scholarly journals OC068: Patient history and ultrasound findings: A logistic regression model for the prediction of pelvic pathology in women with chronic pelvic pain

2003 ◽  
Vol 22 (S1) ◽  
pp. 18-19
Author(s):  
E. Okaro ◽  
G. Condous ◽  
A. Khalid ◽  
D. Timmerman ◽  
S. Rao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Pelvic Pain ◽  
Chronic Pelvic Pain ◽  
Logistic Regression Model ◽  
Patient History ◽  
Ultrasound Findings

Pure-Tone Hearing Asymmetry: A Logistic Approach Modeling Age, Sex, and Noise Exposure History

2012 ◽  
Vol 23 (07) ◽  
pp. 553-570 ◽  
Author(s):  
David A. Zapala ◽  
Robin E. Criter ◽  
Jamie M. Bogle ◽  
Larry B. Lundy ◽  
Michael J. Cevette ◽  
...  
Keyword(s):  
Hearing Loss ◽  
Logistic Regression ◽  
Regression Model ◽  
Pure Tone ◽  
Logistic Regression Model ◽  
Noise Exposure ◽  
Clinical Decision Making ◽  
Clinical Impression ◽  
Patient History ◽  
Exposure History

Background: Asymmetric hearing loss (AHL) can be an early sign of vestibular schwannoma (VS). However, recognizing VS-induced AHL is challenging. There is no universally accepted definition of a “medically significant pure-tone hearing asymmetry,” in part because AHL is a common feature of medically benign forms of hearing loss (e.g., age- or firearm-related hearing loss). In most cases, the determination that an observed AHL does not come from a benign cause involves subjective clinical judgment. Purpose: Our purpose was threefold: (1) to quantify hearing asymmetry distributions in a large group of patients with medically benign forms of hearing loss, stratifying for age, sex, and noise exposure history; (2) to assess how previously proposed hearing asymmetry calculations segregate tumor from nontumor cases; and (3) to present the results of a logistic regression method for defining hearing asymmetry that incorporates age, sex, and noise information. Research Design: Retrospective chart review. Study Sample: Five thousand six hundred and sixty-one patients with idiopathic, age- or noise exposure-related hearing loss and 85 untreated VS patients. Data Collection and Analysis: Audiometric, patient history, and clinical impression data were collected from 22,785 consecutive patient visits to the audiology section at Mayo Clinic in Florida from 2006 to 2009 to screen for eligibility. Those eligible were then stratified by VS presence, age, sex, and self-reported noise exposure history. Pure-tone asymmetry distributions were analyzed. Audiometric data from VS diagnoses were used to create four additional audiograms per patient to model the hypothetical development of AHL prior to the actual hearing test. The ability of 11 previously defined hearing asymmetry calculations to distinguish between VS and non-VS cases was described. A logistic regression model was developed that integrated age, sex, and noise exposure history with pure-tone asymmetry data. Regression model performance was then compared to existing asymmetry calculation methods. Results: The 11 existing pure-tone asymmetry calculations varied in tumor detection performance. Age, sex, and noise exposure history helped to predict benign forms of hearing asymmetry. The logistic regression model outperformed existing asymmetry calculations and better accounted for normal age-, sex-, and noise exposure-related asymmetry variability. Conclusions: Our logistic regression asymmetry method improves the clinician's ability to estimate risk of VS, in part by integrating categorical patient history and numeric test data. This form of modeling can enhance clinical decision making in audiology and otology.

scholarly journals Work absence and multimorbidity in Portugal: results from the 1st National Health Examination Survey

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Matos ◽  
C Matias Dias ◽  
A Félix
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Chronic Diseases ◽  
National Health ◽  
Logistic Regression Model ◽  
Health Examination ◽  
Work Absence ◽  
Health Examination Survey ◽  
Absence From Work ◽  
The Impact

Abstract Background Studies on the impact of patients with multimorbidity in the absence of work indicate that the number and type of chronic diseases may increase absenteeism and that the risk of absence from work is higher in people with two or more chronic diseases. This study analyzed the association between multimorbidity and greater frequency and duration of work absence in the portuguese population between the ages of 25 and 65 during 2015. Methods This is an epidemiological, observational, cross-sectional study with an analytical component that has its source of information from the 1st National Health Examination Survey. The study analyzed univariate, bivariate and multivariate variables under study. A multivariate logistic regression model was constructed. Results The prevalence of absenteeism was 55,1%. Education showed an association with absence of work (p = 0,0157), as well as professional activity (p = 0,0086). It wasn't possible to verify association between the presence of chronic diseases (p = 0,9358) or the presence of multimorbidity (p = 0,4309) with absence of work. The prevalence of multimorbidity was 31,8%. There was association between age (p < 0,0001), education (p < 0,001) and yield (p = 0,0009) and multimorbidity. There is no increase in the number of days of absence from work due to the increase in the number of chronic diseases. In the optimized logistic regression model the only variables that demonstrated association with the variable labor absence were age (p = 0,0391) and education (0,0089). Conclusions The scientific evidence generated will contribute to the current discussion on the need for the health and social security system to develop policies to patients with multimorbidity. Key messages The prevalence of absenteeism and multimorbidity in Portugal was respectively 55,1% and 31,8%. In the optimized model age and education demonstrated association with the variable labor absence.

scholarly journals Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes

2021 ◽  
Vol 11 (14) ◽  
pp. 6594
Author(s):  
Yu-Chia Hsu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Pattern Recognition ◽  
Logistic Regression ◽  
Regression Model ◽  
Convolutional Neural Network ◽  
Logistic Regression Model ◽  
National Football League ◽  
Performance Metrics ◽  
Betting Market

The interdisciplinary nature of sports and the presence of various systemic and non-systemic factors introduce challenges in predicting sports match outcomes using a single disciplinary approach. In contrast to previous studies that use sports performance metrics and statistical models, this study is the first to apply a deep learning approach in financial time series modeling to predict sports match outcomes. The proposed approach has two main components: a convolutional neural network (CNN) classifier for implicit pattern recognition and a logistic regression model for match outcome judgment. First, the raw data used in the prediction are derived from the betting market odds and actual scores of each game, which are transformed into sports candlesticks. Second, CNN is used to classify the candlesticks time series on a graphical basis. To this end, the original 1D time series are encoded into 2D matrix images using Gramian angular field and are then fed into the CNN classifier. In this way, the winning probability of each matchup team can be derived based on historically implied behavioral patterns. Third, to further consider the differences between strong and weak teams, the CNN classifier adjusts the probability of winning the match by using the logistic regression model and then makes a final judgment regarding the match outcome. We empirically test this approach using 18,944 National Football League game data spanning 32 years and find that using the individual historical data of each team in the CNN classifier for pattern recognition is better than using the data of all teams. The CNN in conjunction with the logistic regression judgment model outperforms the CNN in conjunction with SVM, Naïve Bayes, Adaboost, J48, and random forest, and its accuracy surpasses that of betting market prediction.

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