Effectiveness of Debiasing Techniques: An Indigenous Qualitative Analysis

An indigenous perspective on the effectiveness of debiasing techniques for pre-trained language models (PLMs) is presented in this paper. The current techniques used to measure and debias PLMs are skewed towards the US racial biases and rely on pre-defined bias attributes (e.g. "black" vs "white"). Some require large datasets and further pre-training. Such techniques are not designed to capture the underrepresented indigenous populations in other countries, such as M\=aori in New Zealand. Local knowledge and understanding must be incorporated to ensure unbiased algorithms, especially when addressing a resource-restricted society.Comment: accepted with invite to presen

Domain-specific language models for multi-label classification of medical text

Recent advancements in machine learning-based medical text multi-label classifications can be used to enhance the understanding of the human body and aid the need for patient care. This research considers predicting medical codes from electronic health records (EHRs) as multi-label problems, where the number of labels ranged from 15 to 923. It is motivated by the advancements in domain-specific language models to better understand and represent electronic health records and improve the predictive accuracy of medical codes. The thesis presents an extensive empirical study of language models for binary and multi-label medical text classifications. Domain-specific multi-sourced fastText pre-trained embeddings are introduced. Experimental results show considerable improvements to predictive accuracy when such embeddings are used to represent medical text. It is shown that using domain-specific transformer models outperforms results for multi-label problems with fixed sequence length. If processing time is not an issue for a long medical text, then TransformerXL will be the best model to use. Experimental results show significant improvements over other models, including state-of-the-art results, when TransformerXL is used for down-streaming tasks such as predicting medical codes. The thesis considers concatenated language models to handle long medical documents and text data from multiple sources of EHRs. Experimental results show improvements in overall micro and macro F1 scores, and such improvements are achieved with fewer resources. In addition, it is shown that concatenated domain-specific transformers improve F1 scores of infrequent labels across several multi-label problems, especially with long-tail labels

EFFECT OF ORAL AZITHROMYCIN AND METRONIDAZOLE AS AN ADJUNCT TO SCALING AND ROOT PLANING ON GLYCEMIC CONTROL IN TYPE II DIABETIC PATIENTS WITH CHRONIC PERIODONTITIS

ABSTRACTObjective: Periodontitis, a chronic inflammatory disease characterized by destruction of the periodontal ligament and alveolar bone is the sixthcomplication of diabetes mellitus. Periodontal treatment that reduces gingival inflammation aids in the control of hyperglycemia. Therefore, thepresent study was designed to determine the effect of treating chronic periodontitis with oral antibiotics azithromycin and metronidazole on the levelof serum glycated hemoglobin in type-II diabetic patients.Methods: This prospective observational study was carried out in the dental department of a tertiary care hospital for 9 months. Clinical andbiochemistry reports of 90 patients were collected in designed case report forms. All statistical analyses were performed using IBM Statistical Packagefor Social Sciences 17 and Graph Pad Prism 7.0.Results: Significant reduction in all the clinical and dental parameters was comparatively higher in patients who received azithromycin than inpatients who received metronidazole and scaling and root planning alone.Conclusion: Periodontal therapy with oral azithromycin can be employed as a supportive strategy for the management of diabetes mellitus.Henceforth, prevention and control of periodontal disease along with antibiotics must be considered an integral part of glycemic control. However,due to the lesser sample size in this study, further investigations are required to confirm the effect of periodontal therapy on systemic diseases.Keywords: Periodontitis, Azithromycin, Metronidazole, Glycemic control, Diabetes mellitus

Adoption of international privacy standards in New Zealand health information research.

The use of patient health information in secondary research (henceforth referred as health information research) has many potential health and economic benefits for New Zealand, but patient privacy must be protected in the process. In this letter, we describe some exciting recent developments in data de-identification and international privacy law, and how these might impact health information research in New Zealand.https://www.nzma.org.nz/journa

Research using electronic health records: not all de-identified datasets are created equal

We read the article Research using electronic health records: balancing confidentiality and public good by Wallis et al. with great interest. The authors note general practices need to trust de-identification processes when releasing patient records.¹ Patients have also expressed concerns about de-identification practices.² De-identification encompasses a wide range of practices, and there are no universally accepted standards.²,³ We propose here a three-step scheme for judging de-identified health records: (1) the de-identification standards used (2) the performance of the de-identification system and (3) additional security measures taken to prevent re-identification. Such a scheme may be useful to ethics committees, researchers planning a project and health providers deciding whether to participate

Challenges in Annotating Datasets to Quantify Bias in Under-represented Society

Recent advances in artificial intelligence, including the development of highly sophisticated large language models (LLM), have proven beneficial in many real-world applications. However, evidence of inherent bias encoded in these LLMs has raised concerns about equity. In response, there has been an increase in research dealing with bias, including studies focusing on quantifying bias and developing debiasing techniques. Benchmark bias datasets have also been developed for binary gender classification and ethical/racial considerations, focusing predominantly on American demographics. However, there is minimal research in understanding and quantifying bias related to under-represented societies. Motivated by the lack of annotated datasets for quantifying bias in under-represented societies, we endeavoured to create benchmark datasets for the New Zealand (NZ) population. We faced many challenges in this process, despite the availability of three annotators. This research outlines the manual annotation process, provides an overview of the challenges we encountered and lessons learnt, and presents recommendations for future research.Comment: Accepted in Ethics and Trust in Human-AI Collaboration: Socio-Technical Approaches @ The 32nd International Joint Conference on Artificial Intelligenc

Improved Estimation Performance of Sensor in Wireless Sensor Network Using Suboptimal Technique

ABSTRACT--This paper presents a novel network lifetime extension technique. In order to collect information more efficiently, wireless sensor networks (WSNs) are partitioned into clusters. Clustering provides an effective way to prolong the lifetime of WSNs. Current clustering approaches often use two methods: selecting cluster heads with more residual energy, and rotating cluster head periodically, to distribute the energy consumption among nodes in each cluster and extend the network lifetime. However, most of the previous algorithms have not considered the expected residual energy, only consider the estimation performance. In this paper we propose a probabilistic based transmission using clustering algorithm. Probabilistic transmission control at which is to minimize the mean squared error of estimation by increasing the packet transmission success probability of only sensors having high observation SNR. These newly available sensors are partitioned into several sensor sets select the cluster head to maintain the same estimation performance. The simulation results show that the proposed approach is more efficient than other distributed algorithms. It is believed that the technique presented in this paper could be further applied to large-scale wireless sensor networks