Browsing by Subject "Clinical Decision-Making"

Now showing 1 - 4 of 4
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    A machine learning model for orthodontic extraction/non-extraction decision in a racially and ethnically diverse patient population
    (Elsevier, 2023-09) Mason, Taylor; Kelly, Kynnedy M.; Eckert, George; Dean, Jeffrey A.; Dundar, M. Murat; Turkkahraman, Hakan; Orthodontics and Oral Facial Genetics, School of Dentistry
    Introduction The purpose of the present study was to create a machine learning (ML) algorithm with the ability to predict the extraction/non-extraction decision in a racially and ethnically diverse sample. Methods Data was gathered from the records of 393 patients (200 non-extraction and 193 extraction) from a racially and ethnically diverse population. Four ML models (logistic regression [LR], random forest [RF], support vector machine [SVM], and neural network [NN]) were trained on a training set (70% of samples) and then tested on the remaining samples (30%). The accuracy and precision of the ML model predictions were calculated using the area under the curve (AUC) of the receiver operating characteristics (ROC) curve. The proportion of correct extraction/non-extraction decisions was also calculated. Results The LR, SVM, and NN models performed best, with an AUC of the ROC of 91.0%, 92.5%, and 92.3%, respectively. The overall proportion of correct decisions was 82%, 76%, 83%, and 81% for the LR, RF, SVM, and NN models, respectively. The features found to be most helpful to the ML algorithms in making their decisions were maxillary crowding/spacing, L1-NB (mm), U1-NA (mm), PFH:AFH, and SN-MP(̊), although many other features contributed significantly. Conclusions ML models can predict the extraction decision in a racially and ethnically diverse patient population with a high degree of accuracy and precision. Crowding, sagittal, and vertical characteristics all featured prominently in the hierarchy of components most influential to the ML decision-making process.
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    Identifying Patients' Smoking Status from Electronic Dental Records Data
    (IOS Press, 2017) Patel, Jay; Siddiqui, Zasim; Krishnan, Anand; Thyvalikakath, Thankam; Cariology, Operative Dentistry and Dental Public Health, School of Dentistry
    Smoking is a significant risk factor for initiation and progression of oral diseases. A patient's current smoking status and tobacco dependency can aid clinical decision making and treatment planning. The free-text nature of this data limits accessibility causing obstacles during the time of care and research utility. No studies exist on extracting patient's smoking status automatically from the Electronic Dental Record. This study reports the development and evaluation of an NLP system for this purpose.
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    Identifying Patients' Smoking Status from Electronic Dental Records Data
    (IOS Press, 2017) Patel, Jay; Siddiqui, Zasim; Krishnan, Anand; Thyvalikakath, Thankam; Cariology, Operative Dentistry and Dental Public Health, School of Dentistry
    Smoking is a significant risk factor for initiation and progression of oral diseases. A patient's current smoking status and tobacco dependency can aid clinical decision making and treatment planning. The free-text nature of this data limits accessibility causing obstacles during the time of care and research utility. No studies exist on extracting patient's smoking status automatically from the Electronic Dental Record. This study reports the development and evaluation of an NLP system for this purpose.
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    Medical decision making about long-term artificial nutrition after severe stroke: a case report
    (PubMed, 2021-07) Comer, Amber R.; Williams, Linda S.; Bartlett, Stephanie L.; D'Cruz, Lynn E.; Torke, Alexia M.; Health Sciences, School of Health and Human Sciences
    Choosing to use a percutaneous endoscopic gastrostomy (PEG tube) for long term artificial nutrition in the setting of inadequate oral intake after stroke is complex because the decision must be made in a relatively short amount of time and prognosis is often uncertain. This case study utilized interviews with attending and resident neurologists, and surrogate medical decision makers in order to examine how neurologists and surrogate medical decision makers approached the decision to either receive a PEG tube or pursue comfort measures after severe stroke in two patients. Although these two patients presented with similar clinical characteristics and faced similar medical decisions, different decisions regarding PEG tube placement were made. Major challenges included physicians who did not agree on prognosis and surrogates who did not agree on whether to place a PEG tube. These cases demonstrate the importance of the role of the surrogate medical decision maker and the necessity of physicians and surrogate medical decision makers approaching the complex decision of PEG tube placement after stroke together. Additionally, these cases highlight the differing views on what defines a good quality of life and show the vital importance of high-quality goals of care conversations about prognosis and quality of life when deciding whether to place a PEG tube after severe stroke.