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: Maori and epilepsy: Personal perceptions of the cause, treatment and consequences of epilepsy by Maori in the Bay of Plenty

authors: Simonsen, Kiri

subjects: Maori

abstract: This paper discusses the perceptions of epilepsy held by Maori in the Bay of Plenty. The paper
begins by introducing the purpose and rationale of the research. It then moves on to describe the
aims and qualitative research methods that were used to collect the data. Finally the paper
discusses the findings of the research, this includes: a close look at the unique perceptions of
epilepsy that were reported by Maori in the Bay of Plenty; the lack of resources and services
available in a small rural town of the Bay of Plenty; the services desired by Maori; attitudes
towards medication and the inappropriate behaviour many of the participants experienced by the
medical profession.

text: Maori and epilepsy: Personal perceptions of the cause, treatment and consequences of epilepsy by Maori in the Bay of Plenty This paper discusses the perceptions of epilepsy held by Maori in the Bay of Plenty. The paper
begins by introducing the purpose and rationale of the research. It then moves on to describe the
aims and qualitative research methods that were used to collect the data. Finally the paper
discusses the findings of the research, this includes: a close look at the unique perceptions of
epilepsy that were reported by Maori in the Bay of Plenty; the lack of resources and services
available in a small rural town of the Bay of Plenty; the services desired by Maori; attitudes
towards medication and the inappropriate behaviour many of the participants experienced by the
medical profession.

year: 1999

title: The arylation of [Pt₂(μ-S)₂(PPh₃)₄]

authors: Deadman, Benjamin Jade

subjects: platinum complexes

abstract: Routes to the synthesis of the mixed sulfide-phenylthiolate complex [Pt₂(μ-S)(μ -SPh)(PPh₃)₄]⁺ have been explored; reaction of [Pt₂(μ-S)₂(PPh₃)₄] with excess Ph₂IBr proceeds readily to selectively produce this complex, which was structurally characterised as its PF₆⁻ salt. Reactions of [Pt₂(μ-S)₂(PPh₃)₄] with other potent arylating reagents (1-chloro-2,4-dinitrobenzene and 1,5-difluoro-2,4-dinitrobenzene) also produce the corresponding nitroarylthiolate complexes [Pt₂(μ-S){μ -SC₆H₂(NO₂)₂X}(PPh₃)₄]⁺ (X = H, F). The complex [Pt₂(μ-S)(μ -SPh)(PPh₃)₄]⁺ reacts with Me₂SO₄ to produce the mixed alkyl/aryl bis-thiolate complex [Pt₂(μ-SMe)(μ -SPh)(PPh₃)₄]²⁺, but corresponding reactions with the nitroarylthiolate complexes are plagued by elimination of the nitroaryl group and formation of [Pt₂(μ-SMe)₂(PPh₃)₄]²⁺. [Pt₂(μ-S)(μ -SPh)(PPh₃)₄]⁺ also reacts with Ph₃PAuCl to give [Pt₂(μ-SAuPPh₃)(μ -SPh)(PPh₃)₄]²⁺.

text: The arylation of [Pt₂(μ-S)₂(PPh₃)₄] Routes to the synthesis of the mixed sulfide-phenylthiolate complex [Pt₂(μ-S)(μ -SPh)(PPh₃)₄]⁺ have been explored; reaction of [Pt₂(μ-S)₂(PPh₃)₄] with excess Ph₂IBr proceeds readily to selectively produce this complex, which was structurally characterised as its PF₆⁻ salt. Reactions of [Pt₂(μ-S)₂(PPh₃)₄] with other potent arylating reagents (1-chloro-2,4-dinitrobenzene and 1,5-difluoro-2,4-dinitrobenzene) also produce the corresponding nitroarylthiolate complexes [Pt₂(μ-S){μ -SC₆H₂(NO₂)₂X}(PPh₃)₄]⁺ (X = H, F). The complex [Pt₂(μ-S)(μ -SPh)(PPh₃)₄]⁺ reacts with Me₂SO₄ to produce the mixed alkyl/aryl bis-thiolate complex [Pt₂(μ-SMe)(μ -SPh)(PPh₃)₄]²⁺, but corresponding reactions with the nitroarylthiolate complexes are plagued by elimination of the nitroaryl group and formation of [Pt₂(μ-SMe)₂(PPh₃)₄]²⁺. [Pt₂(μ-S)(μ -SPh)(PPh₃)₄]⁺ also reacts with Ph₃PAuCl to give [Pt₂(μ-SAuPPh₃)(μ -SPh)(PPh₃)₄]²⁺.

year: 2009