AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Patient Outcome Assessment

Showing 11 to 20 of 23 articles

Clear Filters

An approach to predicting patient experience through machine learning and social network analysis.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Improving the patient experience has become an essential component of any healthcare system's performance metrics portfolio. In this study, we developed a machine learning model to predict a patient's response to the Hospital Consumer Asse...

Natural language processing of Reddit data to evaluate dermatology patient experiences and therapeutics.

Journal of the American Academy of Dermatology
BACKGROUND: There is a lack of research studying patient-generated data on Reddit, one of the world's most popular forums with active users interested in dermatology. Techniques within natural language processing, a field of artificial intelligence, ...

How to automatically turn patient experience free-text responses into actionable insights: a natural language programming (NLP) approach.

BMC medical informatics and decision making
BACKGROUND: Patient experience surveys often include free-text responses. Analysis of these responses is time-consuming and often underutilized. This study examined whether Natural Language Processing (NLP) techniques could provide a data-driven, hos...

Machine learning for clinical decision support in infectious diseases: a narrative review of current applications.

Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases
BACKGROUND: Machine learning (ML) is a growing field in medicine. This narrative review describes the current body of literature on ML for clinical decision support in infectious diseases (ID).

Ensuring Fairness in Machine Learning to Advance Health Equity.

Annals of internal medicine
Machine learning is used increasingly in clinical care to improve diagnosis, treatment selection, and health system efficiency. Because machine-learning models learn from historically collected data, populations that have experienced human and struct...

Mechanistic models versus machine learning, a fight worth fighting for the biological community?

Biology letters
Ninety per cent of the world's data have been generated in the last 5 years ( Report no. DES4702. Issued April 2017. Royal Society). A small fraction of these data is collected with the aim of validating specific hypotheses. These studies are led by ...

Automation of motor dexterity assessment.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]
Motor dexterity assessment is regularly performed in rehabilitation wards to establish patient status and automatization for such routinary task is sought. A system for automatizing the assessment of motor dexterity based on the Fugl-Meyer scale and ...

Using Artificial Intelligence to Reduce the Risk of Nonadherence in Patients on Anticoagulation Therapy.

Stroke
BACKGROUND AND PURPOSE: This study evaluated the use of an artificial intelligence platform on mobile devices in measuring and increasing medication adherence in stroke patients on anticoagulation therapy. The introduction of direct oral anticoagulan...

Targeted use of growth mixture modeling: a learning perspective.

Statistics in medicine
From the statistical learning perspective, this paper shows a new direction for the use of growth mixture modeling (GMM), a method of identifying latent subpopulations that manifest heterogeneous outcome trajectories. In the proposed approach, we uti...