AIMC Topic: United States
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Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records.
Translational psychiatry
Feb 20, 2020
Suicide is the tenth leading cause of death in the United States (US). An early-warning system (EWS) for suicide attempt could prove valuable for identifying those at risk of suicide attempts, and analyzing the contribution of repeated attempts to th...
Development of a system based on artificial intelligence to identify visual problems in children: study protocol of the TrackAI project.
BMJ open
Feb 17, 2020
INTRODUCTION: Around 70% to 80% of the 19 million visually disabled children in the world are due to a preventable or curable disease, if detected early enough. Vision screening in childhood is an evidence-based and cost-effective way to detect visua...
Policy Implications of Artificial Intelligence and Machine Learning in Diabetes Management.
Current diabetes reports
Feb 1, 2020
PURPOSE OF REVIEW: Machine learning (ML) is increasingly being studied for the screening, diagnosis, and management of diabetes and its complications. Although various models of ML have been developed, most have not led to practical solutions for rea...
Assessment of utilization efficiency using machine learning techniques: A study of heterogeneity in preoperative healthcare utilization among super-utilizers.
American journal of surgery
Jan 27, 2020
INTRODUCTION: In the United States, 5% of patients represent up to 55% of all health care costs. This study sought to define healthcare utilization patterns among super-utilizers, as well as assess possible variation in patient outcomes.
The Digital Health Revolution and People with Disabilities: Perspective from the United States.
International journal of environmental research and public health
Jan 7, 2020
This article serves as the introduction to this special issue on Mobile Health and Mobile Rehabilitation for People with Disabilities. Social, technological and policy trends are reviewed. Needs, opportunities and challenges for the emerging fields o...
Teaching cross-cultural design thinking for healthcare.
Breast (Edinburgh, Scotland)
Jan 6, 2020
OBJECTIVES: Artificial intelligence (AI) is poised to transform breast cancer care. However, most scientists, engineers, and clinicians are not prepared to contribute to the AI revolution in healthcare. In this paper, we describe our experiences teac...
Comparison of Machine Learning Methods With Traditional Models for Use of Administrative Claims With Electronic Medical Records to Predict Heart Failure Outcomes.
JAMA network open
Jan 3, 2020
IMPORTANCE: Accurate risk stratification of patients with heart failure (HF) is critical to deploy targeted interventions aimed at improving patients' quality of life and outcomes.
International evaluation of an AI system for breast cancer screening.
Nature
Jan 1, 2020
Screening mammography aims to identify breast cancer at earlier stages of the disease, when treatment can be more successful. Despite the existence of screening programmes worldwide, the interpretation of mammograms is affected by high rates of false...
Use of natural language processing to improve predictive models for imaging utilization in children presenting to the emergency department.
BMC medical informatics and decision making
Dec 30, 2019
OBJECTIVE: To examine the association between the medical imaging utilization and information related to patients' socioeconomic, demographic and clinical factors during the patients' ED visits; and to develop predictive models using these associated...
Machine learning-based identification and rule-based normalization of adverse drug reactions in drug labels.
BMC bioinformatics
Dec 23, 2019
BACKGROUND: Use of medication can cause adverse drug reactions (ADRs), unwanted or unexpected events, which are a major safety concern. Drug labels, or prescribing information or package inserts, describe ADRs. Therefore, systematically identifying A...
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