Clinical Trials

Creating efficiencies in the extraction of data from randomized trials: a prospective evaluation of a machine learning and text mining tool.

BACKGROUND: Machine learning tools that semi-automate data extraction may create efficiencies in systematic review production. We evaluated a machine learning and text mining tool's ability to (a) automatically extract data elements from randomized trials, and (b) save time compared with manual extraction and verification.

Machine learning using clinical data at baseline predicts the efficacy of vedolizumab at week 22 in patients with ulcerative colitis.

Predicting the response of patients with ulcerative colitis (UC) to a biologic such as vedolizumab (VDZ) before administration is an unmet need for optimizing individual patient treatment. We hypothesized that the machine-learning approach with daily clinical information can be a new, promising strategy for developing a drug-efficacy prediction tool. Random forest with grid search and cross-valida...

iPromoter-ET: Identifying promoters and their strength by extremely randomized trees-based feature selection.

Promoter is a region of DNA that determines the transcription of a particular gene. There are several σ factors in the RNA polymerase, which has the f...

Deep Learning to Determine the Activity of Pulmonary Tuberculosis on Chest Radiographs.

Background Determining the activity of pulmonary tuberculosis on chest radiographs is difficult. Purpose To develop a deep learning model to identify ...

The Humanoid Robot Sil-Bot in a Cognitive Training Program for Community-Dwelling Elderly People with Mild Cognitive Impairment during the COVID-19 Pandemic: A Randomized Controlled Trial.

BACKGROUND: Mild cognitive impairment (MCI) is a stage preceding dementia, and early intervention is critical. This study investigated whether multi-d...

Can machine learning improve randomized clinical trial analysis?

PURPOSE: Recently a realistic simulator of patient seizure diaries was developed that can reproduce effects seen in randomized clinical trials (RCTs)....

Artificial intelligence-assisted clinical decision support for childhood asthma management: A randomized clinical trial.

RATIONALE: Clinical decision support (CDS) tools leveraging electronic health records (EHRs) have been an approach for addressing challenges in asthma...

Automated classification of clinical trial eligibility criteria text based on ensemble learning and metric learning.

BACKGROUND: Eligibility criteria are the primary strategy for screening the target participants of a clinical trial. Automated classification of clini...

The path to precision medicine for MS, from AI to patient recruitment: an interview with Mauricio Farez and Helen Onuorah.

This year’s World Brain Day is focused on stopping Multiple Sclerosis (MS). Although amazing progress has resulted in the development of relatively su...

Recent advancement in nano-optical strategies for detection of pathogenic bacteria and their metabolites in food safety.

Pathogenic bacteria and their metabolites are the leading risk factor in food safety and are one of the major threats to human health because of the c...

Efficacy and safety of an open lung ventilation strategy with staircase recruitment followed by comparison on two different modes of ventilation, in moderate ARDS in cirrhosis: A pilot randomized trial.

BACKGROUND: Mechanical ventilation in cirrhosis with acute respiratory distress syndrome (ARDS) is not widely studied. We aimed to study the effect of...

Generalizability of heterogeneous treatment effects based on causal forests applied to two randomized clinical trials of intensive glycemic control.

Purpose Machine learning is an attractive tool for identifying heterogeneous treatment effects (HTE) of interventions but generalizability of machine ...

Value of a deep learning-based algorithm for detecting Lung-RADS category 4 nodules on chest radiographs in a health checkup population: estimation of the sample size for a randomized controlled trial.

OBJECTIVE: To explore the value of a deep learning-based algorithm in detecting Lung CT Screening Reporting and Data System category 4 nodules on ches...

Bayesian Modeling for the Detection of Adverse Events Underreporting in Clinical Trials.

INTRODUCTION: Safety underreporting is a recurrent issue in clinical trials that can impact patient safety and data integrity. Clinical quality assura...

Robot-Assisted Gait Training in Patients with Multiple Sclerosis: A Randomized Controlled Crossover Trial.

Gait disorders represent one of the most disabling aspects in multiple sclerosis (MS) that strongly influence patient quality of life. The improvemen...

Is off-clamp robot-assisted partial nephrectomy beneficial for renal function? Data from the CLOCK trial.

OBJECTIVES: To compare the functional outcomes of on- vs off-clamp robot-assisted partial nephrectomy (RAPN) within a randomized controlled trial (RCT...

Reducing adenoma miss rate of colonoscopy assisted by artificial intelligence: a multicenter randomized controlled trial.

BACKGROUND: We have developed the computer-aided detection (CADe) system using an original deep learning algorithm based on a convolutional neural net...

Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning.

Expression of CCR5 and its cognate ligands have been implicated in COVID-19 pathogenesis, consequently therapeutics directed against CCR5 are being in...

Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics.

Selecting the right tuning parameters for algorithms is a pravelent problem in machine learning that can significantly affect the performance of algor...

Occupational profiling driven by online job advertisements: Taking the data analysis and processing engineering technicians as an example.

The occupational profiling system driven by the traditional survey method has some shortcomings such as lag in updating, time consumption and laboriou...