Cargando…

Identifying High-Need Primary Care Patients Using Nursing Knowledge and Machine Learning Methods

Background Patient cohorts generated by machine learning can be enhanced with clinical knowledge to increase translational value and provide a practical approach to patient segmentation based on a mix of medical, behavioral, and social factors. Objectives This study aimed to generate a pragmatic e...

Descripción completa

Detalles Bibliográficos
Autores principales:	Hewner, Sharon, Smith, Erica, Sullivan, Suzanne S.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Georg Thieme Verlag KG 2023
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10208721/ https://www.ncbi.nlm.nih.gov/pubmed/36882152 http://dx.doi.org/10.1055/a-2048-7343

Ejemplares similares

Integrating social context into comprehensive shared care plans: A scoping review
por: Sullivan, Suzanne S., et al.
Publicado: (2017)

Integrating Social Determinants of Health into Primary Care Clinical and Informational Workflow during Care Transitions
por: Hewner, Sharon, et al.
Publicado: (2017)

Transitional Care Models for High-Need, High-Cost Adults in the United States: A Scoping Review and Gap Analysis
por: Hewner, Sharon, et al.
Publicado: (2021)

An integrative review of primary health care nurses’ mental health knowledge gaps and learning needs
por: McInnes, Susan, et al.
Publicado: (2022)

Expanding the evidence for cross-sector collaboration in implementation science: creating a collaborative, cross-sector, interagency, multidisciplinary team to serve patients experiencing homelessness and medical complexity at hospital discharge
por: Anderson, Amanda Joy, et al.
Publicado: (2023)

Identifying the Risk Factors Associated with Nursing Home Residents’ Pressure Ulcers Using Machine Learning Methods
por: Lee, Soo-Kyoung, et al.
Publicado: (2021)

Application of Machine Learning Methods in Nursing Home Research
por: Lee, Soo-Kyoung, et al.
Publicado: (2020)

Assessment of staffing needs for registered nurses and licensed practical nurses at primary care units in Brazil using Workload Indicators of Staffing Need (WISN) method
por: Bonfim, Daiana, et al.
Publicado: (2022)

Identifying Educational Needs and Practice Gaps of European Hematologists and Hematology Nurses in the Treatment and Management of Multiple Myeloma
por: Murray, Suzanne, et al.
Publicado: (2018)

The effect of micro-learning on trauma care knowledge and learning satisfaction in nursing students
por: Haghighat, Hossein, et al.
Publicado: (2023)

Machine Learning and Knowledge Extraction
por: Holzinger, Andreas, et al.
Publicado: (2018)

An exploration of learning needs: identifying knowledge deficits among hospitalized adults with heart failure
por: Raines, Erika, et al.
Publicado: (2019)

Nursing assistants’ knowledge, attitudes and training needs regarding urinary incontinence in nursing homes: a mixed-methods study
por: Liao, Lulu, et al.
Publicado: (2023)

Machine learning approaches for predicting high cost high need patient expenditures in health care
por: Yang, Chengliang, et al.
Publicado: (2018)

Evaluation of the Need for Intensive Care in Children With Pneumonia: Machine Learning Approach
por: Liu, Yun-Chung, et al.
Publicado: (2022)

Knowledge and perceptions of Indian primary care nurses towards mental illness
por: Gandhi, Sailaxmi, et al.
Publicado: (2019)

Correction to: Nursing assistants’ knowledge, attitudes and training needs regarding urinary incontinence in nursing homes: a mixed-methods study
por: Liao, Lulu, et al.
Publicado: (2023)

Simplified Machine Learning Models Can Accurately Identify High-Need High-Cost Patients With Inflammatory Bowel Disease
por: Nguyen, Nghia H., et al.
Publicado: (2022)

Machine Learning Methods to Identify Missed Cases of Bladder Cancer in Population-Based Registries
por: Noone, Anne-Michelle, et al.
Publicado: (2021)

High-Efficiency Machine Learning Method for Identifying Foodborne Disease Outbreaks and Confounding Factors
por: Zhang, Peng, et al.
Publicado: (2021)

The MidPIC study: Midwives’ knowledge, perspectives and learning needs regarding preconception and interconception care
por: Bradfield, Zoe, et al.
Publicado: (2023)

Visual knowledge discovery and machine learning
por: Kovalerchuk, Boris
Publicado: (2018)

Integrating Machine Learning with Human Knowledge
por: Deng, Changyu, et al.
Publicado: (2020)

Diabetes knowledge and training needs among non-endocrinology nurses
por: Wang, Yudong, et al.
Publicado: (2023)

Identifying patients at increased risk of hypoglycaemia in primary care: Development of a machine learning‐based screening tool
por: Crutzen, Stijn, et al.
Publicado: (2021)

Oral Assessment and Nursing Interventions Among Nigerian Nurses-Knowledge, Practices and Educational Needs
por: Azodo, CC, et al.
Publicado: (2013)

Identifying essential genes in bacterial metabolic networks with machine learning methods
por: Plaimas, Kitiporn, et al.
Publicado: (2010)

Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods
por: Chen, Lei, et al.
Publicado: (2021)

Identifying Methylation Signatures and Rules for COVID-19 With Machine Learning Methods
por: Li, Zhandong, et al.
Publicado: (2022)

Identifying the predictors of severe psychological distress by auto-machine learning methods
por: Zhang, Xiaomei, et al.
Publicado: (2023)

Accurately identifying hemagglutinin using sequence information and machine learning methods
por: Zou, Xidan, et al.
Publicado: (2023)

Education needs of nurses in thrombosis and hemostasis: An international, mixed‐methods study
por: Lazure, Patrice, et al.
Publicado: (2018)

KNOWLEDGE, ATTITUDE AND PRACTICE OF PRIMARY HEALTH CARE DOCTORS AND NURSES IN HYPERTENSION OF PREGNANCY
por: Gandeh, Mohammad B.S., et al.
Publicado: (1999)

Nurses’ knowledge and attitudes towards palliative care and death: a learning intervention
por: Hao, Yanping, et al.
Publicado: (2021)

Knowledge about Palliative Care and Attitudes toward Care of the Dying among Primary Care Nurses in Spain
por: Mengual, Tamara Escoto, et al.
Publicado: (2023)

Profiling Patients by Intensity of Nursing Care: An Operative Approach Using Machine Learning
por: Ocagli, Honoria, et al.
Publicado: (2020)

Towards the Provision of Palliative Care Services in the Intensive Coronary Care Units: Nurses’ Knowledge, Training Needs, and Related-Barriers
por: Abu-Aziz, Baraa, et al.
Publicado: (2023)

Integrating simulation training during clinical practice in nursing homes: an experimental study of nursing students’ knowledge acquisition, self-efficacy and learning needs
por: Olaussen, Camilla, et al.
Publicado: (2022)

Machine learning and health need better values
por: Ghassemi, Marzyeh, et al.
Publicado: (2022)

Machine Learning Techniques to Identify Antimicrobial Resistance in the Intensive Care Unit
por: Martínez-Agüero, Sergio, et al.
Publicado: (2019)

Cannot write session to /tmp/vufind_sessions/sess_v0kpm5eels114vbso8cqb1339o