AIMC Topic: Primary Health Care

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Artificial Intelligence Techniques That May Be Applied to Primary Care Data to Facilitate Earlier Diagnosis of Cancer: Systematic Review.

Journal of medical Internet research
BACKGROUND: More than 17 million people worldwide, including 360,000 people in the United Kingdom, were diagnosed with cancer in 2018. Cancer prognosis and disease burden are highly dependent on the disease stage at diagnosis. Most people diagnosed w...

Extracting Angina Symptoms from Clinical Notes Using Pre-Trained Transformer Architectures.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Anginal symptoms can connote increased cardiac risk and a need for change in cardiovascular management. In this study, a pre-trained transformer architecture was used to automatically detect and characterize anginal symptoms from within the history o...

Perceptions of virtual primary care physicians: A focus group study of medical and data science graduate students.

PloS one
BACKGROUND: Artificial and virtual technologies in healthcare have advanced rapidly, and healthcare systems have been adapting care accordingly. An intriguing new development is the virtual physician, which can diagnose and treat patients independent...

Machine learning in primary care: potential to improve public health.

Journal of medical engineering & technology
It is estimated that missed opportunities for diagnosis occur in 1 in 20 primary care appointments. This is not only detrimental to individual patients, but also to the healthcare system as health outcomes are affected and healthcare expenditure inev...

COVID-19 Surveillance in a Primary Care Sentinel Network: In-Pandemic Development of an Application Ontology.

JMIR public health and surveillance
BACKGROUND: Creating an ontology for COVID-19 surveillance should help ensure transparency and consistency. Ontologies formalize conceptualizations at either the domain or application level. Application ontologies cross domains and are specified thro...

Cohort profile: CROSS-TRACKS: a population-based open cohort across healthcare sectors in Denmark.

BMJ open
PURPOSE: This paper describes the open cohort CROSS-TRACKS, which comprises population-based data from primary care, secondary care and national registries to study patient pathways and transitions across sectors while adjusting for sociodemographic ...

US primary care in 2029: A Delphi survey on the impact of machine learning.

PloS one
OBJECTIVE: To solicit leading health informaticians' predictions about the impact of AI/ML on primary care in the US in 2029.

Can machine learning be useful as a screening tool for depression in primary care?

Journal of psychiatric research
Depression is a widespread disease with a high economic burden and a complex pathophysiology disease that is still not wholly clarified, not to mention it usually is associated as a risk factor for absenteeism at work and suicide. Just 50% of patient...

Patient-Centered Appointment Scheduling: a Call for Autonomy, Continuity, and Creativity.

Journal of general internal medicine
When making an appointment, patients are generally unaware of how much clinician time is available to address their concerns. Similarly, the primary care clinician is often unaware of what the patient expects to accomplish during the visit, leading t...

Predicting the risk of asthma attacks in children, adolescents and adults: protocol for a machine learning algorithm derived from a primary care-based retrospective cohort.

BMJ open
INTRODUCTION: Most asthma attacks and subsequent deaths are potentially preventable. We aim to develop a prognostic tool for identifying patients at high risk of asthma attacks in primary care by leveraging advances in machine learning.