AIMC Topic: Inpatients

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Publicly available machine learning models for identifying opioid misuse from the clinical notes of hospitalized patients.

BMC medical informatics and decision making
BACKGROUND: Automated de-identification methods for removing protected health information (PHI) from the source notes of the electronic health record (EHR) rely on building systems to recognize mentions of PHI in text, but they remain inadequate at e...

Predicting Inpatient Medication Orders From Electronic Health Record Data.

Clinical pharmacology and therapeutics
In a general inpatient population, we predicted patient-specific medication orders based on structured information in the electronic health record (EHR). Data on over three million medication orders from an academic medical center were used to train ...

An Artificial Neural Network-based Predictive Model to Support Optimization of Inpatient Glycemic Control.

Diabetes technology & therapeutics
Achieving glycemic control in critical care patients is of paramount importance, and has been linked to reductions in mortality, intensive care unit (ICU) length of stay, and morbidities such as infection. The myriad of illnesses and patient conditi...

A continual prediction model for inpatient acute kidney injury.

Computers in biology and medicine
Acute kidney injury (AKI) commonly occurs in hospitalized patients and can lead to serious medical complications. But it is preventable and potentially reversible with early diagnosis and management. Therefore, several machine learning based predicti...

[Hemoglobinuria in children hospitalized in Ouagadougou: short term inpatient care and prognosis].

The Pan African medical journal
INTRODUCTION: The purpose of this study was to analyze the epidemiological, diagnostic, therapeutic and evolutionary features of hemoglobinuria in children hospitalized in the Pediatric University Hospital Charles de Gaulle, Ouagadougou.

Estimating treatment effects with machine learning.

Health services research
OBJECTIVE: To demonstrate the performance of methodologies that include machine learning (ML) algorithms to estimate average treatment effects under the assumption of exogeneity (selection on observables).

Validation of an alcohol misuse classifier in hospitalized patients.

Alcohol (Fayetteville, N.Y.)
BACKGROUND: Current modes of identifying alcohol misuse in hospitalized patients rely on self-report questionnaires and diagnostic codes that have limitations, including low sensitivity. Information in the clinical notes of the electronic health reco...

Perioperative mortality and morbidity of outpatient versus inpatient robot-assisted radical prostatectomy: A propensity matched analysis.

Urologic oncology
OBJECTIVES: To compare the early (≤30 days) postoperative mortality and morbidity in patients who underwent robot-assisted radical prostatectomy (RARP) and were discharged the same surgery day to a propensity score matched patient population of RARP ...

Predicting mechanical restraint of psychiatric inpatients by applying machine learning on electronic health data.

Acta psychiatrica Scandinavica
OBJECTIVE: Mechanical restraint (MR) is used to prevent patients from harming themselves or others during inpatient treatment. The objective of this study was to investigate whether incident MR occurring in the first 3 days following admission could ...