The utilisation of machine learning approaches for medical data classification and personal care system mangementfor sickle cell disease

2017 ◽  
Author(s):  
DhafarHamed Abd ◽  
Jwan K. Alwan ◽  
Mohamed Ibrahim ◽  
Mohammad B. Naeem
Keyword(s):  
Machine Learning ◽  
Sickle Cell Disease ◽  
Sickle Cell ◽  
Data Classification ◽  
Medical Data ◽  
Learning Approaches ◽  
Personal Care ◽  
Cell Disease ◽  
Medical Data Classification ◽  
Care System

scholarly journals Machine learning predicts early-onset acute organ failure in critically ill patients with sickle cell disease

2019 ◽  
Author(s):  
Akram Mohammed ◽  
Pradeep S. B. Podila ◽  
Robert L. Davis ◽  
Kenneth I. Ataga ◽  
Jane S. Hankins ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Sickle Cell Disease ◽  
Kidney Disease ◽  
Organ Failure ◽  
Organ Dysfunction ◽  
Sickle Cell ◽  
Machine Learning Techniques ◽  
Cell Disease ◽  
Learning Techniques

AbstractBackgroundSickle cell disease (SCD) is a genetic disorder of the red blood cells, resulting in multiple acute and chronic complications including pain episodes, stroke, and kidney disease. Patients with SCD develop chronic organ dysfunction, which may progress to organ failure during disease exacerbations. Early detection of acute physiological deterioration leading to organ failure is not always attainable. Machine learning techniques that allow for prediction of organ failure may enable earlier identification and treatment, and potentially reduce mortality. We tested the hypothesis that machine learning physiomarkers could predict the development of organ dysfunction in an adult sample of patients with SCD admitted to intensive care units.Methods and FindingsWe studied 63 sequential SCD patients with 163 patient encounters, mean age 33.0±11.0 years, admitted to intensive care units, some of whom (6.7%) had pre-existing cardiovascular or kidney disease. A subset of these patient encounters (37; 23%) met sequential organ failure assessment (SOFA) criteria. The site of organ failure included: central nervous system (32), cardiovascular (11), renal (10), liver (7), respiratory (5) and coagulation (2) systems. Most (81.5%) of the patient encounters who experienced organ failure had single organ failure. The other 126 SCD patient encounters served as controls. A set of signal processing features (such as fast fourier transform, energy, continuous wavelet transform, etc.) derived from heart rate, blood pressure, and respiratory rate were identified to distinguish patients with SCD who developed acute physiological deterioration leading to organ failure, from SCD patients who did not meet the criteria. A random forest model accurately predicted organ failure up to six hours prior to onset, with a five-fold cross-validation accuracy of 94.57% (average sensitivity and specificity of 90.24% and 98.9% respectively).ConclusionsThis study demonstrates the viability of using machine learning to predict acute physiological deterioration heralded organ failure among hospitalized adults with SCD. The discovery of salient physiomarkers through machine learning techniques has the potential to further accelerate the development and implementation of innovative care delivery protocols and strategies for medically vulnerable patients.

Machine‐learning algorithms for predicting hospital re‐admissions in sickle cell disease

2020 ◽  
Vol 192 (1) ◽  
pp. 158-170
Author(s):  
Arisha Patel ◽  
Kyra Gan ◽  
Andrew A. Li ◽  
Jeremy Weiss ◽  
Mehdi Nouraie ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sickle Cell Disease ◽  
Sickle Cell ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Cell Disease

scholarly journals A Framework for Medical Data Analysis using Deep Learning based on Conventional Neural Network (CNN) and Variable Auto-Encoder

2019 ◽  
Vol 8 (3) ◽  
pp. 852-857
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Data Classification ◽  
Medical Data ◽  
Complex Task ◽  
Medical Data Classification ◽  
High Level ◽  
Performance Results

Medical data classification is an important and complex task. Due to the nature of data, the data is in different forms like text, numeric, images and sometimes combination of all. The goal of this paper is to provide a high-level introduction into practical machine learning for purposes of medical data classification. In this paper we use CNN-Auto encoder to extract data from the medical repository and made the classification of heterogeneous medical data. Here Auto encoder uses to get the prime features and CNN is there to extract detailed features. Combination of these two mechanisms are more suitable for medical data classification. Hybrid AE-CNN (auto encoder based Convolutional neural network). Here the performance of proposed mechanism with respect to baseline methods will be assessed. The performance results showed that the proposed mechanism performed well.

scholarly journals Improved radiation expression profiling in blood by sequential application of sensitive and specific gene signatures

2021 ◽  
Author(s):  
Eliseos J. Mucaki ◽  
Ben C. Shirley ◽  
Peter K. Rogan
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Dna Damage ◽  
Sickle Cell Disease ◽  
Aplastic Anemia ◽  
Dna Damage Response ◽  
Sickle Cell ◽  
False Positive ◽  
Cell Disease ◽  
Damage Response

AbstractPurposeCombinations of expressed genes can discriminate radiation-exposed from normal control blood samples by machine learning based signatures (with 8 to 20% misclassification rates). These signatures can quantify therapeutically-relevant as well as accidental radiation exposures. The prodromal symptoms of Acute Radiation Syndrome (ARS) overlap some viral infections. We recently showed that these human radiation signatures produced unexpected false positive misclassification of Influenza and Dengue infected samples. The present study investigates these and other confounders, and then mitigates their effects on signature accuracy.MethodsThis study investigated recall by previous and novel radiation signatures independently derived from multiple Gene Expression Omnibus datasets on common and rare non-malignant blood disorders and blood-borne infections (thromboembolism, S. aureus bacteremia, malaria, sickle cell disease, polycythemia vera, and aplastic anemia). Normalized expression levels of signature genes are used as input to machine learning-based classifiers to predict radiation exposure in other hematological conditions.ResultsExcept for aplastic anemia, these blood-borne disorders modify the normal baseline expression values of genes present in radiation signatures, leading to false-positive misclassification of radiation exposures in 8 to 54% of individuals. Shared changes, predominantly in DNA damage response and apoptosis-related gene transcripts in radiation and confounding hematological conditions, compromise the utility of these signatures for radiation assessment. These confounding conditions (sickle cell disease, thromboembolism, S. aureus bacteremia, malaria) induce neutrophil extracellular traps, initiated by chromatin decondensation, DNA damage response and fragmentation followed by programmed cell death. Riboviral infections (for example, Influenza, Dengue fever) are proposed to deplete RNA binding proteins, inducing R-loops in chromatin which collide with replication forks resulting in DNA damage, and apoptosis. To mitigate the effects of confounders, we evaluated predicted radiation positive samples with novel gene expression signatures derived from radiation-responsive transcripts encoding secreted blood plasma proteins whose expression levels are unperturbed by these conditions.ConclusionsThis approach identifies and eliminates misclassified samples with underlying hematological or infectious conditions, leaving only samples with true radiation exposures. Diagnostic accuracy is significantly improved by selecting genes that maximize both sensitivity and specificity in the appropriate tissue using combinations of the best signatures for each of these classes of signatures.

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