Table 1_Identifying oral disease variables associated with pneumonia emergence by application of machine learning to integrated medical and dental big data to inform eHealth approaches.docx

BackgroundThe objective of this study was to build models that define variables contributing to pneumonia risk by applying supervised Machine Learning-(ML) to medical and oral disease data to define key risk variables contributing to pneumonia emergence for any pneumonia/pneumonia subtypes.MethodsRetrospective medical and dental data were retrieved from Marshfield Clinic Health System's data warehouse and integrated electronic medical-dental health records (iEHR). Retrieved data were pre-processed prior to conducting analyses and included matching of cases to controls by (a) race/ethnicity and (b) 1:1 Case: Control ratio. Variables with >30% missing data were excluded from analysis. Datasets were divided into four subsets: (1) All Pneumonia (all cases and controls); (2) community (CAP)/healthcare associated (HCAP) pneumonias; (3) ventilator-associated (VAP)/hospital-acquired (HAP) pneumonias and (4) aspiration pneumonia (AP). Performance of five algorithms were compared across the four subsets: Naïve Bayes, Logistic Regression, Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and Random Forests. Feature (input variables) selection and ten-fold cross validation was performed on all the datasets. An evaluation set (10%) was extracted from the subsets for further validation. Model performance was evaluated in terms of total accuracy, sensitivity, specificity, F-measure, Mathews-correlation-coefficient and area under receiver operating characteristic curve (AUC).ResultsIn total, 6,034 records (cases and controls) met eligibility for inclusion in the main dataset. After feature selection, the variables retained in the subsets were: All Pneumonia (n = 29 variables), CAP-HCAP (n = 26 variables); VAP-HAP (n = 40 variables) and AP (n = 37 variables), respectively. Variables retained (n = 22) were common across all four pneumonia subsets. Of these, the number of missing teeth, periodontal status, periodontal pocket depth more than 5 mm and number of restored teeth contributed to all the subsets and were retained in the model. MLP outperformed other predictive models for All Pneumonia, CAP-HCAP and AP subsets, while SVM outperformed other models in VAP-HAP subset.ConclusionThis study validates previously described associations between poor oral health and pneumonia. Benefits of an integrated medical-dental record and care delivery environment for modeling pneumonia risk are highlighted. Based on findings, risk score development could inform referrals and follow-up in integrated healthcare delivery environment and coordinated patient management.</p

Additional file 1: of Patient awareness/knowledge towards oral cancer: a cross-sectional survey

Survey Instrument. (PDF 249 kb

Definition of type 2 diabetes mellitus (DM).

<p>(A) The algorithm for defining patients with DM relied on both electronic diagnostic codes and laboratory results. (B) The algorithm for defining patients without DM also relied on electronic diagnostic codes and laboratory results. Normal and elevated laboratory results for HbA1c and glucose are based on American Diabetes Association guidelines (ADA) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070426#pone.0070426-Tran1" target="_blank">[12]</a>.</p

Subject selection and matching.

<p>Study subjects were selected carefully based on whether or not they developed type 2 diabetes mellitus (DM) in the time period from January 1, 1995 through December 31, 2009.</p

Descriptive characteristics and matching variables in diabetic and non-diabetic subjects.

<p><b><i>Abbreviations</i></b>: DM, type 2 diabetes mellitus; IQR, interquartile range; MESA, Marshfield Epidemiologic Study Area; BMI, body mass index.</p>a<p>Treatment data for colon cancer, only available for subjects in cancer registry, N as indicated.</p

Cancer incidence before and after onset of type 2 diabetes mellitus (DM).

<p>Cumulative incidence of colon cancer in women (A) and men (B) before DM onset and women (C) and men (D) after DM onset. Diabetic subjects are indicated by the solid line and non-diabetic subjects are indicated by the dashed line.</p

Colon cancer risk in men before and after DM onset.

<p><b><i>Abbreviations</i></b>: DM, type 2 diabetes mellitus; HR, hazard ratio; CI, confidence interval; MESA, Marshfield Epidemiologic Survey Area; BMI, body mass index.</p>a<p>Number of subjects with colon cancer over total number of subjects in each group.</p

Progression of type 2 diabetes mellitus (DM) from pre-diabetes to clinical onset.

<p>DM is a progressive disease in which several important physiological changes occur prior to the onset of overt clinical disease. Several changes have potential mitogenic activity and may stimulate underlying carcinogenic processes.</p

Number needed to be exposed to DM for one additional person to develop colon cancer.

a<p>Time 0 represents the date of DM onset/reference date.</p>b<p>Hazard ratio of colon cancer in diabetic compared to non-diabetic subjects.</p>c<p>The probability of a non-diabetic subject being alive and cancer-free at specified time.</p>d<p>NNEH, number need to be exposed to DM for one additional person to be harmed (i.e. develop colon cancer).</p

Colon cancer risk in women before and after DM onset.

<p><b><i>Abbreviations</i></b>: DM, type 2 diabetes mellitus; HR, hazard ratio; CI, confidence interval.</p>a<p>Number of subjects with colon cancer over total number of subjects in each group.</p