Unimodal vs. Multimodal Prediction of Antenatal Depression from Smartphone-based Survey Data in a Longitudinal Study

Zhong, Mengyu; Zoest, Vera van; Bilal, Ayesha Mae; Papadopoulos, Fotios C.; Castellano, Ginevra

doi:10.1145/3536221.3556605

Cited by 4 publications

(5 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our scenario is grounded in previous studies exploring PND prediction through mobile phone data (Zhong et al, 2022), and social robotics in PND (Zhong et al, 2021;Tanqueray et al, 2022). Ultimately, the purpose of the robot is to encourage those predicted to be most at risk of PND to accept a follow-up appointment with a specialist clinician.…”

Section: Scenariomentioning

confidence: 99%

“…This data can be used to predict the future occurrence of PND. For instance, in late pregnancy (Zhong et al, 2022), or after childbirth (Bilal et al, 2022).…”

Section: Scenariomentioning

confidence: 99%

“…The graph uses a waterfall plot to display the SHAP values (Lundberg and Lee, 2017) attributed to each questionnaire item. The displayed data is fictional, but it is based on a real model presented in Zhong et al (2022). In the low transparency condition (Figure 2B), the stages of a normal pregnancy are displayed by cartoon depictions of a woman from early pregnancy until childbirth.…”

Section: Experimental Conditionsmentioning

confidence: 99%

“…We explore these questions by providing a case study within the context of Perinatal Depression (PND) screening. Based on recent work studying automatic detection of PND in late pregnancy and the application of Explainable Artificial Intelligence (XAI) techniques (Lundberg and Lee, 2017;Zhong et al, 2022), we consider a robot-patient interaction, in which a SAR automatically evaluates if the patient is at risk of PND. Addressing question one, we produce design speculations on what lower vs. higher levels of designed anthropomorphism could "look like" in this context, in a fixed, commercially available and commonly used robot platform, while respecting laws and regulations.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A case study in designing trustworthy interactions: implications for socially assistive robotics

Zhong

Fraile

Castellano

et al. 2023

Front. Comput. Sci.

Self Cite

View full text Add to dashboard Cite

This work is a case study in applying recent, high-level ethical guidelines, specifically concerning transparency and anthropomorphisation, to Human-Robot Interaction (HRI) design practice for a real-world Socially Assistive Robot (SAR) application. We utilize an online study to investigate how the perception and efficacy of SARs might be influenced by this design practice, examining how robot utterances and display manipulations influence perceptions of the robot and the medical recommendations it gives. Our results suggest that applying transparency policies can improve the SAR's effectiveness without harming its perceived anthropomorphism. However, our objective measures suggest participant understanding of the robot's decision-making process remained low across conditions. Furthermore, verbal anthropomorphisation does not seem to affect the perception or efficacy of the robot.

show abstract

Section: Scenariomentioning

confidence: 99%

“…This data can be used to predict the future occurrence of PND. For instance, in late pregnancy (Zhong et al, 2022), or after childbirth (Bilal et al, 2022).…”

Section: Scenariomentioning

confidence: 99%

Section: Experimental Conditionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A case study in designing trustworthy interactions: implications for socially assistive robotics

Zhong

Fraile

Castellano

et al. 2023

Front. Comput. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Over the last decade, with the advancements in digital health and computational capabilities, numerous studies have focused on technology-based approaches to advance support for perinatal mental health [12,13]. This includes the development of Arti cial Intelligence (AI)-based models, particularly machine learning and deep learning-based models, for predicting perinatal mental-health conditions [14][15][16][17][18]. These AI-assisted decision-support approaches in mainstream populations have potential to facilitate early and accurate detection of depression, thereby enabling timely interventions.…”

Section: Introductionmentioning

confidence: 99%

Decoding Perinatal Mental Health: Investigating Protective and Risk Factors and Predictive Insights for Aboriginal Perinatal Mental Health through Explainable Machine Learning

Wang,

Bennamoun,

Kwok

et al. 2024

Preprint

View full text Add to dashboard Cite

Background Perinatal mental health significantly affects mothers, infants, and families. Despite their resilience and strengths, Aboriginal mothers experience disproportionate physical and mental health disparities. These result from historical and ongoing impacts of colonization and the resultant complex trauma. Conventional approaches to perinatal care present many barriers for Aboriginal mothers who frequently feel disengaged, apprehensive and unsafe. Current score-based risk-screening practices that algorithmically drive referrals, further ingrain fears including culturally biased judgments and child removal. The Baby Coming You Ready (BCYR) model of care centred around a digitised, holistic, strengths-based assessment, was co-designed to address these barriers. The recent successful pilot demonstrated BCYR effectively replaced all current risk-based screens. However, many professionals disproportionately rely on psychological risk scores, overlooking the contextual circumstances of Aboriginal mothers, their cultural strengths and mitigating protective factors. Methods To address this singular reliance screening psychometrics whilst supporting strengthened culturally considered clinical assessment, we propose a culturally sensitive eXplainable AI (XAI) solution. It combines XAI with Aboriginal lived experience, knowledge and wisdom to generate a clinical prediction model to support professionals and Aboriginal mothers being screened. The XAI solution can identify, prioritise, and weigh both maternal protective strengths and risk factors, quantify their relative impacts on perinatal mental-health and well-being at both group and individual levels. Results Different machine learning algorithms, including Random Forest, K-nearest neighbour, and support vector machine, alongside glassbox Explainable Boosting Machine (EBM) models, were trained on the real life de-identified data generated during the BCYR pilot. Additionally, XAI techniques like SHAP and LIME are utilised for interpretability on black box models. Results show the EBM model demonstrates superior performance in prediction, with an accuracy of 0.849, F1 score of 0.771 and AUC of 0.821. Global explanations across the entire dataset and local explanations for individual cases, achieved through different methods, were compared and showed similar and stable results. Conclusions This study demonstrated the potential for this XAI solution to enhance professionals' capability in culturally responsive clinical reasoning in perinatal mental-health screening to improve experience and strengthen outcomes for Aboriginal women.

show abstract

Trends, Opportunities, and Challenges in Detecting Depressive Disorders Through Mobile Devices: A Review

Elwirehardja,

Isnan,

Perbangsa

et al. 2023

2023 2nd International Conference on Computer System, Information Technology, and Electrical Engineering (COSITE)

View full text Add to dashboard Cite

Unimodal vs. Multimodal Prediction of Antenatal Depression from Smartphone-based Survey Data in a Longitudinal Study

Cited by 4 publications

References 68 publications

A case study in designing trustworthy interactions: implications for socially assistive robotics

A case study in designing trustworthy interactions: implications for socially assistive robotics

Decoding Perinatal Mental Health: Investigating Protective and Risk Factors and Predictive Insights for Aboriginal Perinatal Mental Health through Explainable Machine Learning

Trends, Opportunities, and Challenges in Detecting Depressive Disorders Through Mobile Devices: A Review

Contact Info

Product

Resources

About