A Kernel-Based Framework for Medical Big-Data Analytics

Windridge, David; Bober, Miroslaw

doi:10.1007/978-3-662-43968-5_11

Cited by 11 publications

(7 citation statements)

References 27 publications

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“…A German calciphylaxis registry proposed a framework and developed a tool to integrate medical record, imaging data, and signal data for the purpose of improving knowledge of rare diseases (Deserno et al, 2014). Windridge and Bober (2014) proposed a kernel-based framework to analyze heterogeneous data in the medical domain, which addressed the missing data problem presented by patients with sparse or absent data modalities. Using the kernel method, regression and classification of heterogeneous medical information can be achieved.…”

Section: Physical Examination Systemmentioning

confidence: 99%

Big Data in Health Care: Applications and Challenges

Hong

Luo

Wang

et al. 2018

Data and Information Management

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The concept of Big Data is popular in a variety of domains. The purpose of this review was to summarize the features, applications, analysis approaches, and challenges of Big Data in health care. Big Data in health care has its own features, such as heterogeneity, incompleteness, timeliness and longevity, privacy, and ownership. These features bring a series of challenges for data storage, mining, and sharing to promote healthrelated research. To deal with these challenges, analysis approaches focusing on Big Data in health care need to be developed and laws and regulations for making use of Big Data in health care need to be enacted. From a patient perspective, application of Big Data analysis could bring about improved treatment and lower costs. In addition to patients, government, hospitals, and research institutions could also benefit from the Big Data in health care. Abstract:In the concept of the aesthetic formation of knowledge and its as soon as possible and success-oriented application, insights and profits without the reference to the arguments developed around 1900. The main investigation also includes the period between the entry into force and the presentation in its current version. Their function as part of the literary portrayal and narrative technique. Studies and InvestigationsThe main investigation also includes the period between the entry into force and the presentation in its current version. Their function as part of the literary portrayal and narrative technique.Abstract: In the concept of the aesthetic formation of knowledge and its as soon as possible and success-oriented application, insights and profits without the reference to the arguments developed around 1900. The main investigation also includes the period between the entry into force and the presentation in its current version. Their function as part of the literary portrayal and narrative technique. Studies and InvestigationsThe main investigation also includes the period between the entry into force and the presentation in its current version. Their function as part of the literary portrayal and narrative technique.

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Section: Physical Examination Systemmentioning

confidence: 99%

Big Data in Health Care: Applications and Challenges

Hong

Luo

Wang

et al. 2018

Data and Information Management

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“…Clinical data, however, is characterized by uncertainties as well as heterogeneity (various data types), high dimensionality, and incompleteness (sparsity) [14, 15]. Such structure and characteristics put challenges on conventional machine-learning methods such as support vector machines, artificial neural networks, and decision trees.…”

Section: Background and Basic Taxonomiesmentioning

confidence: 99%

Therapy Decision Support Based on Recommender System Methods

Gräßer

Beckert²,

Küster³

et al. 2017

Journal of Healthcare Engineering

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We present a system for data-driven therapy decision support based on techniques from the field of recommender systems. Two methods for therapy recommendation, namely, Collaborative Recommender and Demographic-based Recommender, are proposed. Both algorithms aim to predict the individual response to different therapy options using diverse patient data and recommend the therapy which is assumed to provide the best outcome for a specific patient and time, that is, consultation. The proposed methods are evaluated using a clinical database incorporating patients suffering from the autoimmune skin disease psoriasis. The Collaborative Recommender proves to generate both better outcome predictions and recommendation quality. However, due to sparsity in the data, this approach cannot provide recommendations for the entire database. In contrast, the Demographic-based Recommender performs worse on average but covers more consultations. Consequently, both methods profit from a combination into an overall recommender system.

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“…According to [64] there are five theoretical bases of graph-based data mining approaches such as (1) subgraph categories, (2) subgraph isomorphism, (3) graph invariants, (4) mining measures and (5) solution methods. Furthermore, there are five groups of different graph-theoretical approaches for data mining such as (1) greedy search based approach, (2) inductive logic programming based approach, (3) inductive database based approach, (4) mathematical graph theory based approach and (5) kernel function based approach [68]. However, the main disadvantage of graphtheoretical text mining is the computational complexity of the graph representation, consequently the goal of future research in the field of graph-theoretical approaches for text mining is to develop efficient graph mining algorithms which implement effective search strategies and data structures [63].…”

Section: Research Track 3 Gdm Graph-based Data Miningmentioning

confidence: 99%

Machine Learning for Health Informatics

Holzinger

2016

Lecture Notes in Computer Science

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Machine Learning (ML) studies algorithms which can learn from data to gain knowledge from experience and to make decisions and predictions. Health Informatics (HI) studies the effective use of probabilistic information for decision making. The combination of both has greatest potential to rise quality, efficacy and efficiency of treatment and care. Health systems worldwide are confronted with "big data" in high dimensions, where the inclusion of a human is impossible and automatic ML (aML) show impressive results. However, sometimes we are confronted with complex data, "little data", or rare events, where aMLapproaches suffer of insufficient training samples. Here interactive ML (iML) may be of help, particularly with a doctor-in-the-loop, e.g. in subspace clustering, k-Anonymization, protein folding and protein design. However, successful application of ML for HI needs an integrated approach, fostering a concerted effort of four areas: (1) data science, (2) algorithms (with focus on networks and topology (structure), and entropy (time), (3) data visualization, and last but not least (4) privacy, data protection, safety & security.

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A Kernel-Based Framework for Medical Big-Data Analytics

Cited by 11 publications

References 27 publications

Big Data in Health Care: Applications and Challenges

Big Data in Health Care: Applications and Challenges

Therapy Decision Support Based on Recommender System Methods

Machine Learning for Health Informatics

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