AIMC Topic: Decision Making, Computer-Assisted
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Ada-WHIPS: explaining AdaBoost classification with applications in the health sciences.
BMC medical informatics and decision making
Oct 2, 2020
BACKGROUND: Computer Aided Diagnostics (CAD) can support medical practitioners to make critical decisions about their patients' disease conditions. Practitioners require access to the chain of reasoning behind CAD to build trust in the CAD advice and...
Pituitary Tumors in the Computational Era, Exploring Novel Approaches to Diagnosis, and Outcome Prediction with Machine Learning.
World neurosurgery
Jul 22, 2020
BACKGROUND: Machine learning has emerged as a viable asset in the setting of pituitary surgery. In the past decade, the number of machine learning models developed to aid in the diagnosis of pituitary lesions and predict intraoperative and postoperat...
Medical data science in rhinology: Background and implications for clinicians.
American journal of otolaryngology
Jul 2, 2020
BACKGROUND: An important challenge of big data is using complex information networks to provide useful clinical information. Recently, machine learning, and particularly deep learning, has enabled rapid advances in clinical practice. The application ...
Machine learning-based prediction of transfusion.
Transfusion
Jun 28, 2020
BACKGROUND: The ability to predict transfusions arising during hospital admission might enable economized blood supply management and might furthermore increase patient safety by ensuring a sufficient stock of red blood cells (RBCs) for a specific pa...
Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review.
BioMed research international
Jun 10, 2020
BACKGROUND: The purpose of this review is to depict current research and impact of artificial intelligence/machine learning (AI/ML) algorithms on dialysis and kidney transplantation. Published studies were presented from two points of view: What medi...
Application of explainable ensemble artificial intelligence model to categorization of hemodialysis-patient and treatment using nationwide-real-world data in Japan.
PloS one
May 29, 2020
BACKGROUND: Although dialysis patients are at a high risk of death, it is difficult for medical practitioners to simultaneously evaluate many inter-related risk factors. In this study, we evaluated the characteristics of hemodialysis patients using m...
Decision Making, Computer-Assisted
Humans
Deep Learning
Artificial Intelligence
Renal Dialysis
Aged, 80 and over
Machine Learning
Cohort Studies
Adult
Female
Databases, Factual
Logistic Models
Male
Support Vector Machine
Risk Factors
Prospective Studies
Middle Aged
Cluster Analysis
Aged
Prognosis
Kidney Failure, Chronic
Japan
Artificial Intelligence and Digital Tools: Future of Diabetes Care.
Clinics in geriatric medicine
Apr 17, 2020
Diabetes mellitus has become a global threat, especially in the emerging economies. In the United States, there are about 24 million people with diabetes mellitus. Diabetes represents a trove of physiologic and sociologic data that are only superfici...
Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury.
Journal of clinical epidemiology
Mar 20, 2020
OBJECTIVE: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury.
Predictive models for patients with lung carcinomas to identify EGFR mutation status via an artificial neural network based on multiple clinical information.
Journal of cancer research and clinical oncology
Dec 5, 2019
PURPOSE: Epidermal growth factor receptor (EGFR) mutation testing has several limitations. Therefore, we built predictive models to determine the EGFR mutation status of patients and guide therapeutic decision-making.
Hash Transformation and Machine Learning-Based Decision-Making Classifier Improved the Accuracy Rate of Automated Parkinson's Disease Screening.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Oct 29, 2019
Digitalized hand-drawn pattern is a noninvasive and reproducible assistive manner to obtain hand actions and motions for evaluating functional tremors and upper-limb movement disorders. In this study, spirals and straight lines in polar coordinates a...
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