Facial biotype classification for orthodontic treatment planning using an alternative learning algorithm for tree augmented Naive Bayes

Ruz, Gonzalo A.; Araya-Díaz, Pamela; Henríquez, Pablo A.

doi:10.1186/s12911-022-02062-7

Cited by 5 publications

(4 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The NB classifier utilized in supervised learning algorithms is derived from the Bayesian theorem, which quantifies the likelihood of an event by incorporating prior knowledge of the relevant conditions. This classifier assumes the conditional independence of attributes for the given class variable, enabling the calculation of the likelihood 197,198 . In practice, it is often difficult to fulfill these prerequisites.…”

Section: Machine Learning (Ml) Methods In Clinical Databasesmentioning

confidence: 99%

See 1 more Smart Citation

Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review

Xu,

Zheng,

et al. 2023

J Evidence Based Medicine

View full text Add to dashboard Cite

BackgroundIncreasingly, patient medication adherence data are being consolidated from claims databases and electronic health records (EHRs). Such databases offer an indirect avenue to gauge medication adherence in our data‐rich healthcare milieu. The surge in data accessibility, coupled with the pressing need for its conversion to actionable insights, has spotlighted data mining, with machine learning (ML) emerging as a pivotal technique. Nonadherence poses heightened health risks and escalates medical costs. This paper elucidates the synergistic interaction between medical database mining for medication adherence and the role of ML in fostering knowledge discovery.MethodsWe conducted a comprehensive review of EHR applications in the realm of medication adherence, leveraging ML techniques. We expounded on the evolution and structure of medical databases pertinent to medication adherence and harnessed both supervised and unsupervised ML paradigms to delve into adherence and its ramifications.ResultsOur study underscores the applications of medical databases and ML, encompassing both supervised and unsupervised learning, for medication adherence in clinical big data. Databases like SEER and NHANES, often underutilized due to their intricacies, have gained prominence. Employing ML to excavate patient medication logs from these databases facilitates adherence analysis. Such findings are pivotal for clinical decision‐making, risk stratification, and scholarly pursuits, aiming to elevate healthcare quality.ConclusionAdvanced data mining in the era of big data has revolutionized medication adherence research, thereby enhancing patient care. Emphasizing bespoke interventions and research could herald transformative shifts in therapeutic modalities.

show abstract

Section: Machine Learning (Ml) Methods In Clinical Databasesmentioning

confidence: 99%

“…This classifier assumes the conditional independence of attributes for the given class variable, enabling the calculation of the likelihood. 197,198 In practice, it is often difficult to fulfill these prerequisites. Strong interattribute correlations diminish the classifier's performance, impeding generalization.…”

Section: Bayesian Theoremmentioning

confidence: 99%

Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review

Xu,

Zheng,

et al. 2023

J Evidence Based Medicine

View full text Add to dashboard Cite

show abstract

“…LR is a classical binary variable classification algorithm commonly used in data mining due to its simplicity and greater explanatory power ( 24 ). BNB is a model based on an application of Bayes’ theorem that allows the use of continuous eigenvalues when they occur ( 25 ).…”

Section: Methodsmentioning

confidence: 99%

Applying machine learning techniques to predict the risk of lung metastases from rectal cancer: a real-world retrospective study

et al. 2023

View full text Add to dashboard Cite

BackgroundMetastasis in the lungs is common in patients with rectal cancer, and it can have severe consequences on their survival and quality of life. Therefore, it is essential to identify patients who may be at risk of developing lung metastasis from rectal cancer.MethodsIn this study, we utilized eight machine-learning methods to create a model for predicting the risk of lung metastasis in patients with rectal cancer. Our cohort consisted of 27,180 rectal cancer patients selected from the Surveillance, Epidemiology and End Results (SEER) database between 2010 and 2017 for model development. Additionally, we validated our models using 1118 rectal cancer patients from a Chinese hospital to evaluate model performance and generalizability. We assessed our models’ performance using various metrics, including the area under the curve (AUC), the area under the precision-recall curve (AUPR), the Matthews Correlation Coefficient (MCC), decision curve analysis (DCA), and calibration curves. Finally, we applied the best model to develop a web-based calculator for predicting the risk of lung metastasis in patients with rectal cancer.ResultOur study employed tenfold cross-validation to assess the performance of eight machine-learning models for predicting the risk of lung metastasis in patients with rectal cancer. The AUC values ranged from 0.73 to 0.96 in the training set, with the extreme gradient boosting (XGB) model achieving the highest AUC value of 0.96. Moreover, the XGB model obtained the best AUPR and MCC in the training set, reaching 0.98 and 0.88, respectively. We found that the XGB model demonstrated the best predictive power, achieving an AUC of 0.87, an AUPR of 0.60, an accuracy of 0.92, and a sensitivity of 0.93 in the internal test set. Furthermore, the XGB model was evaluated in the external test set and achieved an AUC of 0.91, an AUPR of 0.63, an accuracy of 0.93, a sensitivity of 0.92, and a specificity of 0.93. The XGB model obtained the highest MCC in the internal test set and external validation set, with 0.61 and 0.68, respectively. Based on the DCA and calibration curve analysis, the XGB model had better clinical decision-making ability and predictive power than the other seven models. Lastly, we developed an online web calculator using the XGB model to assist doctors in making informed decisions and to facilitate the model’s wider adoption (https://share.streamlit.io/woshiwz/rectal_cancer/main/lung.py).ConclusionIn this study, we developed an XGB model based on clinicopathological information to predict the risk of lung metastasis in patients with rectal cancer, which may help physicians make clinical decisions.

show abstract

“…Various cephalometric measurements are described in the literature to determine the direction of growth as well as the facial biotype (16,17). Some of the authors who have proposed cephalometric analyses for the assessment of these characteristics and are routinely used by orthodontists all over the world are Ricketts and Björk-Jarabak (18). However, in some cases, the use of different cephalometric measurements to determine the same characteristic, for example, growth direction or facial biotype, could lead to an inconsistent diagnosis between the measurements applied (19,20).…”

Section: Introductionmentioning

confidence: 99%

Agreement Between Two Cephalometric Analyses of Facial Growth Direction and Biotype

Aguilar-Perez,

Bataller-Mendez,

Colome-Ruiz

et al. 2024

Odovtos - Int J Dent Sc

View full text Add to dashboard Cite

Cephalometry is a morphological and descriptive diagnostic method that provides relevant data on skeletal and dentoalveolar malocclusions of patients seeking orthodontics treatment. Several authors have proposed different cephalometric measurements to determine facial growth direction and facial biotype, the results of these different measurements from the same patient do not always agree on the diagnosis. The aim of this study was to determine the level of agreement between Ricketts and Björk-Jarabak cephalometric analyses for the determination of facial growth direction and facial biotype in patients from a population of Yucatan, Mexico. A total of 260 lateral cephalograms of patients between 18 and 59 years of age were digitally traced using the Ricketts and Björk-Jarabak cephalometric analyses to determine the direction of facial growth and facial biotype using Dolphin Imagine software. Cohen's kappa statistical test was performed to establish the strength of agreement between the diagnostic results obtained by the cephalometric analyses. A poor diagnostic concordance strength was found for growth direction (K=0.105), and acceptable for facial biotype (K=0.362). The concordance strengths for each diagnostic possibility (level) ranged from slight to acceptable, except for the brachyfacial and dolichofacial biotypes, with a moderate strength of agreement. In conclusion, the Ricketts and Björk-Jarabak cephalometric measurements used for the determination of facial biotype and facial growth direction could suggest non-concordant diagnostic assessments in some individuals.

show abstract

Facial biotype classification for orthodontic treatment planning using an alternative learning algorithm for tree augmented Naive Bayes

Cited by 5 publications

References 32 publications

Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review

Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review

Applying machine learning techniques to predict the risk of lung metastases from rectal cancer: a real-world retrospective study

Agreement Between Two Cephalometric Analyses of Facial Growth Direction and Biotype

Contact Info

Product

Resources

About