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Statistical outbreak detection by joining medical records and pathogen similarity.
Journal of biomedical informatics
Feb 13, 2019
We present a statistical inference model for the detection and characterization of outbreaks of hospital associated infection. The approach combines patient exposures, determined from electronic medical records, and pathogen similarity, determined by...
Predictive Abilities of Machine Learning Techniques May Be Limited by Dataset Characteristics: Insights From the UNOS Database.
Journal of cardiac failure
Feb 6, 2019
BACKGROUND: Traditional statistical approaches to prediction of outcomes have drawbacks when applied to large clinical databases. It is hypothesized that machine learning methodologies might overcome these limitations by considering higher-dimensiona...
Enhancing Retrosynthetic Reaction Prediction with Deep Learning Using Multiscale Reaction Classification.
Journal of chemical information and modeling
Feb 1, 2019
Chemical synthesis planning is a key aspect in many fields of chemistry, especially drug discovery. Recent implementations of machine learning and artificial intelligence techniques for retrosynthetic analysis have shown great potential to improve co...
Making Policy on Augmented Intelligence in Health Care.
AMA journal of ethics
Feb 1, 2019
In June 2018, the American Medical Association adopted new policy to provide a broad framework for the evolution of artificial intelligence (AI) in health care that is designed to help ensure that AI realizes the benefits it promises for patients, ph...
Are Current Tort Liability Doctrines Adequate for Addressing Injury Caused by AI?
AMA journal of ethics
Feb 1, 2019
As capabilities of predictive algorithms improve, machine learning will become an important element of physician practice and patient care. Implementation of artificial intelligence (AI) raises complex legal questions regarding health care profession...
How Should Clinicians Communicate With Patients About the Roles of Artificially Intelligent Team Members?
AMA journal of ethics
Feb 1, 2019
This commentary responds to a hypothetical case involving an assistive artificial intelligence (AI) surgical device and focuses on potential harms emerging from interactions between humans and AI systems. Informed consent and responsibility-specifica...
Natural Language Processing-Identified Problem Opioid Use and Its Associated Health Care Costs.
Journal of pain & palliative care pharmacotherapy
Jan 31, 2019
Use of prescription opioids and problems of abuse and addiction have increased over the past decade. Claims-based studies have documented substantial economic burden of opioid abuse. This study utilized electronic health record (EHR) data to identify...
Male
Opioid-Related Disorders
Middle Aged
Health Care Costs
Female
Case-Control Studies
Natural Language Processing
Humans
Adolescent
Cost of Illness
Aged
Analgesics, Opioid
Retrospective Studies
Young Adult
United States
Electronic Health Records
Medicare
Adult
Longitudinal Studies
Follow-Up Studies
Using Machine Learning To Predict Antimicrobial MICs and Associated Genomic Features for Nontyphoidal .
Journal of clinical microbiology
Jan 30, 2019
Nontyphoidal species are the leading bacterial cause of foodborne disease in the United States. Whole-genome sequences and paired antimicrobial susceptibility data are available for strains because of surveillance efforts from public health agencie...
Machine Learning to Predict Delays in Adjuvant Radiation following Surgery for Head and Neck Cancer.
Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
Jan 29, 2019
OBJECTIVE: To apply a novel methodology with machine learning (ML) to a large national cancer registry to help identify patients who are high risk for delayed adjuvant radiation.
Learning from Longitudinal Data in Electronic Health Record and Genetic Data to Improve Cardiovascular Event Prediction.
Scientific reports
Jan 24, 2019
Current approaches to predicting a cardiovascular disease (CVD) event rely on conventional risk factors and cross-sectional data. In this study, we applied machine learning and deep learning models to 10-year CVD event prediction by using longitudina...
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