extracted patient chart data topics and oral health-related contributors; the authors observed that the risk for carious lesions, occlusal risk, biomechanical risk, gingival recession, periodontitis, and gingivitis were highly predictable using the extracted radiographic and treatment planning topics and chart information [ 145 ].…”