AIMC Topic: Follow-Up Studies

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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 Humans Gastrectomy Disease-Free Survival Image Processing, Computer-Assisted Preoperative Period Kaplan-Meier Estimate Follow-Up Studies Stomach Contrast Media Neoplasm Invasiveness Neoplasm Staging
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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 Decision Support Techniques Humans Triage Prospective Studies Emergency Service, Hospital Female Trauma Severity Indices Infant, Newborn Craniocerebral Trauma Male
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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
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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
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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...
Arthroplasty, Replacement, Knee Patient Satisfaction Humans Female Imaging, Three-Dimensional Aged Prospective Studies Femur Male Treatment Outcome Tomography, X-Ray Computed Knee Prosthesis Middle Aged Follow-Up Studies Osteoarthritis, Knee Time Factors Robotics Equipment Design Knee Joint
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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 Middle Aged Diagnostic Tests, Routine Cohort Studies Infections Humans Aged ROC Curve Patient Admission Algorithms Decision Support Systems, Clinical Aged, 80 and over Hematologic Tests Follow-Up Studies Biomarkers Young Adult Supervised Machine Learning Adult Prognosis Male Clinical Decision-Making Female
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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 Neoplasm Recurrence, Local Uterine Cervical Dysplasia Prognosis Papillomaviridae Retrospective Studies Humans Female Artificial Intelligence Risk Factors Prospective Studies Adult Uterine Cervical Neoplasms
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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 Tomography, Optical Coherence Wet Macular Degeneration Male Middle Aged Disease Progression Diagnosis, Computer-Assisted Deep Learning Humans Neural Networks, Computer Machine Learning Female Aged Aged, 80 and over
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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 Male Cardiovascular Diseases Middle Aged Logistic Models ROC Curve Machine Learning Follow-Up Studies Diabetes Mellitus, Type 2 Algorithms Republic of Korea Female Reproducibility of Results
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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...
Smokers Middle Aged Recurrence Humans Smartphone Machine Learning Smoking Cessation Follow-Up Studies Female Ecological Momentary Assessment Predictive Value of Tests Adult Tobacco Smoking Proportional Hazards Models Male
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