Search

Your search keyword '"Scheepers, Floortje"' showing total 285 results
Start Over Author "Scheepers, Floortje"
285 results on '"Scheepers, Floortje"'

2. Bias Discovery in Machine Learning Models for Mental Health

Author

Mosteiro, Pablo, Kuiper, Jesse, Masthoff, Judith, Scheepers, Floortje, and Spruit, Marco

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computers and Society
Abstract

Fairness and bias are crucial concepts in artificial intelligence, yet they are relatively ignored in machine learning applications in clinical psychiatry. We computed fairness metrics and present bias mitigation strategies using a model trained on clinical mental health data. We collected structured data related to the admission, diagnosis, and treatment of patients in the psychiatry department of the University Medical Center Utrecht. We trained a machine learning model to predict future administrations of benzodiazepines on the basis of past data. We found that gender plays an unexpected role in the predictions-this constitutes bias. Using the AI Fairness 360 package, we implemented reweighing and discrimination-aware regularization as bias mitigation strategies, and we explored their implications for model performance. This is the first application of bias exploration and mitigation in a machine learning model trained on real clinical psychiatry data.

Published
2022
Full Text
View/download PDF

3. Federated learning for violence incident prediction in a simulated cross-institutional psychiatric setting

Author

Borger, Thomas, Mosteiro, Pablo, Kaya, Heysem, Rijcken, Emil, Salah, Albert Ali, Scheepers, Floortje, and Spruit, Marco

Subjects
Computer Science - Computation and Language, Computer Science - Computers and Society, Computer Science - Machine Learning
Abstract

Inpatient violence is a common and severe problem within psychiatry. Knowing who might become violent can influence staffing levels and mitigate severity. Predictive machine learning models can assess each patient's likelihood of becoming violent based on clinical notes. Yet, while machine learning models benefit from having more data, data availability is limited as hospitals typically do not share their data for privacy preservation. Federated Learning (FL) can overcome the problem of data limitation by training models in a decentralised manner, without disclosing data between collaborators. However, although several FL approaches exist, none of these train Natural Language Processing models on clinical notes. In this work, we investigate the application of Federated Learning to clinical Natural Language Processing, applied to the task of Violence Risk Assessment by simulating a cross-institutional psychiatric setting. We train and compare four models: two local models, a federated model and a data-centralised model. Our results indicate that the federated model outperforms the local models and has similar performance as the data-centralised model. These findings suggest that Federated Learning can be used successfully in a cross-institutional setting and is a step towards new applications of Federated Learning based on clinical notes

Published
2022
Full Text
View/download PDF

4. Making sense of violence risk predictions using clinical notes

Author

Mosteiro, Pablo, Rijcken, Emil, Zervanou, Kalliopi, Kaymak, Uzay, Scheepers, Floortje, and Spruit, Marco

Subjects
Computer Science - Machine Learning, Computer Science - Computation and Language, Computer Science - Computers and Society
Abstract

Violence risk assessment in psychiatric institutions enables interventions to avoid violence incidents. Clinical notes written by practitioners and available in electronic health records (EHR) are valuable resources that are seldom used to their full potential. Previous studies have attempted to assess violence risk in psychiatric patients using such notes, with acceptable performance. However, they do not explain why classification works and how it can be improved. We explore two methods to better understand the quality of a classifier in the context of clinical note analysis: random forests using topic models, and choice of evaluation metric. These methods allow us to understand both our data and our methodology more profoundly, setting up the groundwork to work on improved models that build upon this understanding. This is particularly important when it comes to the generalizability of evaluated classifiers to new data, a trustworthiness problem that is of great interest due to the increased availability of new data in electronic format., Comment: arXiv admin note: substantial text overlap with arXiv:2204.13535

Published
2022
Full Text
View/download PDF

5. Machine Learning for Violence Risk Assessment Using Dutch Clinical Notes

Author

Mosteiro, Pablo, Rijcken, Emil, Zervanou, Kalliopi, Kaymak, Uzay, Scheepers, Floortje, and Spruit, Marco

Subjects
Computer Science - Machine Learning, Computer Science - Computation and Language, Computer Science - Computers and Society
Abstract

Violence risk assessment in psychiatric institutions enables interventions to avoid violence incidents. Clinical notes written by practitioners and available in electronic health records are valuable resources capturing unique information, but are seldom used to their full potential. We explore conventional and deep machine learning methods to assess violence risk in psychiatric patients using practitioner notes. The performance of our best models is comparable to the currently used questionnaire-based method, with an area under the Receiver Operating Characteristic curve of approximately 0.8. We find that the deep-learning model BERTje performs worse than conventional machine learning methods. We also evaluate our data and our classifiers to understand the performance of our models better. This is particularly important for the applicability of evaluated classifiers to new data, and is also of great interest to practitioners, due to the increased availability of new data in electronic format.

Published
2022
Full Text
View/download PDF

10. Wearables and location tracking technologies for mental-state sensing in outdoor environments

Author

Birenboim, Amit, Dijst, Martin, Scheepers, Floortje, Poelman, Maartje, and Helbich, Marco

Subjects
Statistics - Applications
Abstract

Advances in commercial wearable devices are increasingly facilitating the collection and analysis of everyday physiological data. This paper discusses the theoretical and practical aspects of using such ambulatory devices for the detection of episodic changes in physiological signals as a marker for mental state in outdoor environments. A pilot study was conducted to evaluate the feasibility of utilizing commercial wearables in combination with location tracking technologies. The study measured physiological signals for 15 participants, including heart rate, heart-rate variability, and skin conductance. Participants' signals were recorded during an outdoor walk that was tracked using a GPS logger. The walk was designed to pass through various types of environments including green, blue, and urban spaces as well as a more stressful road crossing. The data that was obtained was used to demonstrate how biosensors information can be contextualized and enriched using location information. Significant episodic changes in physiological signals under real-world conditions were detectable in the stressful road crossing, but not in the other types of environments. The article concludes that despite challenges and limitations of current off-the-shelf wearables, the utilization of these devices offers novel opportunities for evaluating episodic changes in physiological signals as a marker for mental state during everyday activities including in outdoor environments.

Published
2019
Full Text
View/download PDF

15. Making Sense of Violence Risk Predictions Using Clinical Notes

Author

Mosteiro, Pablo, Rijcken, Emil, Zervanou, Kalliopi, Kaymak, Uzay, Scheepers, Floortje, Spruit, Marco, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huang, Zhisheng, editor, Siuly, Siuly, editor, Wang, Hua, editor, Zhou, Rui, editor, and Zhang, Yanchun, editor

Published
2020
Full Text
View/download PDF

18. Childhood abuse v. neglect and risk for major psychiatric disorders

Author

AIOS Psychiatrie, Brain, Onderzoeksgroep 2, Onderzoeksgroep 11, Onderzoek, Psychiatrie_Medisch, Hart- en Vaatziekten Team A, Genetica, UMC Utrecht, Hersenen-Medisch 1, Alkema, Anne, Marchi, Mattia, Van Der Zaag, Jeroen A.J., Van Der Sluis, Daniëlle, Warrier, Varun, Ophoff, Roel A., Kahn, René S., Cahn, Wiepke, Hovens, Jacqueline G.F.M., Riese, Harriëtte, Scheepers, Floortje, Penninx, Brenda W.J.H., Cecil, Charlotte, Oldehinkel, Albertine J., Vinkers, Christiaan H., Boks, Marco P.M., Genetic Risk and Outcome of Psychosis (GROUP) Investigators, AIOS Psychiatrie, Brain, Onderzoeksgroep 2, Onderzoeksgroep 11, Onderzoek, Psychiatrie_Medisch, Hart- en Vaatziekten Team A, Genetica, UMC Utrecht, Hersenen-Medisch 1, Alkema, Anne, Marchi, Mattia, Van Der Zaag, Jeroen A.J., Van Der Sluis, Daniëlle, Warrier, Varun, Ophoff, Roel A., Kahn, René S., Cahn, Wiepke, Hovens, Jacqueline G.F.M., Riese, Harriëtte, Scheepers, Floortje, Penninx, Brenda W.J.H., Cecil, Charlotte, Oldehinkel, Albertine J., Vinkers, Christiaan H., Boks, Marco P.M., and Genetic Risk and Outcome of Psychosis (GROUP) Investigators

Published
2024

19. Muva physical activity intervention improves social functioning in people with a severe mental illness: A pragmatic stepped wedge cluster controlled trial

Author

AIOS Psychiatrie, UMC Utrecht Holding, Affectieve & Psychotische Med., Brain, Onderzoek, Psychiatrie_Medisch, Maria Koomen, Lisanne Elisabeth, Theodora van de Meent, Ilona Hendrika, Deenik, Jeroen, van Dellen, Edwin, Schnack, Hugo Gerard, Hendrikus van Werkhoven, Cornelis, Swildens, Wilma Elisabeth, van Meijel, Berno, Staal, Wouter, Jörg, Frederike, Scheepers, Floortje, Cahn, Wiepke, AIOS Psychiatrie, UMC Utrecht Holding, Affectieve & Psychotische Med., Brain, Onderzoek, Psychiatrie_Medisch, Maria Koomen, Lisanne Elisabeth, Theodora van de Meent, Ilona Hendrika, Deenik, Jeroen, van Dellen, Edwin, Schnack, Hugo Gerard, Hendrikus van Werkhoven, Cornelis, Swildens, Wilma Elisabeth, van Meijel, Berno, Staal, Wouter, Jörg, Frederike, Scheepers, Floortje, and Cahn, Wiepke

Published
2024

22. Defining the concept of mental dysregulation in patients requiring ambulance and/or emergency department care: protocol for a Delphi consensus study

Author

Onderzoeksgroep 10, UMC Utrecht Holding, Verplegingswetenschap, Cancer, JC onderzoeksprogramma Methodologie, Psychiatrie_Medisch, Brain, Van De Glind, Geurt, Crilly, Julia, Galenkamp, Niek, Schut, Bart, Werner, Lente, Chan, Eric, Hilton, Emily, Schoonhoven, Lisette, Scheepers, Floortje E., Muir, Rachel, Baden, David, Van Veen, Mark, Ham, Wietske H.W., Onderzoeksgroep 10, UMC Utrecht Holding, Verplegingswetenschap, Cancer, JC onderzoeksprogramma Methodologie, Psychiatrie_Medisch, Brain, Van De Glind, Geurt, Crilly, Julia, Galenkamp, Niek, Schut, Bart, Werner, Lente, Chan, Eric, Hilton, Emily, Schoonhoven, Lisette, Scheepers, Floortje E., Muir, Rachel, Baden, David, Van Veen, Mark, and Ham, Wietske H.W.

Published
2024

23. Childhood abuse v. neglect and risk for major psychiatric disorders.

Author

Alkema, Anne, Marchi, Mattia, van der Zaag, Jeroen A. J., van der Sluis, Daniëlle, Warrier, Varun, Alizadeh, Behrooz Z., van Amelsvoort, Therese, Cahn, Wiepke, de Haan, Lieuwe, Schirmbeck, Frederike, Simons, Claudia J. P., van Os, Jim, Veling, Wim, Ophoff, Roel A., Kahn, René S., Hovens, Jacqueline G. F. M., Riese, Harriëtte, Scheepers, Floortje, Penninx, Brenda W. J. H., and Cecil, Charlotte

Subjects
SUICIDE risk factors, MENTAL illness risk factors, SCHIZOPHRENIA risk factors, MENTAL depression risk factors, RISK assessment, BIPOLAR disorder, RESEARCH funding, CHILD abuse, MENTAL illness, DESCRIPTIVE statistics, AGITATION (Psychology), ODDS ratio, HALLUCINATIONS, ATTRIBUTION (Social psychology), CONFIDENCE intervals, REGRESSION analysis, SLEEP disorders, DISEASE risk factors
Abstract

Background: Childhood maltreatment (CM) is a strong risk factor for psychiatric disorders but serves in its current definitions as an umbrella for various fundamentally different childhood experiences. As first step toward a more refined analysis of the impact of CM, our objective is to revisit the relation of abuse and neglect, major subtypes of CM, with symptoms across disorders. Methods: Three longitudinal studies of major depressive disorder (MDD, N = 1240), bipolar disorder (BD, N = 1339), and schizophrenia (SCZ, N = 577), each including controls (N = 881), were analyzed. Multivariate regression models were used to examine the relation between exposure to abuse, neglect, or their combination to the odds for MDD, BD, SCZ, and symptoms across disorders. Bidirectional Mendelian randomization (MR) was used to probe causality, using genetic instruments of abuse and neglect derived from UK Biobank data (N = 143 473). Results: Abuse was the stronger risk factor for SCZ (OR 3.51, 95% CI 2.17–5.67) and neglect for BD (OR 2.69, 95% CI 2.09–3.46). Combined CM was related to increased risk exceeding additive effects of abuse and neglect for MDD (RERI = 1.4) and BD (RERI = 1.1). Across disorders, abuse was associated with hallucinations (OR 2.16, 95% CI 1.55–3.01) and suicide attempts (OR 2.16, 95% CI 1.55–3.01) whereas neglect was associated with agitation (OR 1.24, 95% CI 1.02–1.51) and reduced need for sleep (OR 1.64, 95% CI 1.08–2.48). MR analyses were consistent with a bidirectional causal effect of abuse with SCZ (IVWforward = 0.13, 95% CI 0.01–0.24). Conclusions: Childhood abuse and neglect are associated with different risks to psychiatric symptoms and disorders. Unraveling the origin of these differences may advance understanding of disease etiology and ultimately facilitate development of improved personalized treatment strategies. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

25. Defining the concept of mental dysregulation in patients requiring ambulance and/or emergency department care: protocol for a Delphi consensus study

Author

Van de Glind, Geurt, primary, Crilly, Julia, additional, Galenkamp, Niek, additional, Schut, Bart, additional, Werner, Lente, additional, Chan, Eric, additional, Hilton, Emily, additional, Schoonhoven, Lisette, additional, Scheepers, Floortje E, additional, Muir, Rachel, additional, Baden, David, additional, van Veen, Mark, additional, and Ham, Wietske H W, additional

Published
2024
Full Text
View/download PDF

29. Interventions to reduce interpersonal stigma towards patients with a mental dysregulation for ambulance and emergency department healthcare professionals: review protocol for an integrative review

Author

Van de Glind, Geurt, primary, Galenkamp, Niek, additional, Bleijenberg, Nienke, additional, Schoonhoven, Lisette, additional, Scheepers, Floortje E, additional, Crilly, Julia, additional, van Veen, Mark, additional, and Ham, Wietske H W, additional

Published
2023
Full Text
View/download PDF

33. Feasibility, validity and reliability of the Dutch translation of INCRESE (INCRESE-NL) inventory to characterize mental health recovery narratives.

Author

Lases, Mitch N., Bruins, Jojanneke, Ng, Fiona, Rennick-Egglestone, Stefan, Llewellyn-Beardsley, Joy, Slade, Mike, van Sambeek, Nienke, Scheepers, Floortje, van Balkom, Ingrid D. C., and Castelein, Stynke

Subjects
SCALE analysis (Psychology), MENTAL health, RESEARCH methodology evaluation, RESEARCH evaluation, JUDGMENT sampling, DESCRIPTIVE statistics, CONVALESCENCE, STORYTELLING, RESEARCH methodology, STATISTICAL reliability, MEDICAL coding, DATA analysis software
Abstract

Objective: Methods for characterizing mental health recovery narratives are needed to enable the selection of helpful narratives which can support the recovery processes of other people. The Inventory of Characteristics of Recovery Stories (INCRESE) is an existing English-language tool to characterize recovery narratives, which is not available in Dutch. The aims of this study were to create a Dutch translation of INCRESE and to evaluate the feasibility of sustained and routine use, as well as its validity and reliability. Research design and methods: INCRESE was translated into Dutch (INCRESE-NL) using an established translation methodology. Six coders with different professional backgrounds rated 30 purposively selected Dutch narratives using INCRESE-NL and then completed an evaluation survey. Feasibility was assessed qualitatively and content validity both qualitatively and quantitatively. Gwet's AC1 agreement coefficient was used to calculate intercoder reliability. Results: Coders evaluated INCRESE-NL as a suitable instrument to capture important characteristics of recovery narratives with an acceptable administrative burden. The content validity index was sufficient for 5/7 sections of INCRESE-NL. Most items were found to be clear and suitable for capturing the corresponding characteristic, although some missed strict coding rules or clear descriptions, especially in section 4 on narrative characteristics. Gwet's AC1 could be calculated for 67/77 items, of which 62 scored sufficient and five scored below threshold. Gwet's AC1 coefficients corresponded well with the original INCRESE Fleiss Kappa values. Conclusions: INCRESE-NL has sufficient feasibility, validity, and reliability and can be used to characterize Dutch recovery narratives. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

36. Effect of Calculating Pointwise Mutual Information using a Fuzzy Sliding Window in Topic Modeling

Author

Rijcken, Emil, Zervanou, Kalliopi, Spruit, Marco, Scheepers, Floortje, Kaymak, Uzay, Rijcken, Emil, Zervanou, Kalliopi, Spruit, Marco, Scheepers, Floortje, and Kaymak, Uzay

Abstract

Topic modeling is a popular method for analysing large amounts of unstructured text data and extracting meaningful insights. The coherence of the generated topics is a critical metric for determining the model quality and measuring the semantic relatedness of the words in a topic. The distributional hypothesis, a fundamental theory in linguistics, states that words occurring in the same contexts tend to have similar meanings. Based on this theory, word co-occurrence in a given context is often used to reflect word association in coherence scores. To this end, many coherence scores use Normalised Pointwise Mutual Information (NPMI), which uses a sliding window to describe the neighbourhood that defines the context. It is assumed that there is no other structure in the neighbourhood except for the presence of words. Inspired by the distributional hypothesis, we hypothesise the word distance to be relevant for determining the word association. Hence, we propose using a fuzzy sliding window to define a neighbourhood in which the association between words depends on the membership of the words in the fuzzy sliding window. To this end, we propose Fuzzy Normalized Pointwise Mutual Information (FNPMI) to calculate fuzzy coherence scores. We implement two different neighbourhood structures by the definition of the membership function of the sliding window. In the first implementation, the association between two words correlates positively with the distance, whereas the correlation is negative in the second. We compare the correlation of our proposed new coherence metrics with human judgment. We find that the use of a fuzzy sliding window correlates less with human judgment than a crisp sliding window. This finding indicates that word distance within a window is less important than defining the window size itself.

Published
2023

37. Towards Interpreting Topic Models with ChatGPT

Author

Rijcken, Emil, Scheepers, Floortje, Zervanou, Kalliopi, Spruit, Marco, Mosteiro Romero, Pablo, Kaymak, Uzay, Rijcken, Emil, Scheepers, Floortje, Zervanou, Kalliopi, Spruit, Marco, Mosteiro Romero, Pablo, and Kaymak, Uzay

Abstract

Topic modeling has become a popular approach to identify semantic structures in text corpora. Despite its wide applications, interpreting the outputs of topic models remains challenging. This paper presents an initial study regarding a new approach to better understand this output, leveraging the large language model ChatGPT. Our approach is built on a three- stage process where we first use topic modeling to identify the main topics in the corpus. Then, we ask a domain expert to assign themes to these topics and prompt ChatGPT to generate human- readable summaries of the topics. Lastly, we compare the human- and machine-produced interpretations. The domain expert found half of ChatGPT’s descriptions useful. This explorative work demonstrates ChatGPT’s capability to describe topics accurately and provide useful insights if prompted accurately.

Published
2023

39. Towards Interpreting Topic Models with ChatGPT

Author

Natural Language Processing, Sub Natural Language Processing, Methodology and statistics for the behavioural and social sciences, Leerstoel Schoot, Rijcken, Emil, Scheepers, Floortje, Zervanou, Kalliopi, Spruit, Marco, Mosteiro Romero, Pablo, Kaymak, Uzay, Natural Language Processing, Sub Natural Language Processing, Methodology and statistics for the behavioural and social sciences, Leerstoel Schoot, Rijcken, Emil, Scheepers, Floortje, Zervanou, Kalliopi, Spruit, Marco, Mosteiro Romero, Pablo, and Kaymak, Uzay

Published
2023

40. Childhood abuse v. neglect and risk for major psychiatric disorders

Author

Alkema, Anne, Marchi, Mattia, Van Der Zaag, Jeroen A.J., Van Der Sluis, Daniëlle, Warrier, Varun, Ophoff, Roel A., Kahn, René S., Cahn, Wiepke, Hovens, Jacqueline G.F.M., Riese, Harriëtte, Scheepers, Floortje, Penninx, Brenda W.J.H., Cecil, Charlotte, Oldehinkel, Albertine J., Vinkers, Christiaan H., Boks, Marco P.M., Alizadeh, Behrooz Z., Van Amelsvoort, Therese, De Haane, Lieuwe, Schirmbeck, Frederike, Simons, Claudia J.P., Van Os, Jim, Veling, Wim, Alkema, Anne, Marchi, Mattia, Van Der Zaag, Jeroen A.J., Van Der Sluis, Daniëlle, Warrier, Varun, Ophoff, Roel A., Kahn, René S., Cahn, Wiepke, Hovens, Jacqueline G.F.M., Riese, Harriëtte, Scheepers, Floortje, Penninx, Brenda W.J.H., Cecil, Charlotte, Oldehinkel, Albertine J., Vinkers, Christiaan H., Boks, Marco P.M., Alizadeh, Behrooz Z., Van Amelsvoort, Therese, De Haane, Lieuwe, Schirmbeck, Frederike, Simons, Claudia J.P., Van Os, Jim, and Veling, Wim

Abstract

Background:Childhood maltreatment (CM) is a strong risk factor for psychiatric disorders but serves in its current definitions as an umbrella for various fundamentally different childhood experiences. As first step toward a more refined analysis of the impact of CM, our objective is to revisit the relation of abuse and neglect, major subtypes of CM, with symptoms across disorders. Methods:Three longitudinal studies of major depressive disorder (MDD, N = 1240), bipolar disorder (BD, N = 1339), and schizophrenia (SCZ, N = 577), each including controls (N = 881), were analyzed. Multivariate regression models were used to examine the relation between exposure to abuse, neglect, or their combination to the odds for MDD, BD, SCZ, and symptoms across disorders. Bidirectional Mendelian randomization (MR) was used to probe causality, using genetic instruments of abuse and neglect derived from UK Biobank data (N = 143 473). Results:Abuse was the stronger risk factor for SCZ (OR 3.51, 95% CI 2.17-5.67) and neglect for BD (OR 2.69, 95% CI 2.09-3.46). Combined CM was related to increased risk exceeding additive effects of abuse and neglect for MDD (RERI = 1.4) and BD (RERI = 1.1). Across disorders, abuse was associated with hallucinations (OR 2.16, 95% CI 1.55-3.01) and suicide attempts (OR 2.16, 95% CI 1.55-3.01) whereas neglect was associated with agitation (OR 1.24, 95% CI 1.02-1.51) and reduced need for sleep (OR 1.64, 95% CI 1.08-2.48). MR analyses were consistent with a bidirectional causal effect of abuse with SCZ (IVWforward = 0.13, 95% CI 0.01-0.24). Conclusion:Childhood abuse and neglect are associated with different risks to psychiatric symptoms and disorders. Unraveling the origin of these differences may advance understanding of disease etiology and ultimately facilitate development of improved personalized treatment strategies.

Published
2023

42. Making meaning of trauma in psychosis

Author

Onderzoek, Geneeskunde, Onderwijscentrum Organisatie, Onderwijscentrum Onderwijs, Psychiatrie_Medisch, Brain, van Sambeek, Nienke, Franssen, Gaston, van Geelen, Stefan, Scheepers, Floortje, Onderzoek, Geneeskunde, Onderwijscentrum Organisatie, Onderwijscentrum Onderwijs, Psychiatrie_Medisch, Brain, van Sambeek, Nienke, Franssen, Gaston, van Geelen, Stefan, and Scheepers, Floortje

Published
2023

43. Interventions to reduce interpersonal stigma towards patients with a mental dysregulation for ambulance and emergency department healthcare professionals: review protocol for an integrative review

Author

Verplegingswetenschap, Infection & Immunity, Cancer, JC onderzoeksprogramma Methodology, Psychiatrie_Medisch, Brain, Van de Glind, Geurt, Galenkamp, Niek, Bleijenberg, Nienke, Schoonhoven, Lisette, Scheepers, Floortje E., Crilly, Julia, van Veen, Mark, Ham, Wietske H.W., Verplegingswetenschap, Infection & Immunity, Cancer, JC onderzoeksprogramma Methodology, Psychiatrie_Medisch, Brain, Van de Glind, Geurt, Galenkamp, Niek, Bleijenberg, Nienke, Schoonhoven, Lisette, Scheepers, Floortje E., Crilly, Julia, van Veen, Mark, and Ham, Wietske H.W.

Published
2023

44. Outcome prediction of electroconvulsive therapy for depression

Author

Psychiatrie_Medisch, Brain, Affectieve & Psychotische Med., van der Does, Yuri, Turner, Rosanne J., Bartels, Miel J.H., Hagoort, Karin, Metselaar, Aäron, Scheepers, Floortje, Grünwald, Peter D., Somers, Metten, van Dellen, Edwin, Psychiatrie_Medisch, Brain, Affectieve & Psychotische Med., van der Does, Yuri, Turner, Rosanne J., Bartels, Miel J.H., Hagoort, Karin, Metselaar, Aäron, Scheepers, Floortje, Grünwald, Peter D., Somers, Metten, and van Dellen, Edwin

Published
2023

45. Bayesian network analysis of antidepressant treatment trajectories

Author

Innovatie, Psychiatrie_Medisch, Brain, Turner, Rosanne J., Hagoort, Karin, Meijer, Rosa J., Coenen, Femke, Scheepers, Floortje E., Innovatie, Psychiatrie_Medisch, Brain, Turner, Rosanne J., Hagoort, Karin, Meijer, Rosa J., Coenen, Femke, and Scheepers, Floortje E.

Published
2023

48. The importance of informal support and a listening ear: A narrative analysis of emotional conflict and support needs of siblings of children with a psychiatric disorder.

Author

Veldhuizen, Elien E, Franssen, Gaston, Schothorst, Patricia F, and Scheepers, Floortje E

Subjects
SIBLINGS, SOCIAL support, SELF-evaluation, INTERVIEWING, CONFLICT (Psychology), QUALITATIVE research, PSYCHOSOCIAL factors, EMOTIONS, LISTENING, THEMATIC analysis, MENTAL illness, CHILDREN
Abstract

This study investigates the self-reported impact of children's psychiatric disorders on their siblings and assesses what forms of support such children most value. We used a qualitative research design with open interviews to stimulate children between 8 and 15 years old to talk about their experiences living with a brother or sister with a psychiatric disorder. Their stories were analysed within a hermeneutic phenomenological framework in order to identify narrative themes and interpret the meaning of shared experiences. From our analysis, nine shared narrative themes emerge. Overall, siblings report feeling conflicted about adapting their lives to their brother's or sister's disorder and signal a need for personalized attention from parents. They also indicate that being involved in the care for their brother or sister helps them to better understand their behaviour. Finally, siblings reveal that, in their experience, formal, protocolized forms of support foreground family problems and stress. Thus, we recommend to involve children in the care process; to acknowledge their personal needs and conflicts; and to be mindful of the style of support: help offered in an informal or playful way, instead of formal and protocolized, could be a more effective way of meeting siblings' needs. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

50. Exploring Embedding Spaces for more Coherent Topic Modeling in Electronic Health Records

Author

Rijcken, Emil, Zervanou, Kalliopi, Spruit, Marco, Mosteiro, Pablo, Scheepers, Floortje, Kaymak, Uzay, and JADS Research

Subjects
Psychiatry, Fuzzy Clustering, SDG 3 - Good Health and Well-being, Topic Modeling, Word Embeddings, Electronic Health Records, Neural Network methods, Fuzzy Methods, SDG 3 – Goede gezondheid en welzijn, Natural Language Processing
Abstract

The written notes in the Electronic Health Records contain a vast amount of information about patients. Implementing automated approaches for text classification tasks requires the automated methods to be well-interpretable, and topic models can be used for this goal as they can indicate what topics in a text are relevant to making a decision. We propose a new topic modeling algorithm, FLSA-E, and compare it with another state-of-the-art algorithm FLSA-W. In FLSA-E, topics are found by fuzzy clustering in a word embedding space. Since we use word embeddings as the basis for our clustering, we extend our evaluation with word-embeddings-based evaluation metrics. We find that different evaluation metrics favour different algorithms. Based on the results, there is evidence that FLSA-E has fewer outliers in its topics, a desirable property, given that within-topic words need to be semantically related.

Published
2022

Catalog

Books, media, physical & digital resources
See catalog results