AIMC Topic: Outpatients

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Risk Assessment for Venous Thromboembolism in Chemotherapy-Treated Ambulatory Cancer Patients.

Medical decision making : an international journal of the Society for Medical Decision Making
OBJECTIVE: To design a precision medicine approach aimed at exploiting significant patterns in data, in order to produce venous thromboembolism (VTE) risk predictors for cancer outpatients that might be of advantage over the currently recommended mod...

Characteristics of outpatient clinical summaries in the United States.

International journal of medical informatics
In the United States, federal regulations require that outpatient practices provide a clinical summary to ensure that patients understand what transpired during their appointment and what to do before the next visit. To determine whether clinical sum...

Predicting inpatient stay lasting 2 midnights or longer after robotic surgery for endometrial cancer.

Journal of minimally invasive gynecology
OBJECTIVE: To estimate the rate of inpatient stay and the factors predicting inpatient status after robotic surgery for endometrial cancer following the change in the Medicare definition of "inpatient" to include hospitalization spanning 2 midnights.

The use of natural language processing of infusion notes to identify outpatient infusions.

Pharmacoepidemiology and drug safety
PURPOSE: Outpatient infusions are commonly missing in Veterans Health Affairs (VHA) pharmacy dispensing data sets. Currently, Healthcare Common Procedure Coding System (HCPCS) codes are used to identify outpatient infusions, but concerns exist if the...

Generating Outpatient Progress Notes: A Comparison of Individualized and Generalized Models.

Studies in health technology and informatics
The increasing documentation workload in medical practice, particularly for clinical notes, has driven the development of AI-driven solutions. This study introduces an AI Doctor Assistant (DA) that generates drafts of outpatient progress notes. The D...

Predicting hospital outpatient volume using XGBoost: a machine learning approach.

Scientific reports
Hospital outpatient volume is influenced by a variety of factors, including environmental conditions and healthcare resource availability. Accurate prediction of outpatient demand can significantly enhance operational efficiency and optimize the allo...

Optimizing the dynamic treatment regime of outpatient rehabilitation in patients with knee osteoarthritis using reinforcement learning.

Journal of neuroengineering and rehabilitation
BACKGROUND: Knee osteoarthritis (KOA) is a prevalent chronic disease worldwide, and traditional treatment methods lack personalized adjustment for individual patient differences and cannot meet the needs of personalized treatment.

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 ...

Using Natural Language Processing to Extract and Classify Symptoms Among Patients with Thyroid Dysfunction.

Studies in health technology and informatics
In the United States, more than 12% of the population will experience thyroid dysfunction. Patient symptoms often reported with thyroid dysfunction include fatigue and weight change. However, little is understood about the relationship between these ...