Deep Learning for Polycystic Kidney Disease: Utilizing Neural Networks
for Accurate and Early Detection through Gene Expression Analysis
- URL: http://arxiv.org/abs/2309.03033v2
- Date: Sat, 23 Sep 2023 19:45:15 GMT
- Title: Deep Learning for Polycystic Kidney Disease: Utilizing Neural Networks
for Accurate and Early Detection through Gene Expression Analysis
- Authors: Kapil Panda, Anirudh Mazumder
- Abstract summary: Polycystic Kidney Disease (PKD) potentially leading to fatal complications in patients due to the formation of cysts in kidneys.
In this study we aim to utilize a deep learning-based approach for early disease detection through gene expression analysis.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: With Polycystic Kidney Disease (PKD) potentially leading to fatal
complications in patients due to the formation of cysts in kidneys, early
detection of PKD is crucial for effective management of the condition. However,
the various patient-specific factors that play a role in the diagnosis make it
an intricate puzzle for clinicians to solve, leading to possible kidney
failure. Therefore, in this study we aim to utilize a deep learning-based
approach for early disease detection through gene expression analysis. The
devised neural network is able to achieve accurate and robust prediction
results for possible PKD in kidneys, thereby improving patient outcomes.
Furthermore, by conducting a gene ontology analysis, we were able to predict
the top gene processes and functions that PKD may affect.
Related papers
- AI Age Discrepancy: A Novel Parameter for Frailty Assessment in Kidney Tumor Patients [3.2441121935479877]
This paper introduces AI Age Discrepancy, a novel metric derived from machine learning analysis of preoperative abdominal CT scans.
A higher AI Age Discrepancy is significantly associated with longer hospital stays and lower overall survival rates, independent of established factors.
arXiv Detail & Related papers (2024-06-29T13:15:05Z) - AI-Driven Predictive Analytics Approach for Early Prognosis of Chronic Kidney Disease Using Ensemble Learning and Explainable AI [0.26217304977339473]
Chronic Kidney Disease (CKD) is a heterogeneous disorder that significantly impacts kidney structure and functions, eventually leading to kidney failure.
The goal of this research is to visualize dominating features, feature scores, and values exhibited for early prognosis and detection of CKD using ensemble learning and explainable AI.
arXiv Detail & Related papers (2024-06-10T18:46:14Z) - Survival Prediction Across Diverse Cancer Types Using Neural Networks [40.392772795903795]
Gastric cancer and Colon adenocarcinoma represent widespread and challenging malignancies.
Medical community has embraced the 5-year survival rate as a vital metric for estimating patient outcomes.
This study introduces a pioneering approach to enhance survival prediction models for gastric and Colon adenocarcinoma patients.
arXiv Detail & Related papers (2024-04-11T21:47:13Z) - Current and future roles of artificial intelligence in retinopathy of
prematurity [14.333209377077058]
Retinopathy of prematurity (ROP) is a severe condition affecting premature infants.
Recent advancements in deep learning (DL), especially convolutional neural networks (CNNs) have significantly improved ROP detection and classification.
The i-ROP deep learning (i-ROP-DL) system also shows promise in detecting plus disease, offering reliable ROP diagnosis potential.
arXiv Detail & Related papers (2024-02-15T14:35:02Z) - Multimodal Deep Learning for Personalized Renal Cell Carcinoma
Prognosis: Integrating CT Imaging and Clinical Data [3.790959613880792]
Renal cell carcinoma represents a significant global health challenge with a low survival rate.
This research aimed to devise a comprehensive deep-learning model capable of predicting survival probabilities in patients with renal cell carcinoma.
The proposed framework comprises three modules: a 3D image feature extractor, clinical variable selection, and survival prediction.
arXiv Detail & Related papers (2023-07-07T13:09:07Z) - Towards Outcome-Driven Patient Subgroups: A Machine Learning Analysis
Across Six Depression Treatment Studies [41.34047608276278]
We analyzed data from six clinical trials of pharmacological treatment for depression using a neural network model.
A model classifying remission and outputting individual remission probabilities for five first-line monotherapies and three combination treatments was trained.
Post-hoc analyses yielded clusters (subgroups) based on patient prototypes learned during training.
arXiv Detail & Related papers (2023-03-24T14:34:09Z) - Machine learning for dynamically predicting the onset of renal
replacement therapy in chronic kidney disease patients using claims data [0.89379057739817]
Chronic kidney disease (CKD) represents a slowly progressive disorder that can eventually require renal replacement therapy (RRT)
Early identification of patients who will require RRT improves patient outcomes.
There is currently no commonly used predictive tool for RRT initiation.
arXiv Detail & Related papers (2022-09-03T17:50:27Z) - Identification of Ischemic Heart Disease by using machine learning
technique based on parameters measuring Heart Rate Variability [50.591267188664666]
In this study, 18 non-invasive features (age, gender, left ventricular ejection fraction and 15 obtained from HRV) of 243 subjects were used to train and validate a series of several ANN.
The best result was obtained using 7 input parameters and 7 hidden nodes with an accuracy of 98.9% and 82% for the training and validation dataset.
arXiv Detail & Related papers (2020-10-29T19:14:41Z) - Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic
Programmed Deep Kernels [93.58854458951431]
We present a probabilistic programmed deep kernel learning approach to personalized, predictive modeling of neurodegenerative diseases.
Our analysis considers a spectrum of neural and symbolic machine learning approaches.
We run evaluations on the problem of Alzheimer's disease prediction, yielding results that surpass deep learning.
arXiv Detail & Related papers (2020-09-16T15:16:03Z) - Integrative Analysis for COVID-19 Patient Outcome Prediction [53.11258640541513]
We combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit admission.
Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia.
arXiv Detail & Related papers (2020-07-20T19:08:50Z) - Hemogram Data as a Tool for Decision-making in COVID-19 Management:
Applications to Resource Scarcity Scenarios [62.997667081978825]
COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure.
This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients.
Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity.
arXiv Detail & Related papers (2020-05-10T01:45:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.