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The application of machine learning techniques as an adjunct to clinical decision making in alcohol dependence treatment.

With few exceptions, research in the addictive sciences has relied on linear statistics and methodologies. Addiction involves a complex arra … uality of life, and psychological measures at baseline were used to predict abstinence at 12 weeks. Decision trees had a 77% predictive accuracy across both data sets, Bayesian networks 73%, and discriminant analysis 42%. Combined with clinical experience, machine learning approaches offer promise in understanding the complex relationships that underlie treatmen …

Classification algorithms applied to narrative reports.

Narrative text reports represent a significant source of clinical data. However, the information stored in these reports is inaccessible to many automated decision support systems. Data mining techniques can assist in extracting information from narrative data. Multiple classification methods, such as rule generation, decision trees, Bayesian classifiers, and information retrieval were used to classify a set of 200 chest X-ray reports according to 6 clinical conditions indicated. A general-purpose natural language processor was used to convert the narrative text into a coded form that could be…

Novel method for predicting nonvisible symptoms using machine learning in cancer palliative care.

End-of-life patients with cancer may find expressing their symptoms difficult if they can no longer communicate verbally because of deteriorating health. In this study, we assessed these symptoms using machine learning, which has excellent predictive capabilities and has recently been applied in healthcare. We performed a retrospective clinical survey involving 213 patients with cancer from August 2015 to August 2016. We divided the reported symptoms into two groups-visible and nonvisible symptoms. We used decision tree analysis, an analytical machine learning method that organizes and analyze

Assessing the likelihood of contracting COVID-19 disease based on a predictive tree model: A retrospective cohort study.

BACKGROUND: Primary care is the major point of access in most health systems in developed countries and therefore for the detection of coronavirus disease 2019 (COVID-19) cases. The quality of its IT systems, together with access to the results of mass screening with Polymerase chain reaction (PCR) tests, makes it possible to analyse the impact of various concurrent factors on the likelihood of contracting the disease. METHODS AND FINDINGS: Through data mining techniques with the sociodemographic and clinical variables recorded in patient's medical histories, a decision tree-based logistic re

Development of a machine learning predictive model for central venous catheter-associated thrombosis in patients undergoing abdominal surgery.

BACKGROUND: Central venous catheters (CVCs) are placed where the vena cava meets the right atrium. Their common use raises the risk of catheter-related thrombosis (CRT), a potentially life-threatening complication. AIM: This study leverages machine learning to develop a CRT predictive model for abdominal surgery patients, aiming to refine clinical decisions and elevate treatment quality. STUDY DESIGN: The data were split into training and validation sets using the caret package in R. Decision Trees (DT), Extra Trees (ET), Ada Boost, Gradient Boosting (GB), Light Gradient Boosting Machine (LG…

Exploring machine learning algorithms to predict short birth intervals and identify its determinants among reproductive-age women in East Africa.

BACKGROUND: The occurrence of short birth intervals among reproductive-age women in East Africa is a critical public health issue, contribut … data and normalize numerical features. Four machine learning models, including logistic regression, decision trees, random forests, and some machine learning models, including logistic regression, decision trees, random forests, and naive Bayes, are trained and evaluated to predict short birth intervals. Model performance is assessed using metrics such as accura …

Машинное обучение алгоритмов вводит эвоидные потенциальные возможности в качестве альтернативных биомаркеров для расширения масштабов инвалидности, прогнозируя пациентов с множественным склерозом.

Заметно возросло значение машинного обучения (ML) в области медицины, главным образом благодаря расширению доступа к медицинским данным и прогрессивному совершенствованию алгоритмов ML. Таким образом, ML может использоваться для разработки прогнозных моделей, способствующих диагностике заболеваний, прогнозированию развития заболеваний, адаптации лечения к индивидуальным потребностям пациентов и повышению оперативной эффективности систем здравоохранения. Своевременное обнаружение того или иного заболевания способствует эффективному лечению симптомов и гарантирует надлежащее лечение. В случае мн

Совпадение в аннотации оригинала
Machine learning (ML) has witnessed a notable increase in significance within the medical field, primarily due to the increasing availabilit … eloped intelligent systems that predict the progression of MS utilizing ML algorithms, specifically decision trees and neural networks. In our experimental evaluation, decision trees, neural networks, and Bayes for EPs achieved accuracies of 88.9%, 92.9%, and 88.2% respectively, which are comparable to MRI that obtained accuracies of 88.2%, 96.0%, and 85.0%. The …

Detection of honey adulteration using machine learning.

Honey adulteration is a growing concern due to its health benefits and high nutritional content. Traditional methods like Melissopalynology … cial Neural Networks (ANN), Support-vector machines (SVM), K Nearest Neighbors, Random Forests, and Decision trees. The proposed model relies on hyper-spectrum images and overcomes generalization to unknown honey types of problems. The dataset used includes segmented and pre-processed hyperspectral images of adulterated honey samples from seven different brands …

Predicting Plasmodium falciparum infection status in blood using a multiplexed bead-based antigen detection assay and machine learning approaches.

BACKGROUND: Plasmodium blood-stage infections can be identified by assaying for protein products expressed by the parasites. While the binar … ad bead-based detection and quantification of three Plasmodium antigens: pAldolase, pLDH, and HRP2. Decision trees and principal component analysis (PCA) were conducted in attempt to categorize P. falciparum parasitemia density status based on continuous antigen concentrations. RESULTS: Conditional inference trees were trained using the known P. falciparum infe …

Peripheral blood signature of vasodilator-responsive pulmonary arterial hypertension.

BACKGROUND: Heterogeneity in response to treatment of pulmonary arterial hypertension (PAH) is a major challenge to improving outcome in thi … R1, DSG2, SCD5, P2RY5, MGAT5, RHOQ, UCHL1, ZNF652, RALGPS2, TPD52, MKNL1, RAPGEF2, and PIAS1. Seven decision trees were built with the use of expression levels of 2 genes as the primary genes: DSG2, a desmosomal cadherin involved in Wnt/β-catenin signaling, and RHOQ, which encodes a cytoskeletal protein involved in insulin-mediated signaling. These trees correct …

Using Automated Machine Learning to Predict Necessary Upcoming Therapy Changes in Patients With Psoriasis Vulgaris and Psoriatic Arthritis and Uncover New Influences on Disease Progression: Retrospective Study.

BACKGROUND: Psoriasis vulgaris (PsV) and psoriatic arthritis (PsA) are complex, multifactorial diseases significantly impacting health and quality of life. Predicting treatment response and disease progression is crucial for optimizing therapeutic interventions, yet challenging. Automated machine learning (AutoML) technology shows promise for rapidly creating accurate predictive models based on patient features and treatment data. OBJECTIVE: This study aims to develop highly accurate machine learning (ML) models using AutoML to address key clinical questions for PsV and PsA patients, includin

[Constructing a multicenter collaboration system].

Providing medical care for Gender Identity Disorder (GID) poses enormous challenges and difficulties. The one obstacle to overcome is building a pluralistic treatment system in collaboration with experts from various fields of medicine as outlined in "Guidelines for Treatment and Diagnosis of GID version 3rd" established by the Japanese Society of Psychiatry and Neurology. Another obstacle includes the multilayered decision tree in deployment of physical as well as psychiatric treatment. Offering continuous supports of the pluralistic treatment system following the multilayered decision tree p

Evaluation of synaptophysin as an immunohistochemical marker for equine grass sickness.

It has been proposed that synaptophysin, an abundant integral membrane protein of synaptic vesicles, is an immunohistochemical marker for degenerating neurons in equine grass sickness (GS). In the present study, a statistically generated decision tree based on assessment of synaptophysin-immunolabelled ileal sections facilitated correct differentiation of all 20 cases of GS and 24 cases of non-GS disease (comprising eight horses with colic, six with neuroparalytic botulism and 10 controls). This technique also facilitated correct diagnosis of GS in all three cases that had been erroneously cla

Trajectory of acute postoperative pain and influencing factors in patients with esophageal cancer: A prospective study.

PURPOSE: Acute postoperative pain after esophageal cancer surgery is highly prevalent and hinders rehabilitation. This study explores the trajectory and influencing factors of acute postoperative pain to guide targeted pain management. METHODS: A prospective design was adopted among 200 patients who had undergone esophageal cancer surgery in a tertiary hospital in Nanjing, China, from June to November 2025. Pain was assessed via the Numerical Rating Scale across 11 postoperative time-points (1 h after surgery, 8 a.m. and 8 p.m. from day 1 to 5 after surgery). Latent class growth analysis iden

Digital screening of children with ASD: diagnostic accuracy of emotion recognition and visual preference tasks.

BACKGROUND: In Germany, there is a need to improve care for suspected autism spectrum disorder (ASD) cases, as the time between parents’ ini … on recognition task, while mixed linear models were used for the visual preference task, along with decision trees for both tasks. RESULTS: The results indicate significant group differences in recognizing the emotion “fear” and naming an example for “sadness”. The emotion recognition of fear and sadness was relevant to the decision tree to differentiate between …

Determinants and risk prediction models for frailty among community-living older adults in eastern China.

OBJECTIVE: The purpose of this study is to develop predictive models for frailty risk among community-dwelling older adults in eastern China … ncompasses physical, psychological, and social dimensions. Predictive models were constructed using decision trees, random forests, and XGBoost algorithms, implemented in R software (version 4.4.2). The performance of these models was evaluated using metrics such as the area under the receiver operating characteristic curve (AUC), ROC curves, and confusion matri …

Understanding the Relationship Between Comorbidities, Medication Nonadherence, Activities of Daily Living, and Heart Condition Status Among Older Adults in the United States: A Regression Analysis and Machine Learning Approach.

BACKGROUND: Nonadherence to medication among patients with heart disease poses serious risks, including worsened heart failure and increased mortality rates. OBJECTIVE: This study aims to explore the complex interplay between comorbidities, medication nonadherence, activities of daily living, and heart condition status in older American adults, using both traditional statistical methods and machine learning. METHODS: Data from 326 older adults with heart conditions, drawn from the Health and Retirement Study, were analyzed. Descriptive statistics characterized demographic profiles and comorb

A novel method to demonstrate thrombus formation of the left atrial appendage in patients with persistent atrial fibrillation by cardiac computed tomography.

BACKGROUND: Transesophageal echocardiography (TEE) is currently the gold standard technique for diagnosing left atrial appendage (LAA) thrombi. Cardiac computed tomography (CT) has been expected to become an alternative method to TEE; however, a reliable quantitative evaluation method has not been established. METHODS AND RESULTS: We enrolled 177 patients with persistent atrial fibrillation who underwent both cardiac CT and TEE before catheter ablation. The patients were classified into two groups according to the TEE results: the thrombus group (13 patients) and non-thrombus group (164 patie

Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework.

BACKGROUND: An estimated 425 million people globally have diabetes, accounting for 12% of the world's health expenditures, and the number continues to grow, placing a huge burden on the healthcare system, especially in those remote, underserved areas. METHODS: A total of 584,168 adult subjects who have participated in the national physical examination were enrolled in this study. The risk factors for type II diabetes mellitus (T2DM) were identified by p values and odds ratio, using logistic regression (LR) based on variables of physical measurement and a questionnaire. Combined with the risk

Strategy to reduce unnecessary surgeries in thyroid nodules with cytology of Bethesda category III (AUS/FLUS): a retrospective analysis of 667 patients diagnosed by surgery.

PURPOSE: Fine-needle aspiration (FNA) is widely used for the diagnosis of thyroid nodules detected by ultrasonography. However, the cytology of atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS) often leads to unnecessary thyroid surgery. This study aimed to identify a strategy to reduce unnecessary surgeries in patients with AUS/FLUS nodules. METHODS: Medical records of 667 patients with the cytology of AUS/FLUS who underwent surgery from January 2007 to December 2017 were retrospectively reviewed. Clinicopathological data were analyzed to identify