AIMC Topic: Clinical Decision-Making

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Salivary metabolomics with alternative decision tree-based machine learning methods for breast cancer discrimination.

Breast cancer research and treatment Jul 8, 2019
PURPOSE: The aim of this study is to explore new salivary biomarkers to discriminate breast cancer patients from healthy controls.
Humans Machine Learning Cross-Sectional Studies Middle Aged ROC Curve Metabolomics Saliva Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Clinical Decision-Making Female Aged Aged, 80 and over Adult Breast Neoplasms
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Towards integrating personalized feedback research into clinical practice: Development of the Trier Treatment Navigator (TTN).

Behaviour research and therapy Jul 8, 2019
In this study, a computer-based feedback, decision and clinical problem-solving system for clinical practice will be described - the Trier Treatment Navigator (TTN). The paper deals with the underlying research concepts related to personalized pre-tr...
Cognitive Behavioral Therapy Precision Medicine Adjustment Disorders Adult Patient Dropouts Humans Clinical Decision-Making Personality Disorders Feedback Machine Learning Female Male Mental Disorders Aged Adolescent Middle Aged Young Adult Stress Disorders, Post-Traumatic Anxiety Disorders Decision Support Systems, Clinical Surveys and Questionnaires Mood Disorders Problem Solving
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Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention.

JACC. Cardiovascular interventions Jun 26, 2019
OBJECTIVES: This study sought to determine whether machine learning can be used to better identify patients at risk for death or congestive heart failure (CHF) rehospitalization after percutaneous coronary intervention (PCI).
Treatment Outcome Predictive Value of Tests Registries Percutaneous Coronary Intervention Coronary Artery Disease Heart Failure Patient Readmission Minnesota Aged Male Middle Aged Risk Assessment Time Factors Hospital Mortality Decision Support Techniques Clinical Decision-Making Female Reproducibility of Results Risk Factors Humans Machine Learning
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Machine Learning Prediction of Response to Cardiac Resynchronization Therapy: Improvement Versus Current Guidelines.

Circulation. Arrhythmia and electrophysiology Jun 20, 2019
BACKGROUND: Cardiac resynchronization therapy (CRT) has significant nonresponse rates. We assessed whether machine learning (ML) could predict CRT response beyond current guidelines.
Male Ventricular Function, Left Stroke Volume Echocardiography Middle Aged Risk Assessment Retrospective Studies Clinical Decision-Making Female Risk Factors Aged Heart Failure Cardiac Resynchronization Therapy Progression-Free Survival Practice Guidelines as Topic Heart-Assist Devices Heart Transplantation Disease Progression Time Factors Recovery of Function Predictive Value of Tests Patient Selection Humans Machine Learning Decision Support Techniques Ohio Baltimore
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Collateral Automation for Triage in Stroke: Evaluating Automated Scoring of Collaterals in Acute Stroke on Computed Tomography Scans.

Cerebrovascular diseases (Basel, Switzerland) Jun 19, 2019
Computed tomography angiography (CTA) collateral scoring can identify patients most likely to benefit from mechanical thrombectomy and those more likely to have good outcomes and ranges from 0 (no collaterals) to 3 (complete collaterals). In this stu...
Thrombectomy Collateral Circulation Triage Humans Cerebrovascular Circulation Radiographic Image Interpretation, Computer-Assisted Machine Learning Middle Cerebral Artery Stroke Prognosis Computed Tomography Angiography Patient Selection Clinical Decision-Making Automation Cerebral Angiography Predictive Value of Tests Blood Flow Velocity
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Embracing machine learning and digital health technology for precision dermatology.

The Journal of dermatological treatment Jun 14, 2019
Involvement from the dermatology community in the current revolution in sensors, personal health devices, computing, and machine learning algorithms can enable us to reach the promise of precision medicine in dermatology to deliver the 'right treatme...
Delivery of Health Care Humans Precision Medicine Machine Learning Clinical Decision-Making Algorithms
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Ventricular geometry-regularized QRSd predicts cardiac resynchronization therapy response: machine learning from crosstalk between electrocardiography and echocardiography.

The international journal of cardiovascular imaging May 18, 2019
Up to one-third of patients selected by current guidelines do not respond to cardiac resynchronization therapy (CRT), the aim of this study was to find out novel analytical approaches to improve pre-implantation CRT response prediction. Among 31 pre-...
Aged, 80 and over Male Ventricular Function, Left Echocardiography Middle Aged Humans Electrocardiography Heart Failure Clinical Decision-Making Treatment Outcome Cardiac Resynchronization Therapy Female Support Vector Machine Ventricular Remodeling Predictive Value of Tests Aged Image Interpretation, Computer-Assisted Signal Processing, Computer-Assisted Patient Selection
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Machine Learning to Predict Outcomes in Patients with Acute Gastrointestinal Bleeding: A Systematic Review.

Digestive diseases and sciences May 4, 2019
Risk stratification of patients with gastrointestinal bleeding (GIB) is recommended, but current risk assessment tools have variable performance. Machine learning (ML) has promise to improve risk assessment. We performed a systematic review to evalua...
Aged Treatment Outcome Male Neural Networks, Computer Patient Selection Humans Middle Aged Machine Learning Gastrointestinal Hemorrhage Risk Assessment Decision Support Techniques Hemostatic Techniques Time Factors Clinical Decision-Making Female Risk Factors
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New Multiparametric Analysis of Cardiac Dyssynchrony: Machine Learning and Prediction of Response to CRT.

JACC. Cardiovascular imaging Apr 17, 2019
Ventricular Dysfunction, Left Image Interpretation, Computer-Assisted Decision Support Techniques Treatment Outcome Predictive Value of Tests Retrospective Studies Signal Processing, Computer-Assisted Humans Clinical Decision-Making Heart Failure Machine Learning Ventricular Function, Left Cardiac Resynchronization Therapy Echocardiography Electrocardiography
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Dynamic multi-outcome prediction after injury: Applying adaptive machine learning for precision medicine in trauma.

PloS one Apr 10, 2019
OBJECTIVE: Machine learning techniques have demonstrated superior discrimination compared to conventional statistical approaches in predicting trauma death. The objective of this study is to evaluate whether machine learning algorithms can be used to...
Risk Assessment Time Factors ROC Curve Blood Transfusion Humans Respiratory Distress Syndrome Models, Biological Machine Learning Multiple Organ Failure Clinical Decision-Making Decision Support Techniques Venous Thromboembolism Female Prospective Studies Wounds and Injuries Adult Prognosis Male Middle Aged
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