AIMC Topic: Retrospective Studies

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Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features.

Journal of the American College of Radiology : JACR Feb 2, 2018
PURPOSE: The aim of this study was to determine whether deep features extracted from digital mammograms using a pretrained deep convolutional neural network are prognostic of occult invasive disease for patients with ductal carcinoma in situ (DCIS) o...
Carcinoma, Intraductal, Noninfiltrating Monte Carlo Method Biopsy, Large-Core Needle Carcinoma in Situ Breast Neoplasms Middle Aged Deep Learning Mammography Neoplasm Staging Humans Female Aged Adult Neural Networks, Computer Prognosis Retrospective Studies
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Using Convolutional Neural Networks for Enhanced Capture of Breast Parenchymal Complexity Patterns Associated with Breast Cancer Risk.

Academic radiology Feb 1, 2018
RATIONALE AND OBJECTIVES: We evaluate utilizing convolutional neural networks (CNNs) to optimally fuse parenchymal complexity measurements generated by texture analysis into discriminative meta-features relevant for breast cancer risk prediction.
Humans Case-Control Studies ROC Curve Mammography Area Under Curve Neural Networks, Computer Breast Retrospective Studies Parenchymal Tissue Female Adult Breast Neoplasms
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Development and Validation of Machine Learning Models for Prediction of 1-Year Mortality Utilizing Electronic Medical Record Data Available at the End of Hospitalization in Multicondition Patients: a Proof-of-Concept Study.

Journal of general internal medicine Jan 30, 2018
BACKGROUND: Predicting death in a cohort of clinically diverse, multicondition hospitalized patients is difficult. Prognostic models that use electronic medical record (EMR) data to determine 1-year death risk can improve end-of-life planning and ris...
Proof of Concept Study Mortality Adult Male Middle Aged Cohort Studies Models, Theoretical Humans Female Machine Learning Hospitalization Reproducibility of Results Data Analysis Aged Risk Factors Forecasting Aged, 80 and over Retrospective Studies Electronic Health Records
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Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning.

Journal of medical Internet research Jan 30, 2018
BACKGROUND: As a high-prevalence health condition, hypertension is clinically costly, difficult to manage, and often leads to severe and life-threatening diseases such as cardiovascular disease (CVD) and stroke.
Retrospective Studies Electronic Health Records Female Risk Factors Aged Hypertension Male Middle Aged Cohort Studies Humans Machine Learning Prospective Studies
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Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU.

BMJ open Jan 26, 2018
OBJECTIVES: We validate a machine learning-based sepsis-prediction algorithm () for the detection and prediction of three sepsis-related gold standards, using only six vital signs. We evaluate robustness to missing data, customisation to site-specifi...
Humans San Francisco Machine Learning Boston Middle Aged Female Patients' Rooms Area Under Curve Severity of Illness Index Aged Algorithms Length of Stay Emergency Service, Hospital Young Adult Databases, Factual Sepsis Vital Signs Hospital Mortality Adult Prognosis Adolescent ROC Curve Male Retrospective Studies Shock, Septic Intensive Care Units
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Characterization of Adrenal Lesions on Unenhanced MRI Using Texture Analysis: A Machine-Learning Approach.

Journal of magnetic resonance imaging : JMRI Jan 17, 2018
BACKGROUND: Adrenal adenomas (AA) are the most common benign adrenal lesions, often characterized based on intralesional fat content as either lipid-rich (LRA) or lipid-poor (LPA). The differentiation of AA, particularly LPA, from nonadenoma adrenal ...
Adrenal Gland Neoplasms Image Interpretation, Computer-Assisted Pattern Recognition, Automated Adrenal Glands Young Adult Adult Male Middle Aged Adolescent Image Processing, Computer-Assisted Algorithms Contrast Media Magnetic Resonance Imaging Retrospective Studies Lipids Female Adenoma Reproducibility of Results Aged Humans Machine Learning
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Classification of breast cancer in ultrasound imaging using a generic deep learning analysis software: a pilot study.

The British journal of radiology Jan 10, 2018
OBJECTIVE: To train a generic deep learning software (DLS) to classify breast cancer on ultrasound images and to compare its performance to human readers with variable breast imaging experience.
Machine Learning Software Algorithms Retrospective Studies Female Humans Clinical Competence Adolescent Switzerland Ultrasonography, Mammary Image Interpretation, Computer-Assisted Observer Variation Aged Aged, 80 and over Adult Breast Neoplasms Middle Aged
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An ensemble boosting model for predicting transfer to the pediatric intensive care unit.

International journal of medical informatics Jan 9, 2018
BACKGROUND: Early deterioration indicators have the potential to alert hospital care staff in advance of adverse events, such as patients requiring an increased level of care, or the need for rapid response teams to be called. Our work focuses on the...
ROC Curve Models, Statistical Health Services Needs and Demand Female Patient Transfer Male Child, Hospitalized Severity of Illness Index Child Intensive Care Units, Pediatric Humans Artificial Intelligence Machine Learning Algorithms Retrospective Studies
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Computer-aided assessment of breast density: comparison of supervised deep learning and feature-based statistical learning.

Physics in medicine and biology Jan 9, 2018
Breast density is one of the most significant factors that is associated with cancer risk. In this study, our purpose was to develop a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammograms (DMs). ...
Mammography Radiographic Image Interpretation, Computer-Assisted Breast Breast Density Adult Humans Breast Neoplasms Retrospective Studies Middle Aged Female Deep Learning Aged Models, Statistical Aged, 80 and over
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Predicting post-stroke activities of daily living through a machine learning-based approach on initiating rehabilitation.

International journal of medical informatics Jan 4, 2018
OBJECTIVES: Prediction of activities of daily living (ADL) is crucial for optimized care of post-stroke patients. However, no suitably-validated and practical models are currently available in clinical practice.
Stroke Activities of Daily Living Stroke Rehabilitation Decision Making, Computer-Assisted Humans Aged Male Machine Learning Middle Aged Algorithms Taiwan Retrospective Studies Patient Discharge Female
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