AIMC Topic: False Positive Reactions

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Neural networks versus Logistic regression for 30 days all-cause readmission prediction.

Scientific reports Jun 26, 2019
Heart failure (HF) is one of the leading causes of hospital admissions in the US. Readmission within 30 days after a HF hospitalization is both a recognized indicator for disease progression and a source of considerable financial burden to the health...
ROC Curve Logistic Models United States Predictive Value of Tests Area Under Curve Heart Failure Probability Patient Readmission False Positive Reactions Risk Humans Neural Networks, Computer Machine Learning Algorithms Female Aged Male Middle Aged Deep Learning
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Prediction of early colorectal cancer metastasis by machine learning using digital slide images.

Computer methods and programs in biomedicine Jun 22, 2019
BACKGROUND AND OBJECTIVES: Prediction of lymph node metastasis (LNM) for early colorectal cancer (CRC) is critical for determining treatment strategies after endoscopic resection. Some histologic parameters for predicting LNM have been established, b...
Colorectal Neoplasms Male Middle Aged Retrospective Studies Humans Female Lymph Nodes Machine Learning Risk Factors Lymphatic Metastasis Algorithms Neoplasm Metastasis Immunohistochemistry False Negative Reactions False Positive Reactions Keratins Aged Endoscopy
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Development and Validation of a Bayesian Network Method to Detect External Beam Radiation Therapy Physician Order Errors.

International journal of radiation oncology, biology, physics May 31, 2019
PURPOSE: To investigate a Bayesian network (BN)-based method to detect errors in external beam radiation therapy physician orders.
Cohort Studies ROC Curve Bayes Theorem Radiologists Radiotherapy Planning, Computer-Assisted Humans Neural Networks, Computer Dose Fractionation, Radiation Radiotherapy, Image-Guided Simulation Training Medical Errors Radiotherapy Setup Errors Datasets as Topic False Negative Reactions Technology, Radiologic False Positive Reactions Radiotherapy Dosage Organ Specificity Algorithms Neoplasms
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Gene shaving using a sensitivity analysis of kernel based machine learning approach, with applications to cancer data.

PloS one May 23, 2019
BACKGROUND: Gene shaving (GS) is an essential and challenging tools for biomedical researchers due to the large number of genes in human genome and the complex nature of biological networks. Most GS methods are not applicable to non-linear and multi-...
Colonic Neoplasms Nonlinear Dynamics False Positive Reactions Oligonucleotide Array Sequence Analysis Genetic Techniques Algorithms Computer Simulation Gene Expression Profiling Computational Biology Area Under Curve Humans Artificial Intelligence Machine Learning Software
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Digital hair segmentation using hybrid convolutional and recurrent neural networks architecture.

Computer methods and programs in biomedicine May 15, 2019
BACKGROUND AND OBJECTIVE: Skin melanoma is one of the major health problems in many countries. Dermatologists usually diagnose melanoma by visual inspection of moles. Digital hair removal can provide a non-invasive way to remove hair and hair-like re...
Humans Neural Networks, Computer Skin Neoplasms Algorithms Reproducibility of Results Databases, Factual Diagnosis, Computer-Assisted Melanoma Deep Learning Image Processing, Computer-Assisted Skin Artifacts False Positive Reactions Hair
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A machine learning-based approach for predicting the outbreak of cardiovascular diseases in patients on dialysis.

Computer methods and programs in biomedicine May 13, 2019
BACKGROUND AND OBJECTIVE: Patients with End- Stage Kidney Disease (ESKD) have a unique cardiovascular risk. This study aims at predicting, with a certain precision, death and cardiovascular diseases in dialysis patients.
Female Aged Sensitivity and Specificity Male Cardiovascular Diseases Middle Aged Support Vector Machine Models, Statistical Bayes Theorem Kidney Failure, Chronic Machine Learning Algorithms Databases, Factual Biomarkers Prognosis Humans Italy Registries False Positive Reactions Risk
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Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study.

European radiology Apr 16, 2019
PURPOSE: To study the feasibility of automatically identifying normal digital mammography (DM) exams with artificial intelligence (AI) to reduce the breast cancer screening reading workload.
False Negative Reactions False Positive Reactions Humans Artificial Intelligence Mammography Radiologists Female Workload Breast Neoplasms Feasibility Studies ROC Curve Probability Mass Screening Early Detection of Cancer
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Lungs nodule detection framework from computed tomography images using support vector machine.

Microscopy research and technique Apr 11, 2019
The emergence of cloud infrastructure has the potential to provide significant benefits in a variety of areas in the medical imaging field. The driving force behind the extensive use of cloud infrastructure for medical image processing is the exponen...
False Positive Reactions Sensitivity and Specificity Lung Image Processing, Computer-Assisted Lung Neoplasms Support Vector Machine Humans Tomography, X-Ray Computed
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Machine learning applied to multi-sensor information to reduce false alarm rate in the ICU.

Journal of clinical monitoring and computing Apr 6, 2019
Studies reveal that the false alarm rate (FAR) demonstrated by intensive care unit (ICU) vital signs monitors ranges from 0.72 to 0.99. We applied machine learning (ML) to ICU multi-sensor information to imitate a medical specialist in diagnosing pat...
Humans Machine Learning Decision Support Techniques Retrospective Studies Intensive Care Units Monitoring, Physiologic Knowledge Bases Critical Care Expert Systems Pattern Recognition, Automated False Positive Reactions Clinical Alarms
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Automated Segmentation of Colorectal Tumor in 3D MRI Using 3D Multiscale Densely Connected Convolutional Neural Network.

Journal of healthcare engineering Jan 31, 2019
The main goal of this work is to automatically segment colorectal tumors in 3D T2-weighted (T2w) MRI with reasonable accuracy. For such a purpose, a novel deep learning-based algorithm suited for volumetric colorectal tumor segmentation is proposed. ...
Magnetic Resonance Imaging False Positive Reactions Intestine, Large Deep Learning Imaging, Three-Dimensional Image Processing, Computer-Assisted Contrast Media Humans Neural Networks, Computer Algorithms Colorectal Neoplasms
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