Gender Aspects during and after the Diagnostic Odyssey in M. Fabry: Machine Learning Diagnostic Support Tool Reveals Different Answer Patterns in Diagnostic Questionnaire

Introduction: Patients boarding in the Emergency Department can contribute to overcrowding, leading to longer waiting times and patients leaving without being seen or completing their treatment. The early identification of potential admissions could act as an additional decision support tool to alert clinicians that a patient needs to be reviewed for admission and would also be of benefit to bed managers in advance bed planning for the patient. We aim to create a low-dimensional model predicting admissions early from the paediatric Emergency Department.Methods and Analysis: The methodology Cross Industry Standard Process for Data Mining (CRISP-DM) will be followed. The dataset will comprise of 2 years of data, ~76,000 records. Potential predictors were identified from previous research, comprising of demographics, registration details, triage assessment, hospital usage and past medical history. Fifteen models will be developed comprised of 3 machine learning algorithms (Logistic regression, naïve Bayes and gradient boosting machine) and 5 sampling methods, 4 of which are aimed at addressing class imbalance (undersampling, oversampling, and synthetic oversampling techniques). The variables of importance will then be identified from the optimal model (selected based on the highest Area under the curve) and used to develop an additional low-dimensional model for deployment.Discussion: A low-dimensional model comprised of routinely collected data, captured up to post triage assessment would benefit many hospitals without data rich platforms for the development of models with a high number of predictors. Novel to the planned study is the use of data from the Republic of Ireland and the application of sampling techniques aimed at improving model performance impacted by an imbalance between admissions and discharges in the outcome variable.

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Using Machine Learning to Predict Antimicrobial Resistance of Acinetobacter Baumannii, Klebsiella Pneumoniae and Pseudomonas Aeruginosa Strains

Studies in Health Technology and Informatics - Public Health and Informatics ◽

10.3233/shti210117 ◽

2021 ◽

Author(s):

Georgios Feretzakis ◽

Aikaterini Sakagianni ◽

Evangelos Loupelis ◽

Dimitris Kalles ◽

Maria Martsoukou ◽

...

Keyword(s):

Machine Learning ◽

Pseudomonas Aeruginosa ◽

Antibiotic Resistance ◽

Antimicrobial Resistance ◽

Klebsiella Pneumoniae ◽

Acinetobacter Baumannii ◽

Clinical Microbiology Laboratory ◽

Support Tool ◽

Basic Characteristics ◽

Using Data

Hospital-acquired infections, particularly in ICU, are becoming more frequent in recent years, with the most serious of them being Gram-negative bacterial infections. Among them, Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa are considered the most resistant bacteria encountered in ICU and other wards. Given the fact that about 24 hours are usually required to perform common antibiotic resistance tests after the bacteria identification, the use of machine learning techniques could be an additional decision support tool in selecting empirical antibiotic treatment based on the sample type, bacteria, and patient’s basic characteristics. In this article, five machine learning (ML) models were evaluated to predict antimicrobial resistance of Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa. We suggest implementing ML techniques to forecast antibiotic resistance using data from the clinical microbiology laboratory, available in the Laboratory Information System (LIS).

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Diagnostic support tool for lumbar spinal stenosis

The Journal of Japanese Society of Lumbar Spine Disorders ◽

10.3753/yotsu.15.32 ◽

2009 ◽

Vol 15 (1) ◽

pp. 32-38

Author(s):

Shinichi KONNO

Keyword(s):

Spinal Stenosis ◽

Lumbar Spinal Stenosis ◽

Support Tool ◽

Lumbar Spinal ◽

Diagnostic Support

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Clinical utility of a cluster of tests as a diagnostic support tool for clinical lumbar instability

Musculoskeletal Science and Practice ◽

10.1016/j.msksp.2020.102224 ◽

2020 ◽

Vol 50 ◽

pp. 102224

Author(s):

Pattanasin Areeudomwong ◽

Kitti Jirarattanaphochai ◽

Thapakorn Ruanjai ◽

Vitsarut Buttagat

Keyword(s):

Clinical Utility ◽

Lumbar Instability ◽

Support Tool ◽

Diagnostic Support

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Machine Learning Methodologies for Prediction of Rhythm-Control Strategy in Patients Diagnosed with Atrial Fibrillation: Model Development and Comparison Study (Preprint)

10.2196/preprints.29225 ◽

2021 ◽

Author(s):

Rachel Soon-Yong Kim ◽

Steve Simon ◽

Brett Powers ◽

Amneet Sandhu ◽

Jose Sanchez ◽

...

Keyword(s):

Machine Learning ◽

Atrial Fibrillation ◽

Control Strategy ◽

Prediction Models ◽

Rhythm Control ◽

Machine Learning Algorithms ◽

Support Tool ◽

Wide Range ◽

Complex Models ◽

The Impact

BACKGROUND Identification of the appropriate rhythm management strategy for patients diagnosed with atrial fibrillation (AF) remains a major challenge for providers. While clinical trials have identified sub-groups of patients in whom a rate- or rhythm-control strategy might be indicated to improve outcomes, the wide range of presentations and risk factors among patients presenting with AF makes such approaches challenging. A strength of electronic health records (EHR) is the ability to build in logic to guide management decisions, such that the system can automatically identify patients in whom a rhythm-control strategy is more likely and promote efficient referrals to specialists. However, like any clinical decision-support tool, there is a balance between interpretability and accurate prediction. OBJECTIVE In this investigation, we sought to create an EHR-based prediction tool to guide patient referral to specialists for rhythm-control management by comparing different machine learning algorithms. METHODS We compared machine learning models of increasing complexity and using up to 50,845 variables to predict the rhythm-control strategy in 42,022 patients within the UC Health system at the time of AF diagnosis. Models were evaluated on their classification accuracy, defined by the F1 score and other metrics, and interpretability, captured by inspection of the relative importance of each predictor. RESULTS We found that age was by far the strongest single predictor of a rhythm-control strategy, but that greater accuracy could be achieved with more complex models incorporating neural networks and more predictors for each subject. We determined that the impact of better prediction models was notable primarily in the rate of inappropriate referrals for rhythm-control, in which more complex models provided an average of 20% fewer inappropriate referrals than simpler, more interpretable models. CONCLUSIONS We conclude that any healthcare system seeking to incorporate algorithms to guide rhythm management for patients with AF will need to address this trade-off between prediction accuracy and model interpretability.

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