title: Data and model bias in artificial intelligence for healthcare applications in New Zealand

authors: Yogarajan, Vithya

abstract: Introduction: Developments in Artificial Intelligence (AI) are adopted widely in healthcare. However, the introduction and use of AI may come with biases and disparities, resulting in concerns about healthcare access and outcomes for underrepresented indigenous populations. In New Zealand, Māori experience significant inequities in health compared to the non-Indigenous population. This research explores equity concepts and fairness measures concerning AI for healthcare in New Zealand. Methods: This research considers data and model bias in NZ-based electronic health records (EHRs). Two very distinct NZ datasets are used in this research, one obtained from one hospital and another from multiple GP practices, where clinicians obtain both datasets. To ensure research equality and fair inclusion of Māori, we combine expertise in Artificial Intelligence (AI), New Zealand clinical context, and te ao Māori. The mitigation of inequity needs to be addressed in data collection, model development, and model deployment. In this paper, we analyze data and algorithmic bias concerning data collection and model development, training and testing using health data collected by experts. We use fairness measures such as disparate impact scores, equal opportunities and equalized odds to analyze tabular data. Furthermore, token frequencies, statistical significance testing and fairness measures for word embeddings, such as WEAT and WEFE frameworks, are used to analyze bias in free-form medical text. The AI model predictions are also explained using SHAP and LIME. Results: This research analyzed fairness metrics for NZ EHRs while considering data and algorithmic bias. We show evidence of bias due to the changes made in algorithmic design. Furthermore, we observe unintentional bias due to the underlying pre-trained models used to represent text data. This research addresses some vital issues while opening up the need and opportunity for future research. Discussions: This research takes early steps toward developing a model of socially responsible and fair AI for New Zealand's population. We provided an overview of reproducible concepts that can be adopted toward any NZ population data. Furthermore, we discuss the gaps and future research avenues that will enable more focused development of fairness measures suitable for the New Zealand population's needs and social structure. One of the primary focuses of this research was ensuring fair inclusions. As such, we combine expertise in AI, clinical knowledge, and the representation of indigenous populations. This inclusion of experts will be vital moving forward, proving a stepping stone toward the integration of AI for better outcomes in healthcare.

text: Data and model bias in artificial intelligence for healthcare applications in New Zealand Introduction: Developments in Artificial Intelligence (AI) are adopted widely in healthcare. However, the introduction and use of AI may come with biases and disparities, resulting in concerns about healthcare access and outcomes for underrepresented indigenous populations. In New Zealand, Māori experience significant inequities in health compared to the non-Indigenous population. This research explores equity concepts and fairness measures concerning AI for healthcare in New Zealand. Methods: This research considers data and model bias in NZ-based electronic health records (EHRs). Two very distinct NZ datasets are used in this research, one obtained from one hospital and another from multiple GP practices, where clinicians obtain both datasets. To ensure research equality and fair inclusion of Māori, we combine expertise in Artificial Intelligence (AI), New Zealand clinical context, and te ao Māori. The mitigation of inequity needs to be addressed in data collection, model development, and model deployment. In this paper, we analyze data and algorithmic bias concerning data collection and model development, training and testing using health data collected by experts. We use fairness measures such as disparate impact scores, equal opportunities and equalized odds to analyze tabular data. Furthermore, token frequencies, statistical significance testing and fairness measures for word embeddings, such as WEAT and WEFE frameworks, are used to analyze bias in free-form medical text. The AI model predictions are also explained using SHAP and LIME. Results: This research analyzed fairness metrics for NZ EHRs while considering data and algorithmic bias. We show evidence of bias due to the changes made in algorithmic design. Furthermore, we observe unintentional bias due to the underlying pre-trained models used to represent text data. This research addresses some vital issues while opening up the need and opportunity for future research. Discussions: This research takes early steps toward developing a model of socially responsible and fair AI for New Zealand's population. We provided an overview of reproducible concepts that can be adopted toward any NZ population data. Furthermore, we discuss the gaps and future research avenues that will enable more focused development of fairness measures suitable for the New Zealand population's needs and social structure. One of the primary focuses of this research was ensuring fair inclusions. As such, we combine expertise in AI, clinical knowledge, and the representation of indigenous populations. This inclusion of experts will be vital moving forward, proving a stepping stone toward the integration of AI for better outcomes in healthcare.

year: 2022-12-22

title: Caregiver Health Related Quality of Life and Burden When Caring for Children with Traumatic Brain Injury

authors: Rose, Wallis

subjects: Caregiver Burden

abstract: Often family members take on the responsibility of caregiver when another family member sustains a traumatic brain injury (TBI). The caregiving role is a stressful task which may impact negatively upon caregivers’ psychological and physical health. Variables which may contribute to caregiver burden include: caregiver age, educational attainment, income, gender, ethnicity, marital status, supports, time post-injury, and child clinical variables: TBI severity, behavioural functioning and adaptive functioning. The literature varies in its reporting of which areas of health are most affected, and the degree to which sociodemographic and child variables impact upon negative health related quality of life (HRQoL) and burden. To address this the current study explored health outcomes for TBI caregivers using a battery of measures (Medical Study Short Form-36, Hospital Anxiety and Depression Scale, DSM-IV Depression Diagnostic Scale, BAKAS Caregiving Outcomes Scale); questionnaires were also used to collect sociodemographic information and information relating to supports and services used in the rehabilitation of the TBI child. The Behavioural Assessment System for Children was used to collect behavioural and adaptability information from the caregivers about the child. Assessments were carried out at baseline, 1-month, 6-months and 12-months post TBI from a sample of 94 TBI caregivers and at baseline from a sample of 43 Control caregivers.

TBI caregivers experienced poorer overall health and higher levels of depression and were less likely to report positive life changes compared to Control caregivers. Older age and higher income predicted positive life changes. In addition to this higher income predicted better physical and overall health. Ethnicity was found to predict depression with the Māori/‘other ethnicity’ group suffering higher levels of depression. Children’s dysfunctional behaviour was found to predict poorer caregiver health outcomes across several domains; these include physical health, overall health, and depression; while the child’s adaptive functioning was not found to be a predictive factor in any caregiver health domains. New Zealand European and Māori TBI caregivers experienced similar health outcomes and received similar levels of support. However, Māori experienced more positive life changes than New Zealand Europeans. Caregivers’ physical and psychological well-being was found to improve over time, these changes were the greatest between the 1-month and 12-month period.
As a high percentage of children’s TBI’s occur in the home and at school, education aimed at schools and parents to inform of the effects of TBI upon children and families will bring an awareness which may encourage families to seek medical help. In doing so will offer the opportunity to receive or seek support in the initial period post TBI in the hope of reducing the burden for caregivers and producing better health outcomes. Bringing these families into contact with medical services may also help in identifying caregivers at higher risk of poorer health outcomes. Findings of more positive life changes for Māori may suggest cultural ideology is a protective factor in caregiver burden, further investigation may be necessary to understand these cultural differences and how they impact upon the caregiving role.

text: Caregiver Health Related Quality of Life and Burden When Caring for Children with Traumatic Brain Injury Often family members take on the responsibility of caregiver when another family member sustains a traumatic brain injury (TBI). The caregiving role is a stressful task which may impact negatively upon caregivers’ psychological and physical health. Variables which may contribute to caregiver burden include: caregiver age, educational attainment, income, gender, ethnicity, marital status, supports, time post-injury, and child clinical variables: TBI severity, behavioural functioning and adaptive functioning. The literature varies in its reporting of which areas of health are most affected, and the degree to which sociodemographic and child variables impact upon negative health related quality of life (HRQoL) and burden. To address this the current study explored health outcomes for TBI caregivers using a battery of measures (Medical Study Short Form-36, Hospital Anxiety and Depression Scale, DSM-IV Depression Diagnostic Scale, BAKAS Caregiving Outcomes Scale); questionnaires were also used to collect sociodemographic information and information relating to supports and services used in the rehabilitation of the TBI child. The Behavioural Assessment System for Children was used to collect behavioural and adaptability information from the caregivers about the child. Assessments were carried out at baseline, 1-month, 6-months and 12-months post TBI from a sample of 94 TBI caregivers and at baseline from a sample of 43 Control caregivers.

TBI caregivers experienced poorer overall health and higher levels of depression and were less likely to report positive life changes compared to Control caregivers. Older age and higher income predicted positive life changes. In addition to this higher income predicted better physical and overall health. Ethnicity was found to predict depression with the Māori/‘other ethnicity’ group suffering higher levels of depression. Children’s dysfunctional behaviour was found to predict poorer caregiver health outcomes across several domains; these include physical health, overall health, and depression; while the child’s adaptive functioning was not found to be a predictive factor in any caregiver health domains. New Zealand European and Māori TBI caregivers experienced similar health outcomes and received similar levels of support. However, Māori experienced more positive life changes than New Zealand Europeans. Caregivers’ physical and psychological well-being was found to improve over time, these changes were the greatest between the 1-month and 12-month period.
As a high percentage of children’s TBI’s occur in the home and at school, education aimed at schools and parents to inform of the effects of TBI upon children and families will bring an awareness which may encourage families to seek medical help. In doing so will offer the opportunity to receive or seek support in the initial period post TBI in the hope of reducing the burden for caregivers and producing better health outcomes. Bringing these families into contact with medical services may also help in identifying caregivers at higher risk of poorer health outcomes. Findings of more positive life changes for Māori may suggest cultural ideology is a protective factor in caregiver burden, further investigation may be necessary to understand these cultural differences and how they impact upon the caregiving role.

year: 2012

title: Effects of male call pitch on female behaviour and mate fidelity in little penguins

authors: Miyazaki, Masamine

subjects: advertising call

abstract: Playback experiments with two loudspeakers were conducted to examine how female little penguins (Eudyptula minor) respond to bray calls of males that varied in (1) dominant frequency (low vs high) and (2) familiarity (current partner vs stranger). In the first experiment, there was no statistically significant difference in the number of approaching females between low- and high-pitch calls. In the second experiment, using calls from the current partner and a stranger, females were more likely to approach closer to their own partners’ calls, regardless of the pitch of the stranger’s call. We conclude that female little penguins may recognize their partners’ calls (or at least familiar male calls) and appear to maintain mate fidelity even when larger, potentially more successful, strangers are on offer.

text: Effects of male call pitch on female behaviour and mate fidelity in little penguins Playback experiments with two loudspeakers were conducted to examine how female little penguins (Eudyptula minor) respond to bray calls of males that varied in (1) dominant frequency (low vs high) and (2) familiarity (current partner vs stranger). In the first experiment, there was no statistically significant difference in the number of approaching females between low- and high-pitch calls. In the second experiment, using calls from the current partner and a stranger, females were more likely to approach closer to their own partners’ calls, regardless of the pitch of the stranger’s call. We conclude that female little penguins may recognize their partners’ calls (or at least familiar male calls) and appear to maintain mate fidelity even when larger, potentially more successful, strangers are on offer.

year: 2005