Ontology-Guided Principal Component Analysis: Reaching the Limits of the Doctor-in-the-Loop

Wartner, Sandra; Girardi, Dominic; Wiesinger-Widi, Manuela; Trenkler, Johannes; Kleiser, Raimund; Holzinger, Andreas

doi:10.1007/978-3-319-43949-5_2

Cited by 9 publications

(8 citation statements)

References 20 publications

(19 reference statements)

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“…Ontology learning generally (Maedche and Staab, 2001), and ontology learning from text specifically is a hot topic (Cimiano and Völker, 2005). Ontology reasoning, nowadays based on deep learning rather than logic-based formal reasoning (Hohenecker and Lukasiewicz, 2017) is of great interest, and the applications of ontology-guided approaches are of much practical help for the medical domain expert Wartner et al, 2016).…”

Section: Explainable Models For Textmentioning

confidence: 99%

What do we need to build explainable AI systems for the medical domain?

Holzinger,

Biemann,

Pattichis

et al. 2017

Preprint

Self Cite

152

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Artificial intelligence (AI) generally and machine learning (ML) specifically demonstrate impressive practical success in many different application domains, e.g. in autonomous driving, speech recognition, or recommender systems. Deep learning approaches, trained on extremely large data sets or using reinforcement learning methods have even exceeded human performance in visual tasks, particularly on playing games such as Atari, or mastering the game of Go. Even in the medical domain there are remarkable results. However, the central problem of such models is that they are regarded as black-box models and even if we understand the underlying mathematical principles of such models they lack an explicit declarative knowledge representation, hence have difficulty in generating the underlying explanatory structures. This calls for systems enabling to make decisions transparent, understandable and explainable. A huge motivation for our approach are rising legal and privacy aspects. The new European General Data Protection Regulation (GDPR and ISO/IEC 27001) entering into force on May 25th 2018, will make black-box approaches difficult to use in business. This does not imply a ban on automatic learning approaches or an obligation to explain everything all the time, however, there must be a possibility to make the results re-traceable on demand. This is beneficial, e.g. for general understanding, for teaching, for learning, for research, and it can be helpful in court. In this paper we outline some of our research topics in the context of the relatively new area of explainable-AI with a focus on the application in medicine, which is a very special domain. This is due to the fact that medical professionals are working mostly with distributed heterogeneous and complex sources of data. In this paper we concentrate on three sources: images, *omics data and text. We argue that research in explainable-AI would generally help to facilitate the implementation of AI/ML in the medical domain, and specifically help to facilitate transparency and trust.

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Section: Explainable Models For Textmentioning

confidence: 99%

What do we need to build explainable AI systems for the medical domain?

Holzinger,

Biemann,

Pattichis

et al. 2017

Preprint

Self Cite

152

145

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“…In fact the analysis of all the features helped to determine their particular importance in the frailty prediction. Further, the application of the ontology-based PCA approach described by Wartner et al (2016) [37] was able to deliver some insights, which were further investigated.…”

Section: Discussionmentioning

confidence: 99%

“…Through analysis of known frailty related factors via ontology-guided PCA using the approach described by Wartner et al (2016) [37], mining association rules using the apriori algorithm [38] and general correlation statistics, it can be assumed that the from the doctors described relationships are also present in the data.…”

Section: Methodsmentioning

confidence: 99%

Importance of medical data preprocessing in predictive modeling and risk factor discovery for the frailty syndrome

Hassler

Menasalvas

García‐García

et al. 2019

BMC Med Inform Decis Mak

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Background Increasing life expectancy results in more elderly people struggling with age related diseases and functional conditions. This poses huge challenges towards establishing new approaches for maintaining health at a higher age. An important aspect for age related deterioration of the general patient condition is frailty. The frailty syndrome is associated with a high risk for falls, hospitalization, disability, and finally increased mortality. Using predictive data mining enables the discovery of potential risk factors and can be used as clinical decision support system, which provides the medical doctor with information on the probable clinical patient outcome. This enables the professional to react promptly and to avert likely adverse events in advance. Methods Medical data of 474 study participants containing 284 health related parameters, including questionnaire answers, blood parameters and vital parameters from the Toledo Study for Healthy Aging (TSHA) was used. Binary classification models were built in order to distinguish between frail and non-frail study subjects. Results Using the available TSHA data and the discovered potential predictors, it was possible to design, develop and evaluate a variety of different predictive models for the frailty syndrome. The best performing model was the support vector machine (SVM, 78.31%). Moreover, a methodology was developed, making it possible to explore and to use incomplete medical data and further identify potential predictors and enable interpretability. Conclusions This work demonstrates that it is feasible to use incomplete, imbalanced medical data for the development of a predictive model for the frailty syndrome. Moreover, potential predictive factors have been discovered, which were clinically approved by the clinicians. Future work will improve prediction accuracy, especially with regard to separating the group of frail patients into frail and pre-frail ones and analyze the differences among them. Electronic supplementary material The online version of this article (10.1186/s12911-019-0747-6) contains supplementary material, which is available to authorized users.

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“…However, in some cases, the biological question may not be related to the highest variance in the data [179,180]. To address these limitations, various variations of knowledge-guided PCA have been proposed, such as ontology-guided PCA [181], knowledge-guided gene ranking by coordinative component analysis [182]. In addition, maximum likelihood PCA (MLPCA) has been proposed that can integrate process knowledge into PCA such as measurement errors, nonstationary behavior, and heterogeneous variances and correlations [183][184][185][186].…”

Section: Knowledge-guided Unsupervised Learningmentioning

confidence: 99%

Application of Systems Engineering Principles and Techniques in Biological Big Data Analytics: A Review

Wang

2020

Processes

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In the past few decades, we have witnessed tremendous advancements in biology, life sciences and healthcare. These advancements are due in no small part to the big data made available by various high-throughput technologies, the ever-advancing computing power, and the algorithmic advancements in machine learning. Specifically, big data analytics such as statistical and machine learning has become an essential tool in these rapidly developing fields. As a result, the subject has drawn increased attention and many review papers have been published in just the past few years on the subject. Different from all existing reviews, this work focuses on the application of systems, engineering principles and techniques in addressing some of the common challenges in big data analytics for biological, biomedical and healthcare applications. Specifically, this review focuses on the following three key areas in biological big data analytics where systems engineering principles and techniques have been playing important roles: the principle of parsimony in addressing overfitting, the dynamic analysis of biological data, and the role of domain knowledge in biological data analytics.

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Ontology-Guided Principal Component Analysis: Reaching the Limits of the Doctor-in-the-Loop

Cited by 9 publications

References 20 publications

What do we need to build explainable AI systems for the medical domain?

What do we need to build explainable AI systems for the medical domain?

Importance of medical data preprocessing in predictive modeling and risk factor discovery for the frailty syndrome

Application of Systems Engineering Principles and Techniques in Biological Big Data Analytics: A Review

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