Patterns of high-risk drinking among medical students: a web-based survey with machine learning

2021 ◽  
pp. 104747
Author(s):  
Grasiela Marcon ◽  
Flávia de Ávila Pereira ◽  
Aline Zimerman ◽  
Bruno Castro da Silva ◽  
Lísia von Diemen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Medical Students ◽  
High Risk ◽  
Web Based ◽  
High Risk Drinking

scholarly journals Machine Learning Creates a Simple Endoscopic Classification System that Improves Dysplasia Detection in Barrett’s Oesophagus amongst Non-expert Endoscopists

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Vinay Sehgal ◽  
Avi Rosenfeld ◽  
David G. Graham ◽  
Gideon Lipman ◽  
Raf Bisschops ◽  
...  
Keyword(s):  
Machine Learning ◽  
Medical Students ◽  
Sensitivity And Specificity ◽  
Expert Opinion ◽  
Barrett’S Oesophagus ◽  
Oesophageal Adenocarcinoma ◽  
Undergraduate Medical Students ◽  
Web Based ◽  
Barrett's Oesophagus ◽  
Before And After

Introduction. Barrett’s oesophagus (BE) is a precursor to oesophageal adenocarcinoma (OAC). Endoscopic surveillance is performed to detect dysplasia arising in BE as it is likely to be amenable to curative treatment. At present, there are no guidelines on who should perform surveillance endoscopy in BE. Machine learning (ML) is a branch of artificial intelligence (AI) that generates simple rules, known as decision trees (DTs). We hypothesised that a DT generated from recognised expert endoscopists could be used to improve dysplasia detection in non-expert endoscopists. To our knowledge, ML has never been applied in this manner. Methods. Video recordings were collected from patients with non-dysplastic (ND-BE) and dysplastic Barrett’s oesophagus (D-BE) undergoing high-definition endoscopy with i-Scan enhancement (PENTAX®). A strict protocol was used to record areas of interest after which a corresponding biopsy was taken to confirm the histological diagnosis. In a blinded manner, videos were shown to 3 experts who were asked to interpret them based on their mucosal and microvasculature patterns and presence of nodularity and ulceration as well as overall suspected diagnosis. Data generated were entered into the WEKA package to construct a DT for dysplasia prediction. Non-expert endoscopists (gastroenterology specialist registrars in training with variable experience and undergraduate medical students with no experience) were asked to score these same videos both before and after web-based training using the DT constructed from the expert opinion. Accuracy, sensitivity, and specificity values were calculated before and after training where p<0.05 was statistically significant. Results. Videos from 40 patients were collected including 12 both before and after acetic acid (ACA) application. Experts’ average accuracy for dysplasia prediction was 88%. When experts’ answers were entered into a DT, the resultant decision model had a 92% accuracy with a mean sensitivity and specificity of 97% and 88%, respectively. Addition of ACA did not improve dysplasia detection. Untrained medical students tended to have a high sensitivity but poor specificity as they “overcalled” normal areas. Gastroenterology trainees did the opposite with overall low sensitivity but high specificity. Detection improved significantly and accuracy rose in both groups after formal web-based training although it did it reach the accuracy generated by experts. For trainees, sensitivity rose significantly from 71% to 83% with minimal loss of specificity. Specificity rose sharply in students from 31% to 49% with no loss of sensitivity. Conclusion. ML is able to define rules learnt from expert opinion. These generate a simple algorithm to accurately predict dysplasia. Once taught to non-experts, the algorithm significantly improves their rate of dysplasia detection. This opens the door to standardised training and assessment of competence for those who perform endoscopy in BE. It may shorten the learning curve and might also be used to compare competence of trainees with recognised experts as part of their accreditation process.

scholarly journals The Prevalence of Alcohol and Substance Use Among Medical Students at The Faculty of Medicine, Prince of Songkla University, 2016

2018 ◽  
Vol 36 (2) ◽  
pp. 107
Author(s):  
Jarurin Pitanupong ◽  
Ornpailin Ratanapinsiri
Keyword(s):  
Substance Use ◽  
Medical Students ◽  
High Risk ◽  
Alcohol Consumption ◽  
Social Engagement ◽  
Demographic Data ◽  
Common Reason ◽  
Prince Of Songkla University ◽  
High Risk Drinking ◽  
Significant Factors

Objective: To study the prevalence of alcohol and substance use among medical students.Material and Methods: A cross-sectional study surveyed Prince of Songkla University medical students in 2016. Questionnaires for demographic data, the Alcohol Use Disorder Identification Test (AUDIT), experience with alcohol and substance abuse, and the Patient Health Questionnaire-9 Thai version (PHQ-9) were used. We analyzed the data in order to describe the behavior of subjects by descriptive statistics. The factors associated with alcohol used were analyzed by chi-square test and logistic regression.Results: Seven hundred thirty-nine (70.1%) of medical students completed the questionnaires. Participants, 56.0% were female and 44.0% were male. Mean age was 21.2 years old; 53.3% have drunk alcohol; the gender proportion of drinkers was 60.0% of males and 48.1% of females. By AUDIT, 23.1% of medical students were high-risk drinkers. The most common reason for alcohol consumption was social engagement (91.9%) and the most common reason for not drinking was having knowledge about the harmful health effects of alcohol (51.2%). Of the medical students, 7.6% had experience with substance use. The drugs most commonly used were cigarettes (5.3%) and baraku (4.6%). According to the PHQ-9, 11.2% of all medical students, 12.6% in males and 10.1% in females had depression. However, these high levels of depression did not associate with a high-risk of alcohol consumption. The significant factors that associated with high-risk drinking were gender [odd ratio (OR)=1.9 (1.1-3.4)] and experience with substance use [OR=3.8 (2.0-7.3)].Conclusion: Half of medical students drank alcohol and approximate 1 in 10 had experience with substance use. Gender and experience with substance use were the significant factors that correlated with high-risk drinking.

scholarly journals The Prevalence of Alcohol and Substance Use Among Medical Students at The Faculty of Medicine, Prince of Songkla University, 2016

2018 ◽  
Vol 36 (2) ◽  
pp. 107
Author(s):  
Jarurin Pitanupong ◽  
Ornpailin Ratanapinsiri
Keyword(s):  
Substance Use ◽  
Medical Students ◽  
High Risk ◽  
Alcohol Consumption ◽  
Social Engagement ◽  
Demographic Data ◽  
Common Reason ◽  
Prince Of Songkla University ◽  
High Risk Drinking ◽  
Significant Factors

Objective: To study the prevalence of alcohol and substance use among medical students.Material and Methods: A cross-sectional study surveyed Prince of Songkla University medical students in 2016. Questionnaires for demographic data, the Alcohol Use Disorder Identification Test (AUDIT), experience with alcohol and substance abuse, and the Patient Health Questionnaire-9 Thai version (PHQ-9) were used. We analyzed the data in order to describe the behavior of subjects by descriptive statistics. The factors associated with alcohol used were analyzed by chi-square test and logistic regression.Results: Seven hundred thirty-nine (70.1%) of medical students completed the questionnaires. Participants, 56.0% were female and 44.0% were male. Mean age was 21.2 years old; 53.3% have drunk alcohol; the gender proportion of drinkers was 60.0% of males and 48.1% of females. By AUDIT, 23.1% of medical students were high-risk drinkers. The most common reason for alcohol consumption was social engagement (91.9%) and the most common reason for not drinking was having knowledge about the harmful health effects of alcohol (51.2%). Of the medical students, 7.6% had experience with substance use. The drugs most commonly used were cigarettes (5.3%) and baraku (4.6%). According to the PHQ-9, 11.2% of all medical students, 12.6% in males and 10.1% in females had depression. However, these high levels of depression did not associate with a high-risk of alcohol consumption. The significant factors that associated with high-risk drinking were gender [odd ratio (OR)=1.9 (1.1-3.4)] and experience with substance use [OR=3.8 (2.0-7.3)].Conclusion: Half of medical students drank alcohol and approximate 1 in 10 had experience with substance use. Gender and experience with substance use were the significant factors that correlated with high-risk drinking.

Correcting normative misperceptions to reduce high risk drinking

2004 ◽  
Author(s):  
Clayton Neighbors ◽  
Mary E. Larimer ◽  
Melissa A. Lewis ◽  
Rochelle L. Bergstrom
Keyword(s):  
High Risk ◽  
High Risk Drinking

Personalized stratification of back to work risk amidst COVID-19: A machine learning approach (Preprint)

2020 ◽  
Author(s):  
Carson Lam ◽  
Jacob Calvert ◽  
Gina Barnes ◽  
Emily Pellegrini ◽  
Anna Lynn-Palevsky ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Learning Algorithm ◽  
Severe Disease ◽  
High Specificity ◽  
Population Level ◽  
The United States ◽  
Health Condition ◽  
Available P ◽  
Severe Illness

BACKGROUND In the wake of COVID-19, the United States has developed a three stage plan to outline the parameters to determine when states may reopen businesses and ease travel restrictions. The guidelines also identify subpopulations of Americans that should continue to stay at home due to being at high risk for severe disease should they contract COVID-19. These guidelines were based on population level demographics, rather than individual-level risk factors. As such, they may misidentify individuals at high risk for severe illness and who should therefore not return to work until vaccination or widespread serological testing is available. OBJECTIVE This study evaluated a machine learning algorithm for the prediction of serious illness due to COVID-19 using inpatient data collected from electronic health records. METHODS The algorithm was trained to identify patients for whom a diagnosis of COVID-19 was likely to result in hospitalization, and compared against four U.S policy-based criteria: age over 65, having a serious underlying health condition, age over 65 or having a serious underlying health condition, and age over 65 and having a serious underlying health condition. RESULTS This algorithm identified 80% of patients at risk for hospitalization due to COVID-19, versus at most 62% that are identified by government guidelines. The algorithm also achieved a high specificity of 95%, outperforming government guidelines. CONCLUSIONS This algorithm may help to enable a broad reopening of the American economy while ensuring that patients at high risk for serious disease remain home until vaccination and testing become available.

The Influence of Curricular-based Interventions within First-year "Success" Courses on Student Alcohol Expectancies and Engagement in High-risk Drinking Behaviors

NASPA Journal ◽  
2008 ◽  
Vol 45 (1) ◽  
Author(s):  
Matt J Mayhew ◽  
Rebecca J Caldwell ◽  
Aimee Hourigan
Keyword(s):  
High Risk ◽  
Alcohol Expectancies ◽  
First Year ◽  
Alcohol Intervention ◽  
Grade Point ◽  
Student Affairs Practitioners ◽  
Drinking Behaviors ◽  
High Risk Drinking ◽  
Success Courses ◽  
First Year Success

The purpose of this study was to examine the effect of curricular-based interventions housed within first-year success courses on alcohol expectancies and high-risk drinking behaviors. Specifically, we longitudinally assessed 173 students enrolled in one of ten first-year success courses, including five that received the alcohol intervention and five that did not. We then created a series of models accounting for demographic information (i.e., gender and self-reported expected grade point average), the pretest scores for the six outcome measures, and the intervention effect (i.e., whether students received the intervention or not). ANCOVA results showed that the intervention was effective in reducing high-risk drinking behaviors and alcohol expectancies for students enrolled in the success courses that received the intervention. Implications for student affairs practitioners and higher education scholars are discussed.

scholarly journals New risk model is able to identify patients with a low risk of progression in systemic sclerosis

RMD Open ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. e001524
Author(s):  
Nina Marijn van Leeuwen ◽  
Marc Maurits ◽  
Sophie Liem ◽  
Jacopo Ciaffi ◽  
Nina Ajmone Marsan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systemic Sclerosis ◽  
High Risk ◽  
Disease Progression ◽  
Risk Model ◽  
Immunosuppressive Drugs ◽  
Low Risk ◽  
Expert Assessment ◽  
Risk Of Progression

ObjectivesTo develop a prediction model to guide annual assessment of systemic sclerosis (SSc) patients tailored in accordance to disease activity.MethodsA machine learning approach was used to develop a model that can identify patients without disease progression. SSc patients included in the prospective Leiden SSc cohort and fulfilling the ACR/EULAR 2013 criteria were included. Disease progression was defined as progression in ≥1 organ system, and/or start of immunosuppression or death. Using elastic-net-regularisation, and including 90 independent clinical variables (100% complete), we trained the model on 75% and validated it on 25% of the patients, optimising on negative predictive value (NPV) to minimise the likelihood of missing progression. Probability cutoffs were identified for low and high risk for disease progression by expert assessment.ResultsOf the 492 SSc patients (follow-up range: 2–10 years), disease progression during follow-up was observed in 52% (median time 4.9 years). Performance of the model in the test set showed an AUC-ROC of 0.66. Probability score cutoffs were defined: low risk for disease progression (<0.197, NPV:1.0; 29% of patients), intermediate risk (0.197–0.223, NPV:0.82; 27%) and high risk (>0.223, NPV:0.78; 44%). The relevant variables for the model were: previous use of cyclophosphamide or corticosteroids, start with immunosuppressive drugs, previous gastrointestinal progression, previous cardiovascular event, pulmonary arterial hypertension, modified Rodnan Skin Score, creatine kinase and diffusing capacity for carbon monoxide.ConclusionOur machine-learning-assisted model for progression enabled us to classify 29% of SSc patients as ‘low risk’. In this group, annual assessment programmes could be less extensive than indicated by international guidelines.

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