AIMC Topic: Algorithms

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Reporting on deep learning algorithms in health care.

The Lancet. Digital health Nov 1, 2019
Deep Learning Humans Models, Statistical Algorithms Bias Delivery of Health Care
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AI Copernicus 'discovers' that Earth orbits the Sun.

Nature Nov 1, 2019
Neural Networks, Computer Machine Learning Algorithms Computer Simulation Physics Planets
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Relevant Features in Nonalcoholic Steatohepatitis Determined Using Machine Learning for Feature Selection.

Metabolic syndrome and related disorders Nov 1, 2019
We investigated the prevalence and the most relevant features of nonalcoholic steatohepatitis (NASH), a stage of nonalcoholic fatty liver disease, (NAFLD) in which the inflammation of hepatocytes can lead to increased cardiovascular risk, liver fibr...
Metabolic Syndrome Decision Making Non-alcoholic Fatty Liver Disease Diabetes Mellitus, Type 2 Liver Cirrhosis Hypertension Disease Progression Prevalence Severity of Illness Index Cross-Sectional Studies Male Middle Aged Aged Humans Adult Prognosis Sensitivity and Specificity Machine Learning Retrospective Studies Female Algorithms
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No pixel-level annotations needed.

Nature biomedical engineering Nov 1, 2019
Humans Machine Learning Computational Biology Algorithms Databases, Factual Diagnostic Imaging Neoplasms User-Computer Interface
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High-throughput multimodal automated phenotyping (MAP) with application to PheWAS.

Journal of the American Medical Informatics Association : JAMIA Nov 1, 2019
OBJECTIVE: Electronic health records linked with biorepositories are a powerful platform for translational studies. A major bottleneck exists in the ability to phenotype patients accurately and efficiently. The objective of this study was to develop ...
Unified Medical Language System Algorithms Humans Area Under Curve Phenotype International Classification of Diseases Polymorphism, Single Nucleotide Electronic Health Records Natural Language Processing
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Development and Validation of a Machine Learning Algorithm for Predicting Response to Anticholinergic Medications for Overactive Bladder Syndrome.

Obstetrics and gynecology Nov 1, 2019
OBJECTIVE: To develop and externally validate a prediction model for anticholinergic response in patients with overactive bladder (OAB).
Urinary Bladder, Overactive Urinary Incontinence Cholinergic Antagonists Reproducibility of Results Aged Adult Middle Aged Predictive Value of Tests Area Under Curve Algorithms Prognosis Female Humans Machine Learning
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NimbleMiner: An Open-Source Nursing-Sensitive Natural Language Processing System Based on Word Embedding.

Computers, informatics, nursing : CIN Nov 1, 2019
This study develops and evaluates an open-source software (called NimbleMiner) that allows clinicians to interact with word embedding models with a goal of creating lexicons of similar terms. As a case study, the system was used to identify similar t...
Humans Algorithms Electronic Health Records Natural Language Processing Nursing Process Software Design
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Transparent Reporting on Research Using Unstructured Electronic Health Record Data to Generate 'Real World' Evidence of Comparative Effectiveness and Safety.

Drug safety Nov 1, 2019
Research that makes secondary use of administrative and clinical healthcare databases is increasingly influential for regulatory, reimbursement, and other healthcare decision-making. Consequently, there are numerous guidance documents on reporting fo...
Humans Algorithms Research Design Decision Support Techniques Information Storage and Retrieval Databases, Factual Electronic Health Records Natural Language Processing
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A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock: Development, Implementation, and Impact on Clinical Practice.

Critical care medicine Nov 1, 2019
OBJECTIVES: Develop and implement a machine learning algorithm to predict severe sepsis and septic shock and evaluate the impact on clinical practice and patient outcomes.
Text Messaging Hospitals, Teaching Cohort Studies Algorithms Humans Decision Support Systems, Clinical Retrospective Studies Machine Learning Electronic Health Records Shock, Septic Sensitivity and Specificity Diagnosis, Computer-Assisted Sepsis
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Cost-aware active learning for named entity recognition in clinical text.

Journal of the American Medical Informatics Association : JAMIA Nov 1, 2019
OBJECTIVE: Active Learning (AL) attempts to reduce annotation cost (ie, time) by selecting the most informative examples for annotation. Most approaches tacitly (and unrealistically) assume that the cost for annotating each sample is identical. This ...
Humans Big Data Natural Language Processing Models, Economic Information Storage and Retrieval Algorithms Computer Simulation Electronic Health Records
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