AIMC Topic: Patient Readmission

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Machine learning-based survival models for predicting rehospitalization of older hip fracture patients: a retrospective cohort study.

BMC musculoskeletal disorders
PURPOSE: To evaluate machine learning-based survival model roles in predicting rehospitalization after hip fractures to improve reduce the burden on the healthcare system.

Clinical and Social Characterization of Patients Hospitalized for COPD Exacerbation Using Machine Learning Tools.

Archivos de bronconeumologia
OBJECTIVE: This study aims to employ machine learning (ML) tools to cluster patients hospitalized for acute exacerbations of chronic obstructive pulmonary disease (COPD) based on their diverse social and clinical characteristics. This clustering is i...

Reducing readmissions in the safety net through AI and automation.

The American journal of managed care
OBJECTIVES: To implement a technology-based, systemwide readmission reduction initiative in a safety-net health system and evaluate clinical, care equity, and financial outcomes.

Machine learning for adverse event prediction in outpatient parenteral antimicrobial therapy: a scoping review.

The Journal of antimicrobial chemotherapy
OBJECTIVE: This study aimed to conduct a scoping review of machine learning (ML) techniques in outpatient parenteral antimicrobial therapy (OPAT) for predicting adverse outcomes and to evaluate their validation, implementation and potential barriers ...

Impact of wearable device data and multi-scale entropy analysis on improving hospital readmission prediction.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Unplanned readmissions following a hospitalization remain common despite significant efforts to curtail these. Wearable devices may offer help identify patients at high risk for an unplanned readmission.

Evaluating the Predictive Features of Person-Centric Knowledge Graph Embeddings: Unfolding Ablation Studies.

Studies in health technology and informatics
Developing novel predictive models with complex biomedical information is challenging due to various idiosyncrasies related to heterogeneity, standardization or sparseness of the data. We previously introduced a person-centric ontology to organize in...

Minimally invasive surgery for clinical T4 non-small-cell lung cancer: national trends and outcomes.

European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery
OBJECTIVES: Recent randomized data support the perioperative benefits of minimally invasive surgery (MIS) for non-small-cell lung cancer (NSCLC). Its utility for cT4 tumours remains understudied. We, therefore, sought to analyse national trends and o...

Interpretable Machine Learning Models Using Peripheral Immune Cells to Predict 90-Day Readmission or Mortality in Acute Heart Failure Patients.

Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
BACKGROUND: Acute heart failure (AHF) carries a grave prognosis, marked by high readmission and mortality rates within 90 days post-discharge. This underscores the urgent need for enhanced care transitions, early monitoring, and precise interventions...

Improving Cardiology-Rehospitalization Prediction Through the Synergy of Process Mining and Deep Learning: An Innovative Approach.

Studies in health technology and informatics
Nowadays, hospitals are facing the need for an accurate prediction of rehospitalizations. Rehospitalizations, indeed, represent both a high financial burden for the hospital and a proxy measure of care quality. The current work aims to address such a...