Scaled radial axes for interactive visual feature selection: A case study for analyzing chronic conditions

Sánchez, Alberto; Soguero-Ruíz, Cristina; Mora-Jiménez, Inmaculada; Flores, Francisco Javier Rivas; Lehmann, Dirk J.; Rubio-Sánchez, Manuel

doi:10.1016/j.eswa.2018.01.054

Cited by 33 publications

(23 citation statements)

References 22 publications

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“…We report experimental results for training linear and non-linear models on this dataset, and show that despite its small size we can achieve AUROC scores that are comparable to approaches that have trained on significantly larger datasets. This finding is consistent with recent reports on the usefulness of machine learning from highly reliable judgements by medical doctors, e.g., for the tasks of sample selection (Holzinger, 2016) or feature selection (Sanchez et al, 2018). We conclude our work by presenting case studies of sepsis prediction for terminally ill patients, and by discussing feature contributions by inspecting learned weights for the linear model.…”

Section: Introductionsupporting

confidence: 89%

Leveraging implicit expert knowledge for non-circular machine learning in sepsis prediction

Schamoni

Lindner

Schneider‐Lindner

et al. 2019

Artificial Intelligence in Medicine

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Sepsis is the leading cause of death in non-coronary intensive care units. Moreover, a delay of antibiotic treatment of patients with severe sepsis by only few hours is associated with increased mortality. This insight makes accurate models for early prediction of sepsis a key task in machine learning for healthcare. Previous approaches have achieved high AUROC by learning from electronic health records where sepsis labels were defined automatically following established clinical criteria. We argue that the practice of incorporating the clinical criteria that are used to automatically define ground truth sepsis labels as features of severity scoring models is inherently circular and compromises the validity of the proposed approaches. We propose to create an independent ground truth for sepsis research by exploiting implicit knowledge of clinical practitioners via an electronic questionnaire which records attending physicians' daily judgements of patients' sepsis status. We show that despite its small size, our dataset allows to achieve state-of-the-art AUROC scores. An inspection of learned weights for standardized features of the linear model lets us infer potentially surprising feature contributions and allows to interpret seemingly counterintuitive findings.

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Section: Introductionsupporting

confidence: 89%

Leveraging implicit expert knowledge for non-circular machine learning in sepsis prediction

Schamoni

Lindner

Schneider‐Lindner

et al. 2019

Artificial Intelligence in Medicine

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“…In addition, the visualizations allow analysts to determine the contribution of the different features to the classification, by examining the lengths and orientations of the SC axis vectors. Although the longer axis vectors are more relevant to the plot in general [21], their orientations should also be considered when determining the most discriminative features that contribute more to separating the classes [26].…”

Section: Visually Guided Classification Treesmentioning

confidence: 99%

“…In particular, analysts simply focus on the longest vectors that are oriented in the direction that better separates the classes, and choose one of these vectors according to their expertise. Thus, the visualizations allow clinicians to carry out feature selection interactively (see [21,[24][25][26]), where they can take advantage of their domain knowledge when selecting discriminative features.…”

Section: Introductionmentioning

confidence: 99%

Visually guided classification trees for analyzing chronic patients

et al. 2020

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Background: Chronic diseases are becoming more widespread each year in developed countries, mainly due to increasing life expectancy. Among them, diabetes mellitus (DM) and essential hypertension (EH) are two of the most prevalent ones. Furthermore, they can be the onset of other chronic conditions such as kidney or obstructive pulmonary diseases. The need to comprehend the factors related to such complex diseases motivates the development of interpretative and visual analysis methods, such as classification trees, which not only provide predictive models for diagnosing patients, but can also help to discover new clinical insights. Results: In this paper, we analyzed healthy and chronic (diabetic, hypertensive) patients associated with the University Hospital of Fuenlabrada in Spain. Each patient was classified into a single health status according to clinical risk groups (CRGs). The CRGs characterize a patient through features such as age, gender, diagnosis codes, and drug codes. Based on these features and the CRGs, we have designed classification trees to determine the most discriminative decision features among different health statuses. In particular, we propose to make use of statistical data visualizations to guide the selection of features in each node when constructing a tree. We created several classification trees to distinguish among patients with different health statuses. We analyzed their performance in terms of classification accuracy, and drew clinical conclusions regarding the decision features considered in each tree. As expected, healthy patients and patients with a single chronic condition were better classified than patients with comorbidities. The constructed classification trees also show that the use of antipsychotics and the diagnosis of chronic airway obstruction are relevant for classifying patients with more than one chronic condition, in conjunction with the usual DM and/or EH diagnoses. Conclusions: We propose a methodology for constructing classification trees in a visually guided manner. The approach allows clinicians to progressively select the decision features at each of the tree nodes. The process is guided by exploratory data analysis visualizations, which may provide new insights and unexpected clinical information.

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“…I-TWEC [6] is an interactive web-based clustering tool for twitter data that utilized suffix tree based algorithm to cluster user uploaded tweets using their semantic. Some other examples includes Cluster Sculptor [52],INFUSE [53],radial axes method for visual backward feature selection [54] etc .These tools facilitate analyst to steer the feature selection process according to their domain knowledge and specification.…”

Section: Clustering Similar Crimesmentioning

confidence: 99%

An interactive human centered data science approach towards crime pattern analysis

Qazi

Wong

2019

Information Processing & Management

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Scaled radial axes for interactive visual feature selection: A case study for analyzing chronic conditions

Cited by 33 publications

References 22 publications

Leveraging implicit expert knowledge for non-circular machine learning in sepsis prediction

Leveraging implicit expert knowledge for non-circular machine learning in sepsis prediction

Visually guided classification trees for analyzing chronic patients

An interactive human centered data science approach towards crime pattern analysis

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