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Clinical solutions for natural abutments with preexisting defected crown margin in the aesthetic area.

Crown replacement has remained one of the difficult methods for installing fixed prosthesis but is often performed because of aesthetic factors and invasion of biological width. This article focuses on the clinical scenario in which preexisting crown margin has defects. Given that clinical decisions regarding restoration management and perio-dontal tissues have to be made with caution, a decision tree of the decision making process for natural abutments in aesthetic area with preexisting defected crown margin is presented. The re-establishment of a new crown margin is the key to the success of

Hybrid model based on fuzzy logic and classification trees for the prediction of mortality of critical trauma patients: Pre-hospital variables from the RETRAUCI registry.

BACKGROUND: In critically injured trauma patients, tools that stratify injury severity and estimate mortality are essential. Fuzzy logic (FL) enables the creation of accurate, interpretable models but requires decision rules, which can be generated using machine learning (ML) techniques like classification trees (CT). Our objective was to develop a hybrid model combining fuzzy logic and classification trees to estimate ICU mortality risk using only prehospital variables. METHODS: We conducted a retrospective study using data from the Spanish Trauma ICU registry (RETRAUCI) from 2015 to 2022. P

TC check: a web app for thyroid cancer recurrence prediction using explainable machine learning.

BACKGROUND: Thyroid cancer (TC) is one of the most prevalent endocrine malignancies, and its recurrence presents a major clinical challenge … three heterogeneous base learners, namely Stochastic Gradient Descent (SGD), Extra Trees (ET), and Decision Trees (DT) with eXtreme Gradient Boosting (XGBoost) as the meta learner. The hyperparameter optimization of various learners was performed through 5-fold cross-validation on the training set. The model performance was evaluated on testing set using accura …

SeizFt: Interpretable Machine Learning for Seizure Detection Using Wearables.

This work presents SeizFt-a novel seizure detection framework that utilizes machine learning to automatically detect seizures using wearable SensorDot EEG data. Inspired by interpretable sleep staging, our novel approach employs a unique combination of data augmentation, meaningful feature extraction, and an ensemble of decision trees to improve resilience to variations in EEG and to increase the capacity to generalize to unseen data. Fourier Transform (FT) Surrogates were utilized to increase sample size and improve the class balance between labeled non-seizure and seizure epochs. To enhance …

Multivariable Predictive Models to Identify the Optimal Biologic Therapy for Treatment of Patients With Psoriasis at the Individual Level.

IMPORTANCE: Identifying the optimal long-term biologic therapy for patients with psoriasis is often done through trial and error. OBJECTIVE … 4.9 years at psoriasis diagnosis and 45.5 years at initiation of biologic therapy. Gradient-boosted decision trees and logistic regression were able to predict a specific cytokine target (eg, interleukin-17 inhibition) associated with a successful treatment with accuracies of 63.6% and 59.2%, and top 2 accuracies of 95.9% and 93.9%. When predicting specific drug …

The role of clinical signs and spirometry in the diagnosis of obstructive airway diseases: a systematic analysis adapted to general practice settings.

BACKGROUND: In general practice (GP), the diagnosis of obstructive airway diseases much relies on diagnostic questions, in view of the limit … a, cough, phlegm and wheeze. Optionally standard parameters of the flow-volume-curve were included. Decision trees for the diagnosis of COPD and asthma were constructed, moreover a probabilistic diagnostic network based on the results of path analyses describing the relationship between variables. RESULTS: In the decision trees, age, sex, current smoking, wheez …

Developing a Prognostic Model to Predict Mortality in Patients with Acute Bacterial Meningitis.

Bacterial meningitis is one of the harmful and deadly infectious diseases, and any delay in its treatment will lead to death. In this paper, a prognostic model was developed to predict the risk of death amongst probable cases of bacterial meningitis. Our prognostic model was developed using a decision tree algorithm on the national meningitis registry of the Iranian Center for Disease and Prevention (ICDCP) containing 3,923 records of meningitis suspected cases in 2018-2019. The most important features have been selected for the model construction. This model can predict the mortality risk for

Pharmacological and behavioral treatments for aggressive psychiatric inpatients.

OBJECTIVE: Because aggressive behaviors of psychiatric patients may be caused by environmental or biological factors, treatment plans that incorporate medication and behavior therapies are the most effective. The authors review research on pharmacological and behavioral treatments for aggressive patients and present a decision tree for use on behavioral units to direct treatment of such patients. METHODS: The empirical literature was searched for studies of pharmacological and behavioral interventions that have been shown to have some value for treating this problem. RESULTS AND CONCLUSIONS:

A Machine Learning Approach to Predicting Need for Hospitalization for Pediatric Asthma Exacerbation at the Time of Emergency Department Triage.

OBJECTIVES: Pediatric asthma is a leading cause of emergency department (ED) utilization and hospitalization. Earlier identification of need … features, and Centers for Disease Control and Prevention influenza patterns. We tested four models: decision trees, LASSO logistic regression, random forests, and gradient boosting machines. For each model, 80% of the data set was used for training and 20% was used to validate the models. The area under the receiver operating characteristic (AUC) curve was calcu …

Neural correlates of temporal credit assignment in the parietal lobe.

Empirical studies of decision making have typically assumed that value learning is governed by time, such that a reward prediction error ari … al decision task where two consecutive eye movement decisions led to a final reward. The underlying decision trees were structured such that the two decisions had different relationships with the final reward, and the optimal strategy was to learn based on the final reward at one of the steps (the "F" step) but ignore changes in this reward at the remaining step …

Decision analysis of treatment options in pyriform sinus carcinoma.

Decision analysis was used to evaluate the current treatment options for stage III squamous cell carcinoma of the pyriform sinus (surgery, radiation therapy, and combined surgery/radiation therapy). Using published data, a decision tree was constructed based on quality-adjusted weeks of survival. With this model the combination of surgery and postoperative radiation therapy is preferred over either primary surgery or the combination of preoperative irradiation and surgery; primary radiation therapy is least favored. The decision is quite sensitive to the augmentation in survival that postopera

Stress in nursing workers caring for people with COVID-19.

OBJECTIVES: to analyze stress from the perspective of nursing workers caring for people with COVID-19 in a public hospital in the Recôncavo region of Bahia. METHODS: this is an exploratory qualitative study, conducted through semi-structured interviews. The data were analyzed using word clouds, similarity trees, and content analysis. RESULTS: nursing workers were exposed to stress while attending to patients with COVID-19. The reported stressors in the workplace included: work overload, lack of planning, speed in performing tasks, fatigue, lack of participation in decision-making, lack of su

A Review of Machine Learning Techniques for Keratoconus Detection and Refractive Surgery Screening.

Various machine learning techniques have been developed for keratoconus detection and refractive surgery screening. These techniques utilize inputs from a range of corneal imaging devices and are built with automated decision trees, support vector machines, and various types of neural networks. In general, these techniques demonstrate very good differentiation of normal and keratoconic eyes, as well as good differentiation of normal and form fruste keratoconus. However, it is difficult to directly compare these studies, as keratoconus represents a wide spectrum of disease. More importantly, no…

Clinical and 2D/3D-Echo Cardiography Determinants of Mitral Valve Reoperation in Children With Congenital Mitral Valve Disease.

BACKGROUND: Congenital mitral valve disease (CMVD) presents major challenges in its medical and surgical management. OBJECTIVES: The purpose of this study was to investigate the value of 3-dimensional echocardiography (3DE) and identify associations with MV reoperation in this setting. METHODS: All children <18 years of age who underwent MV reconstruction for CMVD in 2002 to 2018 were included. Preoperative and postoperative 2-dimensional echocardiography (2DE) and 3DE data were collected. Competing risks and Cox regression analysis were used to identify independent associations with MV reop

Prognostic impact of adenomyosis in cervical cancer: insights from machine learning-driven survival analysis.

OBJECTIVE: The aim of this study was to determine whether adenomyosis is an independent prognostic factor in cervical cancer using integrated survival analysis and machine-learning models. METHODS: This retrospective cohort study included 131 patients with early-stage cervical cancer treated surgically between 2008 and 2020. Patients were stratified by the presence (n=28) or absence (n=103) of adenomyosis based on final histopathology. Kaplan-Meier curves and log-rank tests assessed overall survival. Independent prognostic factors were identified through multivariate Cox regression and logist

Использование машинного обучения с помощью краткого перечня симптомов для просмотра Comorbid Disabilities: A account of Principle Study.

ВВЕДЕНИЕ: Психические расстройства являются основными причинами заболеваемости во всем мире и часто связаны с потреблением психоактивных веществ и другими видами наркотического поведения. Однако многие лица, находящиеся в психиатрических учреждениях, не проходят проверку на наличие зависимости. Общие психопатологические меры, такие, как краткий список симптомов (BSI), могут дать ценное представление о риске наркомании. В этом исследовании рассматривается вопрос о том, могут ли модели машинного обучения использовать реакцию БСИ для прогнозирования проблемного потребления веществ (алкоголь, нарк

Совпадение в аннотации оригинала
INTRODUCTION: Mental health disorders are leading causes of morbidity worldwide and often co-occur with substance use and other addictive be … bling, gaming, compulsive sexual behavior, and pornography use. Machine learning models - including decision trees, random forest, boosting, LASSO, subset selection, and elastic net regression - were employed to predict addiction outcomes based on BSI responses. RESULTS: The BSI demonstrated varying degrees of predictive ability across different addictions and …

An empirical study of diagnosis-related group classification for hospitalization costs in lumbar disk herniation patients treated in Chinese medicine hospitals in China.

OBJECTIVES: Lumbar disk herniation (LDH) is a frequent and multiple disease that imposes a heavier global economic burden. As China's diagnosis related group (DRG) payment system for Chinese medicine health insurance has entered the initial stage. Our study explored the factors influencing the hospitalization costs and established DRG cases classification scheme of LDH patients in Chinese medicine hospitals. METHODS: A total of 2,857 LDH patients were hospitalized in Qingyang City Hospital of Chinese Medicine from January 2017 to June 2022. Using univariate analysis and multiple linear regres

Anomaly-based threat detection in smart health using machine learning.

BACKGROUND: Anomaly detection is crucial in healthcare data due to challenges associated with the integration of smart technologies and healthcare. Anomaly in electronic health record can be associated with an insider trying to access and manipulate the data. This article focuses around the anomalies under different contexts. METHODOLOGY: This research has proposed methodology to secure Electronic Health Records (EHRs) within a complex environment. We have employed a systematic approach encompassing data preprocessing, labeling, modeling, and evaluation. Anomalies are not labelled thus a mech

Combination of urinary biomarkers and machine-learning models provided a higher predictive accuracy to predict long-term treatment outcomes of patients with interstitial cystitis/bladder pain syndrome.

PURPOSE: To identify predictive factors for satisfactory treatment outcome of the patients with IC/BPS using urine biomarkers and machine-learning models. METHODS: The IC/BPS patients were prospectively enrolled and provide urine samples. The targeted analytes included inflammatory cytokines, neurotrophins, and oxidative stress biomarkers. The patients with overall subjective symptom improvement of ≥ 50% were considered to have satisfactory results. Binary logistic regression, receiver-operating characteristic (ROC) curve, machine-learning decision tree, and random forest models were used to

Evaluation of Machine Learning Algorithm Utilization for Lung Cancer Classification Based on Gene Expression Levels.

BACKGROUND: Lung cancer remains one of the most common cancers in the world, both in terms of new cases (about 13% of total per year) and deaths (nearly one cancer death in five), because of the high case fatality. Errors in lung cancer type or malignant growth determination lead to degraded treatment efficacy, because anticancer strategy depends on tumor morphology. MATERIALS AND METHODS: We have made an attempt to evaluate effectiveness of machine learning algorithms in the task of lung cancer classification based on gene expression levels. We processed four publicly available data sets. Th