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Patient Outcome Assessment

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Using Domain Adaptation and Inductive Transfer Learning to Improve Patient Outcome Prediction in the Intensive Care Unit: Retrospective Observational Study.

Journal of medical Internet research
BACKGROUND: Accurate patient outcome prediction in the intensive care unit (ICU) can potentially lead to more effective and efficient patient care. Deep learning models are capable of learning from data to accurately predict patient outcomes, but the...

Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review.

Artificial intelligence in medicine
OBJECTIVE: Natural language processing (NLP) combined with machine learning (ML) techniques are increasingly used to process unstructured/free-text patient-reported outcome (PRO) data available in electronic health records (EHRs). This systematic rev...

State-of-the-art Applications of Patient-Reported Outcome Measures in Spinal Care.

The Journal of the American Academy of Orthopaedic Surgeons
Patient-reported outcome measures (PROMs) assign objective measures to patient's subjective experiences of health, pain, disability, function, and quality of life. PROMs can be useful for providers in shared decision making, outcome assessment, and i...

Analyzing patient experiences using natural language processing: development and validation of the artificial intelligence patient reported experience measure (AI-PREM).

BMC medical informatics and decision making
BACKGROUND: Evaluating patients' experiences is essential when incorporating the patients' perspective in improving healthcare. Experiences are mainly collected using closed-ended questions, although the value of open-ended questions is widely recogn...

Using natural language processing to understand, facilitate and maintain continuity in patient experience across transitions of care.

International journal of medical informatics
BACKGROUND: Patient centred care necessitates that healthcare experiences and perceived outcomes be considered across all transitions of care. Information encoded within free-text patient experience comments relating to transitions of care are not ca...

Nothing about us without us: involving patient collaborators for machine learning applications in rheumatology.

Annals of the rheumatic diseases
Novel machine learning methods open the door to advances in rheumatology through application to complex, high-dimensional data, otherwise difficult to analyse. Results from such efforts could provide better classification of disease, decision support...

Interpretation of machine learning predictions for patient outcomes in electronic health records.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Electronic health records are an increasingly important resource for understanding the interactions between patient health, environment, and clinical decisions. In this paper we report an empirical study of predictive modeling of seven patient outcom...

Automated Extraction of Patient-Centered Outcomes After Breast Cancer Treatment: An Open-Source Large Language Model-Based Toolkit.

JCO clinical cancer informatics
PURPOSE: Patient-centered outcomes (PCOs) are pivotal in cancer treatment, as they directly reflect patients' quality of life. Although multiple studies suggest that factors affecting breast cancer-related morbidity and survival are influenced by tre...

Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review.

BMJ health & care informatics
OBJECTIVES: Unstructured free-text patient feedback contains rich information, and analysing these data manually would require a lot of personnel resources which are not available in most healthcare organisations.To undertake a systematic review of t...