Symbolic features and classification via support vector machine for predicting death in patients with Chagas disease

Untargeted Metabolomic Analysis of Exhaled Breath Condensate Identifies Disease-Specific Signatures in Adults With Asthma

PURPOSE

An objective test for the auxiliary diagnosis of asthma is still lacking. The aim of this study was to discriminate asthma signatures via an untargeted metabolomic analysis of exhaled breath condensate.

MATERIALS AND METHODS

This study enrolled 19 patients diagnosed with asthma and 23 healthy volunteers. Samples of exhaled breath condensate (EBC) were collected from both groups. Untargeted metabolomic analyses of EBC were used to identify disease-specific signatures for asthma.

RESULT

There were 30 identifiable differentially expressed metabolites and 7 disordered metabolic pathways between the EBCs of asthmatic patients and healthy control subjects. The main differential pathways included biosynthesis of unsaturated fatty acids, HIF-1 signalling pathway, Glutathione metabolism, Ascorbate and aldarate metabolism, and fatty acid biosynthesis. The integrated machine learning method was used to construct an asthma EBC metabolomic signature model from four differential metabolites; 3,4'-dimethoxy-2'-hydroxychalcone, C17-sphinganine, (z)-6-octadecenoic acid, and 2-butylaniline. The model showed a high level of discrimination efficiency (area under curve (AUC) = 0.98), with robust validation through logistic regression (LR), random forest (RF), and support vector machine (SVM) (LR AUC = 0.98, RF AUC = 0.94, SVM AUC = 1.00). The discriminative ability of the EBC metabolomic signature model in both the training set (AUC = 1.0) and testing data (AUC = 0.817) was superior to that of FeNO (AUC = 0.515 and 0.567, respectively) and FEV1/FVC % predicted (AUC = 0.767 and 0.765, respectively). Among the four biomarkers, (z)-6-octadecenoic acid was significantly correlated with serum IgE.

CONCLUSION

The EBC metabolomic signature model demonstrated good feasibility for assisting in the diagnosis of asthma in adults.

Two-year death prediction models among patients with Chagas Disease using machine learning-based methods

Chagas disease (CD) is recognized by the World Health Organization as one of the thirteen most neglected tropical diseases. More than 80% of people affected by CD will not have access to diagnosis and continued treatment, which partly supports the high morbidity and mortality rate. Machine Learning (ML) can identify patterns in data that can be used to increase our understanding of a specific problem or make predictions about the future. Thus, the aim of this study was to evaluate different models of ML to predict death in two years of patients with CD. ML models were developed using different techniques and configurations. The techniques used were: Random Forests, Adaptive Boosting, Decision Tree, Support Vector Machine, and Artificial Neural Networks. The adopted settings considered only interview variables, only complementary exam variables, and finally, both mixed. Data from a cohort study with CD patients called SaMi-Trop were analyzed. The predictor variables came from the baseline; and the outcome, which was death, came from the first follow-up. All models were evaluated in terms of Sensitivity, Specificity and G-mean. Among the 1694 individuals with CD considered, 134 (7.9%) died within two years of follow-up. Using only the predictor variables from the interview, the different techniques achieved a maximum G-mean of 0.64 in predicting death. Using only the variables from complementary exams, the G-mean was up to 0.77. In this configuration, the protagonism of NT-proBNP was evident, where it was possible to observe that an ML model using only this single variable reached G-mean of 0.76. The configuration that mixed interview variables and complementary exams achieved G-mean of 0.75. ML can be used as a useful tool with the potential to contribute to the management of patients with CD, by identifying patients with the highest probability of death. Trial Registration: This trial is registered with ClinicalTrials.gov, Trial ID: NCT02646943.

Prospectively Assigned AAST Grade versus Modified Hinchey Class and Acute Diverticulitis Outcomes

BACKGROUND

The American Association for the Surgery of Trauma (AAST) recently developed a classification system to standardize outcomes analyses for several emergency general surgery conditions. To highlight this system's full potential, we conducted a study integrating prospective AAST grade assignment within the electronic medical record.

METHODS

Our institution integrated AAST grade assignment into our clinical workflow in July 2018. Patients with acute diverticulitis were prospectively assigned AAST grades and modified Hinchey classes at the time of surgical consultation. Support vector machine-a machine learning algorithm attuned for small sample sizes-was used to compare the associations between the two classification systems and decision to operate and incidence of complications.

RESULTS

67 patients were included (median age of 62 y, 40% male) for analysis. The decision for operative management, hospital length of stay, intensive care unit admission, and intensive care unit length of stay were associated with both increasing AAST grade and increasing modified Hinchey class (all P < 0.001). AAST grade additionally showed a correlation with complication severity (P = 0.02). Compared with modified Hinchey class, AAST grade better predicted decision to operate (88.2% versus 82.4%).

CONCLUSIONS

This study showed the feasibility of electronic medical record integration to support the full potential of AAST classification system's utility as a clinical decision-making tool. Prospectively assigned AAST grade may be an accurate and pragmatic method to find associations with outcomes, yet validation requires further study.

Personality classification based on profiles of social networks’ users and the five-factor model of personality

Online social networks have become demanded ways for users to show themselves and connect and share information with each other among these social networks. Facebook is the most popular social network. Personality recognition is one of the new challenges between investigators in social networks. This paper presents a hypothesis that users by similar personality are expected to display mutual behavioral patterns when cooperating through social networks. With the goal of personality recognition in terms of analyzing user activity within Facebook, we collected information about the personality traits of users and their profiles on Facebook, hence we flourished an application using API Facebook. The participants of this study are 100 volunteers of Facebook users. We asked the participants to respond the NEO personality questionnaire in a period of 1 month in May 2012. At the end of this questionnaire, there was a link that asked the participants to permit the application to access their profiles. Based on all the collected data, classifiers were learned using different data mining techniques to recognize user personality by their profile and without filling out any questionnaire. With comparing classifiers’ results, the boosting-decision tree was our proposed model with 82.2% accuracy was more accurate than previous studies that were able to foresee personality according to the variables in their profiles in five factors for using it as a model for recognizing personality.