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.
Highlights
Dental records, known as patient charts, are legal documents that provide detailed information of patients’ oral health history and consist of significant amounts of data about the patient
Using the topic modeling thresholds as discussed, 10 topics were identified for radiographic findings and eight topics were identified for suggested treatment plans using the topic modeling packages in R
The proposed Machine learning (ML) method was able to extract relevant topics and predict 8 out of 10 oral health outcomes with >72% predictability rate, which indicates that this model is well-suited to predict oral health-related outcomes using patient chart data
Summary
Known as patient charts, are legal documents that provide detailed information of patients’ oral health history and consist of significant amounts of data about the patient They include: demographics, clinical, medical, and radiographic findings, corresponding diagnosis, and treatment planning [1–3]. Many dentists have traditionally employed paper patient charts in their practice, an increasing number of dentists are utilizing electronic records to collect patient clinical data [4] While both formats provide a reasonable option to record and store patient information, electronic records are often cited as advantageous due to their storage capacity, accessibility, ease of information relay, time efficiency, and scalability (e.g., features such as fillable templates and typed notes enable dentists to spend more time with patients and completing procedures than writing or interpreting notes) [4, 5]. It is important to note that human errors in the accuracy and relevance of information recorded can occur in either paper or electronic formations (e.g., illegible handwritten notes or auto-fillable templates may result in meaningless or incorrect entries) [5]
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