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ICT research methods for machine learning engineering

The current set of research methods on ictresearchmethods.nl contains only one research method that refers to machine learning: the “Data analytics” method in the “Lab” strategy. This does not reflect the way of working in ML projects, where Data Analytics is not a method to answer one question but the main goal of the project. For ML projects, the Data Analytics method should be divided in several smaller steps, each becoming a method of its own. In other words, we should treat the Data Analytics (or more appropriate ML engineering) process in the same way the software engineering process is treated in the framework. In the remainder of this post I will briefly discuss each of the existing research methods and how they apply to ML projects. The methods are organized by strategy. In the discussion I will give pointers to relevant tools or literature for ML projects.

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Acceleration of direct identification of S.aureus versus Coagulase Negative Staphylococci from blood culture material: a comparison of six bacterial DNA extraction methods

To accelerate differentiation between Staphylococcus aureus and Coagulase Negative Staphylococci (CNS), this study aimed to compare six different DNA extraction methods from 2 commonly used blood culture materials, i.e. BACTEC and Bact/ALERT. Furthermore, we analyzed the effect of reduced blood culture times for detection of Staphylococci directly from blood culture material. A real-time PCR duplex assay was used to compare 6 different DNA isolation protocols on two different blood culture systems. Negative blood culture material was spiked with MRSA. Bacterial DNA was isolated with: automated extractor EasyMAG (3 protocols), automated extractor MagNA Pure LC (LC Microbiology Kit MGrade), a manual kit MolYsis Plus, and a combination between MolYsis Plus and the EasyMAG. The most optimal isolation method was used to evaluate reduced bacterial culture times. Bacterial DNA isolation with the MolYsis Plus kit in combination with the specific B protocol on the EasyMAG resulted in the most sensitive detection of S.aureus, with a detection limit of 10 CFU/ml, in Bact/ALERT material, whereas using BACTEC resulted in a detection limit of 100 CFU/ml. An initial S.aureus load of 1 CFU/ml blood can be detected after 5 hours of culture in Bact/ALERT3D by combining the sensitive isolation method and the tuf LightCycler assay.

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Acceleration of direct identification of S.aureus versus Coagulase Negative Staphylococci from blood culture material: a comparison of six bacterial DNA extraction methods

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Assessing peak aerobic capacity in Dutch law enforcement officers

Objectives: To cross-validate the existing peak rate of oxygen consumption (VO2peak) prediction equations in Dutch law enforcement officers and to determine whether these prediction equations can be used to predict VO2peak for groups and in a single individual. A further objective was to report normative absolute and relative VO2peak values of a sample of law enforcement officers in the Netherlands. Material and Methods: The peak rate of oxygen consumption (ml×kg–1×min–1) was measured using a maximal incremental bicycle test in 1530 subjects, including 1068 male and 461 female police officers. Validity of the prediction equations for groups was assessed by comparing predicted VO2peak with measured VO2peak using paired t-tests. For individual differences limits of agreement (LoA) were calculated. Equations were considered valid for individuals when the difference between measured and predicted VO2peak did not exceed ±1 metabolic equivalent (MET) in 95% of individuals. Results: None of the equations met the validity criterion of 95% of individuals having ±1 MET difference or less than the measured value. Limits of agreement (LoAs) were large in all predictions. At the individual level, none of the equations were valid predictors of VO2peak (ml×kg–1×min–1). Normative values for Dutch law enforcement officers were presented. Conclusions: Substantial differences between measured and predicted VO2peak (ml×kg–1×min–1) were found. Most tested equations were invalid predictors of VO2peak at group level and all were invalid at individual levels.

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Assessing peak aerobic capacity in Dutch law enforcement officers

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Importance analysis of psychosociological variables in frailty syndrome in heart failure patients using machine learning approach

The prevention and diagnosis of frailty syndrome (FS) in cardiac patients requires innovative systems to support medical personnel, patient adherence, and self-care behavior. To do so, modern medicine uses a supervised machine learning approach (ML) to study the psychosocial domains of frailty in cardiac patients with heart failure (HF). This study aimed to determine the absolute and relative diagnostic importance of the individual components of the Tilburg Frailty Indicator (TFI) questionnaire in patients with HF. An exploratory analysis was performed using machine learning algorithms and the permutation method to determine the absolute importance of frailty components in HF. Based on the TFI data, which contain physical and psychosocial components, machine learning models were built based on three algorithms: a decision tree, a random decision forest, and the AdaBoost Models classifier. The absolute weights were used to make pairwise comparisons between the variables and obtain relative diagnostic importance. The analysis of HF patients’ responses showed that the psychological variable TFI20 diagnosing low mood was more diagnostically important than the variables from the physical domain: lack of strength in the hands and physical fatigue. The psychological variable TFI21 linked with agitation and irritability was diagnostically more important than all three physical variables considered: walking difficulties, lack of hand strength, and physical fatigue. In the case of the two remaining variables from the psychological domain (TFI19, TFI22), and for all variables from the social domain, the results do not allow for the rejection of the null hypothesis. From a long-term perspective, the ML based frailty approach can support healthcare professionals, including psychologists and social workers, in drawing their attention to the nonphysical origins of HF.

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Importance analysis of psychosociological variables in frailty syndrome in heart failure patients using machine learning approach

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Machine Learning Methods to Personalize Persuasive Strategies in mHealth Interventions That Promote Physical Activity

Background: Although physical activity (PA) has positive effects on health and well-being, physical inactivity is a worldwide problem. Mobile health interventions have been shown to be effective in promoting PA. Personalizing persuasive strategies improves intervention success and can be conducted using machine learning (ML). For PA, several studies have addressed personalized persuasive strategies without ML, whereas others have included personalization using ML without focusing on persuasive strategies. An overview of studies discussing ML to personalize persuasive strategies in PA-promoting interventions and corresponding categorizations could be helpful for such interventions to be designed in the future but is still missing. Objective: First, we aimed to provide an overview of implemented ML techniques to personalize persuasive strategies in mobile health interventions promoting PA. Moreover, we aimed to present a categorization overview as a starting point for applying ML techniques in this field. Methods: A scoping review was conducted based on the framework by Arksey and O’Malley and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria. Scopus, Web of Science, and PubMed were searched for studies that included ML to personalize persuasive strategies in interventions promoting PA. Papers were screened using the ASReview software. From the included papers, categorized by the research project they belonged to, we extracted data regarding general study information, target group, PA intervention, implemented technology, and study details. On the basis of the analysis of these data, a categorization overview was given. Results: In total, 40 papers belonging to 27 different projects were included. These papers could be categorized in 4 groups based on their dimension of personalization. Then, for each dimension, 1 or 2 persuasive strategy categories were found together with a type of ML. The overview resulted in a categorization consisting of 3 levels: dimension of personalization, persuasive strategy, and type of ML. When personalizing the timing of the messages, most projects implemented reinforcement learning to personalize the timing of reminders and supervised learning (SL) to personalize the timing of feedback, monitoring, and goal-setting messages. Regarding the content of the messages, most projects implemented SL to personalize PA suggestions and feedback or educational messages. For personalizing PA suggestions, SL can be implemented either alone or combined with a recommender system. Finally, reinforcement learning was mostly used to personalize the type of feedback messages. Conclusions: The overview of all implemented persuasive strategies and their corresponding ML methods is insightful for this interdisciplinary field. Moreover, it led to a categorization overview that provides insights into the design and development of personalized persuasive strategies to promote PA. In future papers, the categorization overview might be expanded with additional layers to specify ML methods or additional dimensions of personalization and persuasive strategies.

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Machine Learning Methods to Personalize Persuasive Strategies in mHealth Interventions That Promote Physical Activity

Personen 126

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Alican Noyan

Senior Lecturer in Applied Data Science and AI

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Angelica van Dam

Lecturer

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Alina Lemling

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Aart van der Maas

Onderzoeker

Projecten 501

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‘Function Focused Care’: bewegingsgerichte zorg in het ziekenhuis

Veel ouderen ervaren tijdens en na ziekenhuisopname functieverlies. ‘Function Focused Care in Hospital’, ook wel bekend als bewegingsgerichte zorg, is een interventie gericht op het voorkomen en verminderen van functieverlies bij ouderen tijdens een ziekenhuisopname. Verpleegkundigen moedigen patiënten aan tot actieve betrokkenheid in de dagelijkse zorgmomenten.

Lopend

‘Function Focused Care’: bewegingsgerichte zorg in het ziekenhuis

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360 graden newsroom

In het project werken onderzoekers van het Lectoraat samen met publieke organisaties toe naar een tool waarmee onderstromen in het publieke debat rondom issues eerder kunnen worden opgemerkt. We exploreren met welk algoritme we patronen in geruchtvorming en mobilisatie kunnen opsporen, en tevens hoe we de interactie tussen newsroom-analisten en de output van een monitoring tool het beste kunnen vormgeven.

Afgerond

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4D: Doorgrond Discriminatie Digitale Differentatie

Heb je wel eens gemerkt dat de premie voor je autoverzekering verandert als je in een andere wijk gaat wonen? Verzekeraars berekenen dit met een algoritme, wat kan leiden tot indirecte discriminatie. Dit project onderzoekt hoe zulke digitale differentiatie (DD) zowel eerlijk als rendabel kan.

Lopend

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Activators for change: Moving to Design Based Education.

Om tegemoet te komen aan de eisen die gesteld worden aan werknemers in de huidig snel veranderende samenleving heeft de NHL Stenden Hogeschool gekozen voor een nieuw onderwijsconcept, namelijk Design Based Education (DBE). DBE is gebaseerd op het gedachtegoed van Design Thinking en stimuleert iteratieve en creatieve denkprocessen. DBE is een student-georiënteerde leeromgeving, gebaseerd op praktijk-, dialoog-, en vraaggestuurde onderwijsprincipes en op zelfsturend, constructief, contextueel en samenwerkend leren. Studenten construeren gezamenlijk kennis en ontwikkelen een prototype voor een praktijkvraagstuk. Student-georiënteerde leeromgevingen vragen andere begeleidingsstrategieën van docenten dan zij gewend zijn. Van docenten wordt verwacht dat zij studenten activeren gezamenlijk kennis te construeren en dat zij nauw samenwerken met werkveldprofessionals. Eerder onderzoek toont aan dat docenten, zelfs in een student-georiënteerde leeromgeving, geneigd zijn terug te vallen op conventionele strategieën. De overstap naar een ander onderwijsconcept gaat dus blijkbaar niet vanzelf. Collectief leren stimuleert docenten de dialoog aan te gaan met andere docenten en werkveldprofessionals met als doel gezamenlijk te experimenteren en collectief te handelen. De centrale vraag van het postdoc-onderzoek is het ontwerpen en ontwikkelen van (karakteristieken van) interventies die collectief leren van docenten en werkveldprofessionals stimuleren. Het doel van het postdoconderzoek is om de overstap naar DBE zo probleemloos mogelijk te laten verlopen door docenten te ondersteunen DBE leeromgevingen te ontwikkelen in samenwerking met werkveldprofessionals en DBE te integreren in hun docentactiviteiten. De onderzoeksmethode is Educational Design Research en bestaat uit vier fasen: preliminair onderzoek, ontwikkelen van prototypes, evaluatie en bijdrage aan de praktijk. Het onderzoek is verbonden aan het lectoraat Sustainable Educational Concepts in Higher Education en wordt hiërarchisch en inhoudelijk aangestuurd door de lector. Docenten, experts, werkveldprofessionals en studenten worden betrokken bij het onderzoek. Dit onderzoek kan zowel binnen als buiten de hogeschool een bijdrage leveren omdat steeds meer hogescholen kiezen voor een ander onderwijsconcept.

Afgerond

Partijen 1

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Hogeschool van Amsterdam

Hogeschool

Redactie-artikelen 12

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Door gesloten deuren heen prikken: over bemoeizorg en sociaal werk

redactie

Hoe slim laden een overbelast elektriciteitsnet kan voorkomen

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De toekomst van intelligentie: Stefan Leijnen over impact en ethiek van AI

Producten 412

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ICT research methods for machine learning engineering

LINK

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Acceleration of direct identification of S.aureus versus Coagulase Negative Staphylococci from blood culture material: a comparison of six bacterial DNA extraction methods

DOCUMENT

product

Assessing peak aerobic capacity in Dutch law enforcement officers

DOCUMENT

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Importance analysis of psychosociological variables in frailty syndrome in heart failure patients using machine learning approach

DOCUMENT

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Machine Learning Methods to Personalize Persuasive Strategies in mHealth Interventions That Promote Physical Activity

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