Concordance of a Self Assessment Tool and Measurement of Bone Mineral Density in Identifying the Risk of Osteoporosis in Elderly Taiwanese Women

AbstractObjectiveOsteoporosis is the most common generalized bone disease related to aging. The Osteoporosis Self-Assessment Tool for Asians (OSTA) risk index was developed to screen postmenopausal Asian women to identify women who should be evaluated with bone densitometry. In Taiwan, there is no report of the validity of the OSTA with dual energy X-ray absorptiometry (DXA) as a reference. In this study, we assessed the validity of the OSTA risk index and discuss its applications, using DXA of the lumbar spine as the gold standard.Patients and MethodsHealthy subjects, aged 30–85 years, who were receiving a health check-up at a teaching hospital in eastern Taiwan were invited to participate in this study. All subjects gave their consent to analyze their data. A self-administered questionnaire was used to assess their demographic characteristics, and reproductive and medical histories. Bone mineral density of the posterior-anterior lumbar spine was measured by DXA, and a diagnosis of osteoporosis was made according to World Health Organization criteria. The sensitivity and specificity and their 95% confidence intervals (CIs) were calculated for the dichotomized OSTA risk index.ResultsThis cohort consisted of 498 postmenopausal Taiwanese women, with a mean age of 60.3 ± 7.6 years and a mean weight of 57.9 ± 8.9 kg. Spinal DXA revealed that 35.9% were osteoporotic (with a T-score of £ −2.5). The OSTA risk index at the standard cut-off point of £ −1 had a sensitivity of 57.0% (95% CI: 52.7, 61.3) and a specificity of 69.3% (95% CI: 65.3, 73.4). Among women aged 60–70 years, the sensitivity, specificity, and accuracy of the OSTA risk index were 77.1% (95% CI: 63.7, 76.9), 49.2% (95% CI: 42.0, 56.4), and 64.9% (95% CI: 60.7, 69.8), respectively.ConclusionThe OSTA risk index is a convenient but not a very sensitive tool to help target high-risk women aged 60–70 years for DXA testing. Clinical risk factors and the OSTA risk index should be combined to assess women aged £ 60 years. Further study of the validity of the OSTA risk index among elderly women with a larger sample size in different populations should be conducted with spinal and femur neck DXA testing as references

Singing voice correction using canonical time warping

Expressive singing voice correction is an appealing but challenging problem. A robust time-warping algorithm which synchronizes two singing recordings can provide a promising solution. We thereby propose to address the problem by canonical time warping (CTW) which aligns amateur singing recordings to professional ones. A new pitch contour is generated given the alignment information, and a pitch-corrected singing is synthesized back through the vocoder. The objective evaluation shows that CTW is robust against pitch-shifting and time-stretching effects, and the subjective test demonstrates that CTW prevails the other methods including DTW and the commercial auto-tuning software. Finally, we demonstrate the applicability of the proposed method in a practical, real-world scenario

Maternal Demographic and Psychosocial Factors Associated with Low Birth Weight in Eastern Taiwan

The relationship between birth weight and maternal sociodemographic characteristics was examined in a sample from two teaching hospitals in eastern Taiwan. Using a structured questionnaire, we conducted face- to-face interviews with women at antenatal clinics between 1998 and 1999 in Hualien City. One year later, we took the outcome of pregnancy from medical records and birth certificates from the Public Health Bureau of Hualien County. Of the 1,128 single live births, 6.8% had low birth weight (LBW) using the World Health Organization cut-off of 2,500 g. LBW was more common in teenage (< 20 years), older (> 30 years), first-time, and unmarried mothers; those with basic/intermediate educational attainment; and residents of aboriginal districts. Teenage pregnancies were more likely than those in adults to be unplanned, and such mothers had smoking or alcohol-drinking behavior. Prevention of teenage pregnancy is crucial to lower LBW rates in eastern Taiwan. For adult mothers, basic or intermediate educational attainment, residence in an aboriginal district, and first-term pregnancy were significant factors associated with LBW, after adjustment for other psychosocial attributes, such as psychologic distress and poor family support. Thus, we should pay more attention when caring for pregnant women with such sociodemographic characteristics, and ensure that they have adequate prenatal care and can adopt a healthy lifestyle

Decoding AI's Nudge: A Unified Framework to Predict Human Behavior in AI-assisted Decision Making

With the rapid development of AI-based decision aids, different forms of AI assistance have been increasingly integrated into the human decision making processes. To best support humans in decision making, it is essential to quantitatively understand how diverse forms of AI assistance influence humans' decision making behavior. To this end, much of the current research focuses on the end-to-end prediction of human behavior using ``black-box'' models, often lacking interpretations of the nuanced ways in which AI assistance impacts the human decision making process. Meanwhile, methods that prioritize the interpretability of human behavior predictions are often tailored for one specific form of AI assistance, making adaptations to other forms of assistance difficult. In this paper, we propose a computational framework that can provide an interpretable characterization of the influence of different forms of AI assistance on decision makers in AI-assisted decision making. By conceptualizing AI assistance as the ``{\em nudge}'' in human decision making processes, our approach centers around modelling how different forms of AI assistance modify humans' strategy in weighing different information in making their decisions. Evaluations on behavior data collected from real human decision makers show that the proposed framework outperforms various baselines in accurately predicting human behavior in AI-assisted decision making. Based on the proposed framework, we further provide insights into how individuals with different cognitive styles are nudged by AI assistance differently.Comment: AAAI 202