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Статті в журналах з теми "Machine Learning, Bioinformatics, Rare Diseases, Healthcare"
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Hauschild, Anne-Christin, Marta Lemanczyk, Julian Matschinske, Tobias Frisch, Olga Zolotareva, Andreas Holzinger, Jan Baumbach, and Dominik Heider. "Federated Random Forests can improve local performance of predictive models for various healthcare applications." Bioinformatics 38, no. 8 (February 9, 2022): 2278–86. http://dx.doi.org/10.1093/bioinformatics/btac065.
Повний текст джерелаАнотація:
Abstract Motivation Limited data access has hindered the field of precision medicine from exploring its full potential, e.g. concerning machine learning and privacy and data protection rules. Our study evaluates the efficacy of federated Random Forests (FRF) models, focusing particularly on the heterogeneity within and between datasets. We addressed three common challenges: (i) number of parties, (ii) sizes of datasets and (iii) imbalanced phenotypes, evaluated on five biomedical datasets. Results The FRF outperformed the average local models and performed comparably to the data-centralized models trained on the entire data. With an increasing number of models and decreasing dataset size, the performance of local models decreases drastically. The FRF, however, do not decrease significantly. When combining datasets of different sizes, the FRF vastly improve compared to the average local models. We demonstrate that the FRF remain more robust and outperform the local models by analyzing different class-imbalances. Our results support that FRF overcome boundaries of clinical research and enables collaborations across institutes without violating privacy or legal regulations. Clinicians benefit from a vast collection of unbiased data aggregated from different geographic locations, demographics and other varying factors. They can build more generalizable models to make better clinical decisions, which will have relevance, especially for patients in rural areas and rare or geographically uncommon diseases, enabling personalized treatment. In combination with secure multi-party computation, federated learning has the power to revolutionize clinical practice by increasing the accuracy and robustness of healthcare AI and thus paving the way for precision medicine. Availability and implementation The implementation of the federated random forests can be found at https://featurecloud.ai/. Supplementary information Supplementary data are available at Bioinformatics online.
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R, Pooja M. "Application of Learning Approaches in Healthcare." International Journal of Advanced Medical Sciences and Technology 1, no. 3 (June 10, 2021): 1–2. http://dx.doi.org/10.35940/ijamst.b3005.061321.
Повний текст джерелаАнотація:
The learning approaches in healthcare would aim at phenotyping the disease based on clinical as well as physiological characteristics as ideally disease is defined and diagnosed by a combination of clinical symptoms and physiologic abnormalities.The medicine today is advanced into new realm with the growth of applications of artificial intelligence and machine learning in healthcare. This is important as we will not be addressing the target population for a specific disease alone; rather predict the likely outcome of the related disease in an unknown population of interest with the knowledge gained. This is of utmost focus especially with rare diseases, the data for which are available in lower volumes. Further, prediction outcomes available at earlier stages are important to prepare points of care to handle disastrous outcomes resulting from the diseases.
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M R, Pooja. "Application of Learning Approaches in Healthcare." International Journal of Advanced Medical Sciences and Technology 1, no. 3 (June 10, 2021): 1–2. http://dx.doi.org/10.54105/ijamst.b3005.061321.
Повний текст джерелаАнотація:
The learning approaches in healthcare would aim at phenotyping the disease based on clinical as well as physiological characteristics as ideally disease is defined and diagnosed by a combination of clinical symptoms and physiologic abnormalities. The medicine today is advanced into new realm with the growth of applications of artificial intelligence and machine learning in healthcare. This is important as we will not be addressing the target population for a specific disease alone; rather predict the likely outcome of the related disease in an unknown population of interest with the knowledge gained. This is of utmost focus especially with rare diseases, the data for which are available in lower volumes. Further, prediction outcomes available at earlier stages are important to prepare points of care to handle disastrous outcomes resulting from the diseases.
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Setty, Samarth Thonta, Marie-Pier Scott-Boyer, Tania Cuppens, and Arnaud Droit. "New Developments and Possibilities in Reanalysis and Reinterpretation of Whole Exome Sequencing Datasets for Unsolved Rare Diseases Using Machine Learning Approaches." International Journal of Molecular Sciences 23, no. 12 (June 18, 2022): 6792. http://dx.doi.org/10.3390/ijms23126792.
Повний текст джерелаАнотація:
Rare diseases impact the lives of 300 million people in the world. Rapid advances in bioinformatics and genomic technologies have enabled the discovery of causes of 20–30% of rare diseases. However, most rare diseases have remained as unsolved enigmas to date. Newer tools and availability of high throughput sequencing data have enabled the reanalysis of previously undiagnosed patients. In this review, we have systematically compiled the latest developments in the discovery of the genetic causes of rare diseases using machine learning methods. Importantly, we have detailed methods available to reanalyze existing whole exome sequencing data of unsolved rare diseases. We have identified different reanalysis methodologies to solve problems associated with sequence alterations/mutations, variation re-annotation, protein stability, splice isoform malfunctions and oligogenic analysis. In addition, we give an overview of new developments in the field of rare disease research using whole genome sequencing data and other omics.
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Yao, Junfeng, Wen Sun, Zhongquan Jian, Qingqiang Wu, and Xiaoli Wang. "Effective knowledge graph embeddings based on multidirectional semantics relations for polypharmacy side effects prediction." Bioinformatics 38, no. 8 (February 17, 2022): 2315–22. http://dx.doi.org/10.1093/bioinformatics/btac094.
Повний текст джерелаАнотація:
Abstract Motivation Polypharmacy is the combined use of drugs for the treatment of diseases. However, it often shows a high risk of side effects. Due to unnecessary interactions of combined drugs, the side effects of polypharmacy increase the risk of disease and even lead to death. Thus, obtaining abundant and comprehensive information on the side effects of polypharmacy is a vital task in the healthcare industry. Early traditional methods used machine learning techniques to predict side effects. However, they often make costly efforts to extract features of drugs for prediction. Later, several methods based on knowledge graphs are proposed. They are reported to outperform traditional methods. However, they still show limited performance by failing to model complex relations of side effects among drugs. Results To resolve the above problems, we propose a novel model by further incorporating complex relations of side effects into knowledge graph embeddings. Our model can translate and transmit multidirectional semantics with fewer parameters, leading to better scalability in large-scale knowledge graphs. Experimental evaluation shows that our model outperforms state-of-the-art models in terms of the average area under the ROC and precision–recall curves. Availability and implementation Code and data are available at: https://github.com/galaxysunwen/MSTE-master.
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Kothari, Sonali, Shwetambari Chiwhane, Shruti Jain, and Malti Baghel. "Cancerous brain tumor detection using hybrid deep learning framework." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 3 (June 1, 2022): 1651. http://dx.doi.org/10.11591/ijeecs.v26.i3.pp1651-1661.
Повний текст джерелаАнотація:
Computational <span>models based on deep learning (DL) algorithms have multiple processing layers representing data at multiple levels of abstraction. Deep learning has exploded in popularity in recent years, particularly in medical image processing, medical image analysis, and bioinformatics. As a result, deep learning has effectively modified and strengthened the means of identification, prediction, and diagnosis in several healthcare fields, including pathology, brain tumours, lung cancer, the abdomen, cardiac, and retina. In general, brain tumours are among the most common and aggressive malignant tumour diseases, with a limited life span if diagnosed at a higher grade. After identifying the tumour, brain tumour grading is a crucial step in evaluating a successful treatment strategy. This research aims to propose a cancerous brain tumor detection and classification using deep learning. In this paper, numerous soft computing techniques and a deep learning model to summarise the pathophysiology of brain cancer, imaging modalities for brain cancer, and automated computer-assisted methods for brain cancer characterization is used. In the sense of machine learning and the deep learning model, paper has highlighted the association between brain cancer and other brain disorders such as epilepsy, stroke, Alzheimer's, Parkinson's, and Wilson's disease, leukoaraiosis, and other neurological disorders.</span>
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Prakash, PKS, Srinivas Chilukuri, Nikhil Ranade, and Shankar Viswanathan. "RareBERT: Transformer Architecture for Rare Disease Patient Identification using Administrative Claims." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (May 18, 2021): 453–60. http://dx.doi.org/10.1609/aaai.v35i1.16122.
Повний текст джерелаАнотація:
A rare disease is any disease that affects a very small percentage (1 in 1,500) of population. It is estimated that there are nearly 7,000 rare disease affecting 30 million patients in the U. S. alone. Most of the patients suffering from rare diseases experience multiple misdiagnoses and may never be diagnosed correctly. This is largely driven by the low prevalence of the disease that results in a lack of awareness among healthcare providers. There have been efforts from machine learning researchers to develop predictive models to help diagnose patients using healthcare datasets such as electronic health records and administrative claims. Most recently, transformer models have been applied to predict diseases BEHRT, G-BERT and Med-BERT. However, these have been developed specifically for electronic health records (EHR) and have not been designed to address rare disease challenges such as class imbalance, partial longitudinal data capture, and noisy labels. As a result, they deliver poor performance in predicting rare diseases compared with baselines. Besides, EHR datasets are generally confined to the hospital systems using them and do not capture a wider sample of patients thus limiting the availability of sufficient rare dis-ease patients in the dataset. To address these challenges, we introduced an extension of the BERT model tailored for rare disease diagnosis called RareBERT which has been trained on administrative claims datasets. RareBERT extends Med-BERT by including context embedding and temporal reference embedding. Moreover, we introduced a novel adaptive loss function to handle the class imbal-ance. In this paper, we show our experiments on diagnosing X-Linked Hypophosphatemia (XLH), a genetic rare disease. While RareBERT performs significantly better than the baseline models (79.9% AUPRC versus 30% AUPRC for Med-BERT), owing to the transformer architecture, it also shows its robustness in partial longitudinal data capture caused by poor capture of claims with a drop in performance of only 1.35% AUPRC, compared with 12% for Med-BERT and 33.0% for LSTM and 67.4% for boosting trees based baseline.
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Ahmad, Iftikhar, Muhammad Javed Iqbal, and Mohammad Basheri. "Biological Data Classification and Analysis Using Convolutional Neural Network." Journal of Medical Imaging and Health Informatics 10, no. 10 (October 1, 2020): 2459–65. http://dx.doi.org/10.1166/jmihi.2020.3179.
Повний текст джерелаАнотація:
The size of data gathered from various ongoing biological and clinically studies is increasing at an exponential rate. The bio-inspired data mainly comprises of genes of DNA, protein and variety of proteomics and genetic diseases. Additionally, DNA microarray data is also available for early diagnosis and prediction of various types of cancer diseases. Interestingly, this data may store very vital information about genes, their structure and important biological function. The huge volume and constant increase in the extracted bio data has opened several challenges. Many bioinformatics and machine learning models have been developed but those fail to address key challenges presents in the efficient and accurate analysis of variety of complex biologically inspired data such as genetic diseases etc. The reliable and robust process of classifying the extracted data into different classes based on the information hidden in the sample data is also a very interesting and open problem. This research work mainly focuses to overcome major challenges in the accurate protein classification keeping in view of the success of deep learning models in natural language processing since it assumes the proteins sequences as a language. The learning ability and overall classification performance of the proposed system can be validated with deep learning classification models. The proposed system can have the superior ability to accurately classify the mentioned datasets than previous approaches and shows better results. The in-depth analysis of multifaceted biological data may also help in the early diagnosis of diseases that causes due to mutation of genes and to overcome arising challenges in the development of large-scale healthcare systems.
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Ahmad, Iftikhar, Muhammad Javed Iqbal, and Mohammad Basheri. "Biological Data Classification and Analysis Using Convolutional Neural Network." Journal of Medical Imaging and Health Informatics 10, no. 10 (October 1, 2020): 2459–65. http://dx.doi.org/10.1166/jmihi.2020.31792459.
Повний текст джерелаАнотація:
The size of data gathered from various ongoing biological and clinically studies is increasing at an exponential rate. The bio-inspired data mainly comprises of genes of DNA, protein and variety of proteomics and genetic diseases. Additionally, DNA microarray data is also available for early diagnosis and prediction of various types of cancer diseases. Interestingly, this data may store very vital information about genes, their structure and important biological function. The huge volume and constant increase in the extracted bio data has opened several challenges. Many bioinformatics and machine learning models have been developed but those fail to address key challenges presents in the efficient and accurate analysis of variety of complex biologically inspired data such as genetic diseases etc. The reliable and robust process of classifying the extracted data into different classes based on the information hidden in the sample data is also a very interesting and open problem. This research work mainly focuses to overcome major challenges in the accurate protein classification keeping in view of the success of deep learning models in natural language processing since it assumes the proteins sequences as a language. The learning ability and overall classification performance of the proposed system can be validated with deep learning classification models. The proposed system can have the superior ability to accurately classify the mentioned datasets than previous approaches and shows better results. The in-depth analysis of multifaceted biological data may also help in the early diagnosis of diseases that causes due to mutation of genes and to overcome arising challenges in the development of large-scale healthcare systems.
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Cesario, Alfredo, Marika D’Oria, Riccardo Calvani, Anna Picca, Antonella Pietragalla, Domenica Lorusso, Gennaro Daniele, et al. "The Role of Artificial Intelligence in Managing Multimorbidity and Cancer." Journal of Personalized Medicine 11, no. 4 (April 19, 2021): 314. http://dx.doi.org/10.3390/jpm11040314.
Повний текст джерелаАнотація:
Traditional healthcare paradigms rely on the disease-centered approach aiming at reducing human nature by discovering specific drivers and biomarkers that cause the advent and progression of diseases. This reductive approach is not always suitable to understand and manage complex conditions, such as multimorbidity and cancer. Multimorbidity requires considering heterogeneous data to tailor preventing and targeting interventions. Personalized Medicine represents an innovative approach to address the care needs of multimorbid patients considering relevant patient characteristics, such as lifestyle and individual preferences, in opposition to the more traditional “one-size-fits-all” strategy focused on interventions designed at the population level. Integration of omic (e.g., genomics) and non-strictly medical (e.g., lifestyle, the exposome) data is necessary to understand patients’ complexity. Artificial Intelligence can help integrate and manage heterogeneous data through advanced machine learning and bioinformatics algorithms to define the best treatment for each patient with multimorbidity and cancer. The experience of an Italian research hospital, leader in the field of oncology, may help to understand the multifaceted issue of managing multimorbidity and cancer in the framework of Personalized Medicine.
Дисертації з теми "Machine Learning, Bioinformatics, Rare Diseases, Healthcare"
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Visibelli, Anna. "Machine learning in Bioinformatics: Novel approaches to Precision Medicine, Life Sciences and Healthcare." Doctoral thesis, Università di Siena, 2022. http://hdl.handle.net/11365/1182445.
Повний текст джерелаАнотація:
In recent years, biological research revolves around huge amounts of data which are extrapolated due to high-throughput techniques. Thanks to the emergence of omics information and big data, the use of computational tools has become crucial to evaluate the efficacy of medical treatments or deeply investigate the correlation between patients and diseases according to their own molecular characteristics. The Precision Medicine approach is widely applied to the healthcare area, in particular to rare diseases with the creation of patient registries leveraging large amounts of data to discover potential links. Harmonizing databases and including disease registries are the major facilitators to understand the complexity of diseases, to conduct clinical trials, to improve the drug development process and to assign the right treatment to the right individual after a reliable patient stratification. Moreover, the application of data mining in healthcare and public health, which has been growing over the last years, allows to systematically identify inefficiencies and best practices that improve care and reduce costs with remarkable economic benefits. In this thesis we focus on the development of new Artificial Intelligence algorithms for a number of important problems in the field of Precision Medicine, Life Sciences and Healthcare. The project demonstrates the power of computational modelling for clinical research, opening up possibilities that would be unimaginable without knowledge of the data. The application of Bioinformatics and Computational biology algorithms together with the creation of digital databases will offer an opportunity to translate new data into actionable information.
Частини книг з теми "Machine Learning, Bioinformatics, Rare Diseases, Healthcare"
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Mehta, Neha, and Archana Chaudhary. "Patient Empowerment of People with Rare Diseases." In Machine Learning Approaches and Applications in Applied Intelligence for Healthcare Data Analytics, 27–40. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003132110-3.
Повний текст джерела
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Biloborodova, Tetiana, Inna Skarga-Bandurova, Mark Koverha, Illia Skarha-Bandurov, and Yelyzaveta Yevsieieva. "A Learning Framework for Medical Image-Based Intelligent Diagnosis from Imbalanced Datasets." In Applying the FAIR Principles to Accelerate Health Research in Europe in the Post COVID-19 Era. IOS Press, 2021. http://dx.doi.org/10.3233/shti210801.
Повний текст джерелаАнотація:
Medical image classification and diagnosis based on machine learning has made significant achievements and gradually penetrated the healthcare industry. However, medical data characteristics such as relatively small datasets for rare diseases or imbalance in class distribution for rare conditions significantly restrains their adoption and reuse. Imbalanced datasets lead to difficulties in learning and obtaining accurate predictive models. This paper follows the FAIR paradigm and proposes a technique for the alignment of class distribution, which enables improving image classification performance in imbalanced data and ensuring data reuse. The experiments on the acne disease dataset support that the proposed framework outperforms the baselines and enable to achieve up to 5% improvement in image classification.