AIMC Topic: Patient Participation
Clear Filters Showing 21 to 30 of 48 articles
From Patient Engagement to Precision Oncology: Leveraging Informatics to Advance Cancer Care.
Yearbook of medical informatics
Aug 21, 2020
OBJECTIVES: Conduct a survey of the literature for advancements in cancer informatics over the last three years in three specific areas where there has been unprecedented growth: 1) digital health; 2) machine learning; and 3) precision oncology. We a...
Digital health technologies: opportunities and challenges in rheumatology.
Nature reviews. Rheumatology
Jul 24, 2020
The past decade in rheumatology has seen tremendous innovation in digital health technologies, including the electronic health record, virtual visits, mobile health, wearable technology, digital therapeutics, artificial intelligence and machine learn...
A Machine Learning Approach to Understanding Patterns of Engagement With Internet-Delivered Mental Health Interventions.
JAMA network open
Jul 1, 2020
IMPORTANCE: The mechanisms by which engagement with internet-delivered psychological interventions are associated with depression and anxiety symptoms are unclear.
Intelligent, Autonomous Machines in Surgery.
The Journal of surgical research
Apr 24, 2020
Surgeons perform two primary tasks: operating and engaging patients and caregivers in shared decision-making. Human dexterity and decision-making are biologically limited. Intelligent, autonomous machines have the potential to augment or replace surg...
'AI gone mental': engagement and ethics in data-driven technology for mental health.
Journal of mental health (Abingdon, England)
Jan 30, 2020
Patient-based prediction algorithm of relapse after allo-HSCT for acute Leukemia and its usefulness in the decision-making process using a machine learning approach.
Cancer medicine
Jul 15, 2019
Although allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a curative therapy for high-risk acute leukemia (AL), some patients still relapse. Since patients simultaneously have many prognostic factors, difficulties are associated with...
Humans
Machine Learning
Algorithms
Prognosis
Clinical Decision-Making
Treatment Outcome
Decision Trees
Survival Analysis
Disease Management
Hematopoietic Stem Cell Transplantation
Leukemia, Myeloid, Acute
Female
Transplantation, Homologous
Young Adult
Male
Patient Participation
Adult
Middle Aged
Models, Theoretical
The Application of Deep Learning in the Risk Grading of Skin Tumors for Patients Using Clinical Images.
Journal of medical systems
Jul 13, 2019
According to diagnostic criteria, skin tumors can be divided into three categories: benign, low degree and high degree malignancy. For high degree malignant skin tumors, if not detected in time, they can do serious harm to patients' health. However, ...
A two-site survey of medical center personnel's willingness to share clinical data for research: implications for reproducible health NLP research.
BMC medical informatics and decision making
Apr 4, 2019
BACKGROUND: A shareable repository of clinical notes is critical for advancing natural language processing (NLP) research, and therefore a goal of many NLP researchers is to create a shareable repository of clinical notes, that has breadth (from mult...
Parsing clinical text using the state-of-the-art deep learning based parsers: a systematic comparison.
BMC medical informatics and decision making
Apr 4, 2019
BACKGROUND: A shareable repository of clinical notes is critical for advancing natural language processing (NLP) research, and therefore a goal of many NLP researchers is to create a shareable repository of clinical notes, that has breadth (from mult...
A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data.
International journal of medical informatics
Feb 20, 2019
OBJECTIVE: In this systematic review, we aim to synthesize the literature on the use of natural language processing (NLP) and text mining as they apply to symptom extraction and processing in electronic patient-authored text (ePAT).
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