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  1. Article ; Online: Eetstoornissen.

    van Leeuwen, Margriet

    Nederlands tijdschrift voor geneeskunde

    2022  Volume 166

    Abstract: Eating disorders, such as anorexia, bulimia and binge eating disorder, are a common mental health problem, but are even so easily missed in the medical field. Patients experience a lot of shame to come up with their eating problem. Doctors tend to forget ...

    Title translation Eating disorders.
    Abstract Eating disorders, such as anorexia, bulimia and binge eating disorder, are a common mental health problem, but are even so easily missed in the medical field. Patients experience a lot of shame to come up with their eating problem. Doctors tend to forget asking for eating pattern and purging when a patient has a normal weight or is obese. A third of the obese population experience binges. A relatively new diagnose is ARFID (avoidant restrictive food intake disorder). Patients are not scared to gain weight, but have nutritional deficits because of not being able to eat, forgetting to eat or eating only a couple of products. Motivating patients to seek treatment is challenging. Understanding their struggles, knowing the complications and what to examine is important. The article gives an overview how to diagnose and examine eating disorders and when and where to refer to.
    MeSH term(s) Anorexia Nervosa/psychology ; Feeding Behavior ; Feeding and Eating Disorders/complications ; Feeding and Eating Disorders/diagnosis ; Humans ; Obesity/complications ; Retrospective Studies
    Language Dutch
    Publishing date 2022-05-03
    Publishing country Netherlands
    Document type Journal Article ; Review
    ZDB-ID 82073-8
    ISSN 1876-8784 ; 0028-2162
    ISSN (online) 1876-8784
    ISSN 0028-2162
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: Probabilistic Truly Unordered Rule Sets

    Yang, Lincen / van Leeuwen, Matthijs

    2024  

    Abstract: Rule set learning has recently been frequently revisited because of its interpretability. Existing methods have several shortcomings though. First, most existing methods impose orders among rules, either explicitly or implicitly, which makes the models ... ...

    Abstract Rule set learning has recently been frequently revisited because of its interpretability. Existing methods have several shortcomings though. First, most existing methods impose orders among rules, either explicitly or implicitly, which makes the models less comprehensible. Second, due to the difficulty of handling conflicts caused by overlaps (i.e., instances covered by multiple rules), existing methods often do not consider probabilistic rules. Third, learning classification rules for multi-class target is understudied, as most existing methods focus on binary classification or multi-class classification via the ``one-versus-rest" approach. To address these shortcomings, we propose TURS, for Truly Unordered Rule Sets. To resolve conflicts caused by overlapping rules, we propose a novel model that exploits the probabilistic properties of our rule sets, with the intuition of only allowing rules to overlap if they have similar probabilistic outputs. We next formalize the problem of learning a TURS model based on the MDL principle and develop a carefully designed heuristic algorithm. We benchmark against a wide range of rule-based methods and demonstrate that our method learns rule sets that have lower model complexity and highly competitive predictive performance. In addition, we empirically show that rules in our model are empirically ``independent" and hence truly unordered.

    Comment: Submitted to JMLR
    Keywords Computer Science - Machine Learning
    Subject code 006
    Publishing date 2024-01-18
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: BioMonitor session at the EU Bioeconomy Conference, Brussel

    van Leeuwen, Myrna

    2022  

    Keywords Life Science
    Publishing country nl
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Circular@WUR conference "Living within planetary boundaries"

    van Leeuwen, Myrna

    2022  

    Keywords Life Science
    Publishing country nl
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: Explainable Contextual Anomaly Detection using Quantile Regression Forests

    Li, Zhong / van Leeuwen, Matthijs

    2023  

    Abstract: Traditional anomaly detection methods aim to identify objects that deviate from most other objects by treating all features equally. In contrast, contextual anomaly detection methods aim to detect objects that deviate from other objects within a context ... ...

    Abstract Traditional anomaly detection methods aim to identify objects that deviate from most other objects by treating all features equally. In contrast, contextual anomaly detection methods aim to detect objects that deviate from other objects within a context of similar objects by dividing the features into contextual features and behavioral features. In this paper, we develop connections between dependency-based traditional anomaly detection methods and contextual anomaly detection methods. Based on resulting insights, we propose a novel approach to inherently interpretable contextual anomaly detection that uses Quantile Regression Forests to model dependencies between features. Extensive experiments on various synthetic and real-world datasets demonstrate that our method outperforms state-of-the-art anomaly detection methods in identifying contextual anomalies in terms of accuracy and interpretability.

    Comment: Manuscript submitted to Data Mining and Knowledge Discovery in October 2022 for possible publication. This is the revised version submitted in April 2023
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
    Subject code 006
    Publishing date 2023-02-22
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: 2021 EU Conference on modelling for policy support (online)

    van Leeuwen, Myrna

    2021  

    Keywords Life Science
    Publishing country nl
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Explainable haemoglobin deferral predictions using machine learning models: Interpretation and consequences for the blood supply.

    Vinkenoog, Marieke / van Leeuwen, Matthijs / Janssen, Mart P

    Vox sanguinis

    2022  Volume 117, Issue 11, Page(s) 1262–1270

    Abstract: Background and objectives: Accurate predictions of haemoglobin (Hb) deferral for whole-blood donors could aid blood banks in reducing deferral rates and increasing efficiency and donor motivation. Complex models are needed to make accurate predictions, ... ...

    Abstract Background and objectives: Accurate predictions of haemoglobin (Hb) deferral for whole-blood donors could aid blood banks in reducing deferral rates and increasing efficiency and donor motivation. Complex models are needed to make accurate predictions, but predictions must also be explainable. Before the implementation of a prediction model, its impact on the blood supply should be estimated to avoid shortages.
    Materials and methods: Donation visits between October 2017 and December 2021 were selected from Sanquin's database system. The following variables were available for each visit: donor sex, age, donation start time, month, number of donations in the last 24 months, most recent ferritin level, days since last ferritin measurement, Hb at nth previous visit (n between 1 and 5), days since the nth previous visit. Outcome Hb deferral has two classes: deferred and not deferred. Support vector machines were used as prediction models, and SHapley Additive exPlanations values were used to quantify the contribution of each variable to the model predictions. Performance was assessed using precision and recall. The potential impact on blood supply was estimated by predicting deferral at earlier or later donation dates.
    Results: We present a model that predicts Hb deferral in an explainable way. If used in practice, 64% of non-deferred donors would be invited on or before their original donation date, while 80% of deferred donors would be invited later.
    Conclusion: By using this model to invite donors, the number of blood bank visits would increase by 15%, while deferral rates would decrease by 60% (currently 3% for women and 1% for men).
    MeSH term(s) Male ; Humans ; Female ; Child, Preschool ; Hemoglobins/analysis ; Blood Donors ; Blood Banks ; Machine Learning ; Ferritins
    Chemical Substances Hemoglobins ; Ferritins (9007-73-2)
    Language English
    Publishing date 2022-09-14
    Publishing country England
    Document type Journal Article
    ZDB-ID 80313-3
    ISSN 1423-0410 ; 0042-9007
    ISSN (online) 1423-0410
    ISSN 0042-9007
    DOI 10.1111/vox.13350
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 74: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  9. Book ; Online: Truly Unordered Probabilistic Rule Sets for Multi-class Classification

    Yang, Lincen / van Leeuwen, Matthijs

    2022  

    Abstract: Rule set learning has long been studied and has recently been frequently revisited due to the need for interpretable models. Still, existing methods have several shortcomings: 1) most recent methods require a binary feature matrix as input, while ... ...

    Abstract Rule set learning has long been studied and has recently been frequently revisited due to the need for interpretable models. Still, existing methods have several shortcomings: 1) most recent methods require a binary feature matrix as input, while learning rules directly from numeric variables is understudied; 2) existing methods impose orders among rules, either explicitly or implicitly, which harms interpretability; and 3) currently no method exists for learning probabilistic rule sets for multi-class target variables (there is only one for probabilistic rule lists). We propose TURS, for Truly Unordered Rule Sets, which addresses these shortcomings. We first formalize the problem of learning truly unordered rule sets. To resolve conflicts caused by overlapping rules, i.e., instances covered by multiple rules, we propose a novel approach that exploits the probabilistic properties of our rule sets. We next develop a two-phase heuristic algorithm that learns rule sets by carefully growing rules. An important innovation is that we use a surrogate score to take the global potential of the rule set into account when learning a local rule. Finally, we empirically demonstrate that, compared to non-probabilistic and (explicitly or implicitly) ordered state-of-the-art methods, our method learns rule sets that not only have better interpretability but also better predictive performance.

    Comment: Camera ready version for ECMLPKDD 2022, with Supplementary Materials
    Keywords Computer Science - Machine Learning
    Subject code 006
    Publishing date 2022-06-17
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Book ; Online: Feature Selection for Fault Detection and Prediction based on Event Log Analysis

    Li, Zhong / van Leeuwen, Matthijs

    2022  

    Abstract: Event logs are widely used for anomaly detection and prediction in complex systems. Existing log-based anomaly detection methods usually consist of four main steps: log collection, log parsing, feature extraction, and anomaly detection, wherein the ... ...

    Abstract Event logs are widely used for anomaly detection and prediction in complex systems. Existing log-based anomaly detection methods usually consist of four main steps: log collection, log parsing, feature extraction, and anomaly detection, wherein the feature extraction step extracts useful features for anomaly detection by counting log events. For a complex system, such as a lithography machine consisting of a large number of subsystems, its log may contain thousands of different events, resulting in abounding extracted features. However, when anomaly detection is performed at the subsystem level, analyzing all features becomes expensive and unnecessary. To mitigate this problem, we develop a feature selection method for log-based anomaly detection and prediction, largely improving the effectiveness and efficiency.

    Comment: This is a work-in-progress paper that was accepted by the AI for Manufacturing Workshop at ECMLPKDD 2022, with oral presentation \& poster
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
    Subject code 004
    Publishing date 2022-08-19
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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