AIMC Topic: Patient Discharge

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Predicting hospital readmission in patients with mental or substance use disorders: A machine learning approach.

International journal of medical informatics
OBJECTIVE: Mental or substance use disorders (M/SUD) are major contributors of disease burden with high risk for hospital readmissions. We sought to develop and evaluate a readmission model using a machine learning (ML) approach.

Outpatient Robot-Assisted Radical Prostatectomy: Are Patients Ready for Same-Day Discharge?

Journal of endourology
Several case series have demonstrated the safety and feasibility of outpatient robot-assisted radical prostatectomy (RARP) in well-selected patients; however, the patient perspective of this practice has not been well explored. In this study, we exp...

Same-day discharge surgery for robot-assisted radical prostatectomy in the era of ERAS and prehabilitation pathways: a contemporary, comparative, feasibility study.

World journal of urology
PURPOSE: To assess the feasibility of same-day discharge (SDD) after robot-assisted radical prostatectomy (RARP) in the context of enhanced recovery after surgery (ERAS) and prehabilitation pathways.

Development and Validation of a Machine Learning Model to Aid Discharge Processes for Inpatient Surgical Care.

JAMA network open
IMPORTANCE: Inpatient overcrowding is associated with delays in care, including the deferral of surgical care until beds are available to accommodate postoperative patients. Timely patient discharge is critical to address inpatient overcrowding and r...

Mapping anatomical related entities to human body parts based on wikipedia in discharge summaries.

BMC bioinformatics
*: Background Consisting of dictated free-text documents such as discharge summaries, medical narratives are widely used in medical natural language processing. Relationships between anatomical entities and human body parts are crucial for building m...

A Lightweight API-Based Approach for Building Flexible Clinical NLP Systems.

Journal of healthcare engineering
Natural language processing (NLP) has become essential for secondary use of clinical data. Over the last two decades, many clinical NLP systems were developed in both academia and industry. However, nearly all existing systems are restricted to speci...

Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: a retrospective, population-based registry study.

BMJ open
OBJECTIVES: The aim of this work was to train machine learning models to identify patients at end of life with clinically meaningful diagnostic accuracy, using 30-day mortality in patients discharged from the emergency department (ED) as a proxy.

Readmission prediction using deep learning on electronic health records.

Journal of biomedical informatics
Unscheduled 30-day readmissions are a hallmark of Congestive Heart Failure (CHF) patients that pose significant health risks and escalate care cost. In order to reduce readmissions and curb the cost of care, it is important to initiate targeted inter...

Detecting adverse drug reactions in discharge summaries of electronic medical records using Readpeer.

International journal of medical informatics
BACKGROUND: Hospital discharge summaries offer a potentially rich resource to enhance pharmacovigilance efforts to evaluate drug safety in real-world clinical practice. However, it is infeasible for experts to read through all discharge summaries to ...

Analysis of Unstructured Text-Based Data Using Machine Learning Techniques: The Case of Pediatric Emergency Department Records in Nicaragua.

Medical care research and review : MCRR
Free-text information is still widely used in emergency department (ED) records. Machine learning techniques are useful for analyzing narratives, but they have been used mostly for English-language data sets. Considering such a framework, the perform...