AIMC Topic: Follow-Up Studies

Clear Filters Showing 711 to 720 of 788 articles

Machine Learning-Based Computational Models Derived From Large-Scale Radiographic-Radiomic Images Can Help Predict Adverse Histopathological Status of Gastric Cancer.

Clinical and translational gastroenterology Oct 1, 2019
INTRODUCTION: Adverse histopathological status (AHS) decreases outcomes of gastric cancer (GC). With the lack of a single factor with great reliability to preoperatively predict AHS, we developed a computational approach by integrating large-scale im...
Female Reproducibility of Results Stomach Neoplasms Male Middle Aged Prevalence Machine Learning Computer Simulation Prognosis Retrospective Studies Tomography, X-Ray Computed Neoplasm Recurrence, Local Gastrectomy Disease-Free Survival Preoperative Period Kaplan-Meier Estimate Stomach Image Processing, Computer-Assisted Follow-Up Studies Humans Contrast Media Neoplasm Invasiveness Neoplasm Staging
View on PubMed DOI

Comparison of Machine Learning Optimal Classification Trees With the Pediatric Emergency Care Applied Research Network Head Trauma Decision Rules.

JAMA pediatrics Jul 1, 2019
IMPORTANCE: Computed tomographic (CT) scanning is the standard for the rapid diagnosis of intracranial injury, but it is costly and exposes patients to ionizing radiation. The Pediatric Emergency Care Applied Research Network (PECARN) rules for ident...
Infant Adolescent Child, Preschool Child Machine Learning Emergency Medical Services Follow-Up Studies Humans Decision Support Techniques Triage Prospective Studies Emergency Service, Hospital Female Trauma Severity Indices Infant, Newborn Craniocerebral Trauma Male
View on PubMed DOI

Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers.

Annals of oncology : official journal of the European Society for Medical Oncology Jun 1, 2019
INTRODUCTION: Immunotherapy is regarded as one of the major breakthroughs in cancer treatment. Despite its success, only a subset of patients responds-urging the quest for predictive biomarkers. We hypothesize that artificial intelligence (AI) algori...
Tomography, X-Ray Computed Melanoma Survival Rate Lung Neoplasms Immunotherapy Antineoplastic Combined Chemotherapy Protocols Humans Artificial Intelligence Machine Learning Algorithms Prognosis Carcinoma, Non-Small-Cell Lung Follow-Up Studies Predictive Value of Tests Programmed Cell Death 1 Receptor
View on PubMed DOI

Improved Interpretability of Machine Learning Model Using Unsupervised Clustering: Predicting Time to First Treatment in Chronic Lymphocytic Leukemia.

JCO clinical cancer informatics May 1, 2019
PURPOSE: Time to event is an important aspect of clinical decision making. This is particularly true when diseases have highly heterogeneous presentations and prognoses, as in chronic lymphocytic lymphoma (CLL). Although machine learning methods can ...
Models, Theoretical Treatment Outcome Follow-Up Studies Cluster Analysis Time-to-Treatment Kaplan-Meier Estimate Aged Adult Leukemia, Lymphocytic, Chronic, B-Cell Male Middle Aged Humans Machine Learning Prognosis Female Reproducibility of Results Risk Factors
View on PubMed DOI

Does component placement affect short-term clinical outcome in robotic-arm assisted unicompartmental knee arthroplasty?

The bone & joint journal Apr 1, 2019
AIMS: The purpose of this multicentre observational study was to investigate the association between intraoperative component positioning and soft-tissue balancing on short-term clinical outcomes in patients undergoing robotic-arm assisted unicompart...
Humans Prospective Studies Tomography, X-Ray Computed Arthroplasty, Replacement, Knee Patient Satisfaction Female Imaging, Three-Dimensional Femur Aged Knee Prosthesis Treatment Outcome Male Osteoarthritis, Knee Follow-Up Studies Middle Aged Robotics Time Factors Equipment Design Knee Joint
View on PubMed DOI

Supervised machine learning for the prediction of infection on admission to hospital: a prospective observational cohort study.

The Journal of antimicrobial chemotherapy Apr 1, 2019
BACKGROUND: Infection diagnosis can be challenging, relying on clinical judgement and non-specific markers of infection. We evaluated a supervised machine learning (SML) algorithm for diagnosing bacterial infection using routinely available blood par...
Disease Management Diagnostic Tests, Routine Infections Patient Admission Hematologic Tests Middle Aged Cohort Studies ROC Curve Decision Support Systems, Clinical Follow-Up Studies Aged Supervised Machine Learning Aged, 80 and over Young Adult Adult Male Humans Algorithms Biomarkers Prognosis Clinical Decision-Making Female
View on PubMed DOI

Artificial intelligence estimates the impact of human papillomavirus types in influencing the risk of cervical dysplasia recurrence: progress toward a more personalized approach.

European journal of cancer prevention : the official journal of the European Cancer Prevention Organisation (ECP) Mar 1, 2019
The objective of this study was to determine whether the pretreatment human papillomavirus (HPV) genotype might predict the risk of cervical dysplasia persistence/recurrence. Retrospective analysis of prospectively collected data of consecutive 5104 ...
Case-Control Studies Papillomavirus Infections DNA, Viral Follow-Up Studies Cervix Uteri Prognosis Neoplasm Recurrence, Local Retrospective Studies Uterine Cervical Dysplasia Female Papillomaviridae Risk Factors Humans Adult Artificial Intelligence Uterine Cervical Neoplasms Prospective Studies
View on PubMed DOI

Deep Learning for Prediction of AMD Progression: A Pilot Study.

Investigative ophthalmology & visual science Feb 1, 2019
PURPOSE: To develop and assess a method for predicting the likelihood of converting from early/intermediate to advanced wet age-related macular degeneration (AMD) using optical coherence tomography (OCT) imaging and methods of deep learning.
Pilot Projects ROC Curve Follow-Up Studies Humans Tomography, Optical Coherence Wet Macular Degeneration Neural Networks, Computer Machine Learning Male Female Middle Aged Aged Disease Progression Aged, 80 and over Diagnosis, Computer-Assisted Deep Learning
View on PubMed DOI

Machine Learning for the Prediction of New-Onset Diabetes Mellitus during 5-Year Follow-up in Non-Diabetic Patients with Cardiovascular Risks.

Yonsei medical journal Feb 1, 2019
PURPOSE: Many studies have proposed predictive models for type 2 diabetes mellitus (T2DM). However, these predictive models have several limitations, such as user convenience and reproducibility. The purpose of this study was to develop a T2DM predic...
Humans Risk Factors Logistic Models Male Follow-Up Studies Machine Learning Cardiovascular Diseases Algorithms Middle Aged Republic of Korea ROC Curve Female Diabetes Mellitus, Type 2 Reproducibility of Results
View on PubMed DOI

Using Elastic Net Penalized Cox Proportional Hazards Regression to Identify Predictors of Imminent Smoking Lapse.

Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco Jan 4, 2019
INTRODUCTION: Machine learning algorithms such as elastic net regression and backward selection provide a unique and powerful approach to model building given a set of psychosocial predictors of smoking lapse measured repeatedly via ecological moment...
Middle Aged Smokers Humans Smartphone Recurrence Machine Learning Follow-Up Studies Smoking Cessation Predictive Value of Tests Female Ecological Momentary Assessment Proportional Hazards Models Tobacco Smoking Adult Male
View on PubMed DOI
Popular Topics
Recent Journals