Juan Jimenez

Although the association between risk factors and non-surgical root canal treatment (NSRCT) failure has been extensively studied, methods to predict the outcomes of NSRCT are in an early stage, and dentists currently make the treatment prognosis based mainly on their clinical experience. Since this involves different sources of error, we investigated the use of machine learning (ML) models as a second opinion to support the clinical decision on whether to perform NSRCT. We undertook a retrospective study of 119 confirmed and not previously treated Apical Periodontitis cases that received the same treatment by the same specialist. For each patient, we recorded the variables from a newly proposed data collection template and defined a binary outcome: Success if the lesion clears and failure otherwise. We conducted tests for detecting the association between the variables and the outcome and selected a set of variables as the initial inputs into four ML algorithms: Logistic Regression (LR), Random Forest (RF), Naive-Bayes (NB), and K Nearest Neighbors (KNN). According to our results, RF and KNN significantly improve (p-values < 0.05) the sensitivity and accuracy of the dentist’s treatment prognosis. Taking our results as a proof of concept, we conclude that future randomized clinical trials are worth designing to test the clinical utility of ML models as a second opinion for NSRCT prognosis.

Differential diagnosis between bacterial and viral meningitis is crucial. In our study, to differentiate bacterial vs. viral meningitis, three machine learning (ML) algorithms (multiple logistic regression (MLR), random forest (RF), and naïve-Bayes (NB)) were applied for the two age groups (0–14 and >14 years) of patients with meningitis by both conventional (culture) and molecular (PCR) methods. Cerebrospinal fluid (CSF) neutrophils, CSF lymphocytes, neutrophil-to-lymphocyte ratio (NLR), blood albumin, blood C-reactive protein (CRP), glucose, blood soluble urokinase-type plasminogen activator receptor (suPAR), and CSF lymphocytes-to-blood CRP ratio (LCR) were used as predictors for the ML algorithms. The performance of the ML algorithms was evaluated through a cross-validation procedure, and optimal predictions of the type of meningitis were above 95% for viral and 78% for bacterial meningitis. Overall, MLR and RF yielded the best performance when using CSF neutrophils, CSF lymphocytes, NLR, albumin, glucose, gender, and CRP. Also, our results reconfirm the high diagnostic accuracy of NLR in the differential diagnosis between bacterial and viral meningitis.

According to the World-Wide Organization of the Health, the number of people infected with the Tripanosoma Cruzi is considered between 6 and 8 million, causal agent of the Chagas’ disease , and in 550000 the people are expected to be exposed to the affectation risk. When concluding in 1983 a longitudinal epidemiologist study in patients with the disease evaluated every 3 years, the cardiac affectation: chronic Chagasic myocarditis (MCHC) increased from a 17% at the beginning of the study to a 49, 4% after 15 years. Previous studies of the variability of cardiac frequency (HRV) in patients with the Chagas’ disease, show alterations in the spectral indices of the HRV. A nonlinear modeling technique allows the definition of early risk markers in patients with Chagas’s disease and without any evidence of cardiac involvement evaluated by standard diagnostic test (CH1). We analyze ECGs by Holter recordings in patients with ECG alterations (CH2), patients without ECG alterations (CH1) who had positive serological findings for Chagas’ disease and healthy (Control) matched for sex and age. Two ECGs were digitized for each volunteer, one begins at supine position and the other at orthostatic position. The technique applied is based upon quantitative comparison of the evolution from similar ECGs Sub segments, and a measure of the predictability decay rate were calculated for each register. We did find significant differences among Control and CH2 during supine and also standing with high sensibility and specificity in the early detection of cardiac involvement. It is suggested that these alterations are very early autonomic disturbances in patients without ECG alterations (CH1) that could be useful to enhance risk stratification.

We propose a model based on a population of agents whose states represent either hostile or peaceful behavior. Randomly selected pairs of agents interact according to a variation of the Prisoners Dilemma game, and the probabilities that the agents behave aggressively or not are constantly updated by the model so that the agents that remain in the game are those with the highest fitness. We show that the population of agents oscillate between generalized conflict and global peace, without either reaching a stable state. We then use this model to explain some of the emergent behaviors in collective conflicts, by comparing the simulated results with empirical data obtained from social systems. In particular, using public data reports we show how the model precisely reproduces interesting quantitative characteristics of diverse types of armed conflicts, public protests, riots and strikes.

Results of a comparative analysis between Heart Rate (HR) patterns occurring before the onset of Paroxysmal Ventricular Tachycardia (PVT) and from healthy subjects are shown. Two study groups were made after electrocardiographic dynamical monitoring (Holter) of volunteers. The first group includes 100h from 27 healthy control subjects, and the second group consists of 88h, ending with self-terminating episodes of PVT from 55 patients. Patterns are defined as sequences of consecutive RR intervals, while atypical patterns are defined as corresponding to unlikely behavior of Heart Rate in healthy subjects. We investigated spatial and temporal distributions of these patterns in order to find early signs of PVT. We found that they can be grouped on a reduced number of clusters, and the number of atypical patterns increases as we approach the onset of the episode.

The idea of this research is to propose an alarm system that can alert the early establishment of an event paroxysmal ventricular tarquicardia (PVT), from study of heart rate variability (HRV). The study of the variability is support in the frequency of abnormal patterns prior to the events. We select and analyze in detail 24 hours records from dynamic electrocardiography (Holter). We selected 88 one-hour segments prior to the events of PVT, and once analyzed the system found an efficiency of 77% in early detection of the establishment of the arrhythmia, with an average prediction time of 43 plusmn 8 minutes distant from events, statistically significant result. To assess the percentage of false alarms we evaluate 103 hours from control healthy subjects, resulting in a specificity of 85%.

According to the World wide Organization of the Health, the number of people infected with the Tri-panosoma Cruzi is considered between 16 and 18 million, causal agent of the Chagaspsila disease, and in 100 million the people exposed to the affectation risk. When concluding in 1983 a study longitudinal epidemiologist in patients with the disease evaluated every 3 years, the cardiac affectation: chronic Chagasic myocarditis (MCHC) increased from a 17% at the beginning of the study to a 49, 4% after 15 years. Previous studies of the variability of cardiac frequency in patients with the Chagaspsila disease, show alterations in the spectral indices of the HRV. We analyze the 24-hour heart rate by Holter recordings in 62 patients with ECG alterations (CH2), 32 patients without ECG alterations (CH1) who had positive serological findings for disease of Chagaspsila and 36 healthy subjects (control) matched for sex and age. We find a orthogonal base that is able to discriminate the groups from circadian profiles, control and CH2, and stratify the groups CH1.