AIMC Topic: Comorbidity
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LeMeDISCO is a computational method for large-scale prediction & molecular interpretation of disease comorbidity.
Communications biology
Aug 25, 2022
To understand the origin of disease comorbidity and to identify the essential proteins and pathways underlying comorbid diseases, we developed LeMeDISCO (Large-Scale Molecular Interpretation of Disease Comorbidity), an algorithm that predicts disease...
DAC Stacking: A Deep Learning Ensemble to Classify Anxiety, Depression, and Their Comorbidity From Reddit Texts.
IEEE journal of biomedical and health informatics
Jul 1, 2022
Depression is the most incapacitating disease worldwide, and it has an alarming comorbidity rate with anxiety. The use of social networks to expose personal difficulties has enabled works on the automatic identification of specific mental conditions,...
A Novel Deep Learning-Based Black Fungus Disease Identification Using Modified Hybrid Learning Methodology.
Contrast media & molecular imaging
Jan 27, 2022
Currently, countries across the world are suffering from a prominent viral infection called COVID-19. Most countries are still facing several issues due to this disease, which has resulted in several fatalities. The first COVID-19 wave caused devasta...
Anemia or other comorbidities? using machine learning to reveal deeper insights into the drivers of acute coronary syndromes in hospital admitted patients.
PloS one
Jan 24, 2022
Acute coronary syndromes (ACS) are a leading cause of deaths worldwide, yet the diagnosis and treatment of this group of diseases represent a significant challenge for clinicians. The epidemiology of ACS is extremely complex and the relationship betw...
Current forecast of COVID-19 in Mexico: A Bayesian and machine learning approaches.
PloS one
Jan 21, 2022
The COVID-19 pandemic has been widely spread and affected millions of people and caused hundreds of deaths worldwide, especially in patients with comorbilities and COVID-19. This manuscript aims to present models to predict, firstly, the number of co...
Comparing LASSO and random forest models for predicting neurological dysfunction among fluoroquinolone users.
Pharmacoepidemiology and drug safety
Dec 28, 2021
BACKGROUND: Fluoroquinolones are associated with central (CNS) and peripheral (PNS) nervous system symptoms, and predicting the risk of these outcomes may have important clinical implications. Both LASSO and random forest are appealing modeling metho...
Development of a prediction score for in-hospital mortality in COVID-19 patients with acute kidney injury: a machine learning approach.
Scientific reports
Dec 24, 2021
Acute kidney injury (AKI) is frequently associated with COVID-19 and it is considered an indicator of disease severity. This study aimed to develop a prognostic score for predicting in-hospital mortality in COVID-19 patients with AKI (AKI-COV score)....
Using Machine Learning to Establish Predictors of Mortality in Patients Undergoing Laparotomy for Emergency General Surgical Conditions.
World journal of surgery
Oct 26, 2021
INTRODUCTION: Patients undergoing laparotomy for emergency general surgery (EGS) conditions, constitute a high-risk group with poor outcomes. These patients have a high prevalence of comorbidities. This study aims to identify patient factors, physiol...
Predictive value of red blood cell distribution width in septic shock patients with thrombocytopenia: A retrospective study using machine learning.
Journal of clinical laboratory analysis
Oct 21, 2021
BACKGROUND: Sepsis-associated thrombocytopenia (SAT) is common in critical patients and results in the elevation of mortality. Red cell distribution width (RDW) can reflect body response to inflammation and oxidative stress. We try to investigate the...
Subcategorizing EHR diagnosis codes to improve clinical application of machine learning models.
International journal of medical informatics
Sep 21, 2021
BACKGROUND: Electronic health record (EHR) data is commonly used for secondary purposes such as research and clinical decision support. However, reuse of EHR data presents several challenges including but not limited to identifying all diagnoses asso...
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