China ; Data Analysis ; Humans ; Lung

This paper aims to highlight some of the common areas of concern in qualitative research proposals and manuscripts, gleaned from the authors’ first-hand experience as an external referee and peer-reviewer. The purpose is to provide broad guidance to researchers who are contemplating on writing a research proposal or journal manuscript using a qualitative approach. The three issues are (1) application of the generic label “qualitative” when proponents or authors describe the study design; (2) overreliance on, and even misuse of, interviews and focus groups for data collection; and (3) misconceptions on the process of qualitative data analysis. Practice points are offered on how researchers can avoid these missteps. Research has been characterized as a quest for knowledge, and it has been proposed that both qualitative and quantitative approaches uncover different dimensions of “truth”. The predominance of a positivist ontology in health research in the Philippines and elsewhere, coupled with intense methodological training in quantitative approaches, however, has relegated qualitative research to second-class status. Improving the quality of qualitative research work by addressing some of the issues outlined in this paper is one way of moving past this situation.
Qualitative Research ; Data Collection ; Data Analysis ; Research Report ; Journal Article

This paper focuses on lossless medical image compression methods for medical images that operate on three-dimensional(3-D) irreversible integer wavelet transform. We offer an application of the Set Partitioning in Hierarchical Trees(SPIHT) algorithm to medical images, using a 3-D wavelet decomposition and a 3-D spatial dependence tree. The wavelet decomposition is accomplished with integer wavelet filters implemented with the lifting method, where careful scaling(square root 2) and truncations keep the integer precision and the transform unitary. We have tested our encoder on volumetric medical images using different integer filters and different coding unit sizes. The coding unit sizes of 16 slices save considerable dynamic memory(RAM) and coding delay from full sequence coding units used in previous works. Results show that, even with these small coding units, our algorithm with certain filters performs as well and better in lossless coding than previous coding systems using 3-D integer wavelet transforms on volumetric medical images.
Clinical Coding ; Data Compression* ; Lifting* ; Wavelet Analysis*

The Chinese national college students' life science competition has been held for three times, with good organization, large scale and high participation degree. The competition plays an important role in promoting life science education and research. This paper reports the form and status of the competition, statistically analyses the registration data and competition results by region and year, based on the previous three competitions. By combining new changes and understanding in the field of life science, we also indicate prospects on how to better promote the competition.
Biological Science Disciplines ; Data Analysis ; Humans ; Students

Traditional biomedical data analysis technology faces enormous challenges in the context of the big data era. The application of deep learning technology in the field of biomedical analysis has ushered in tremendous development opportunities. In this paper, we reviewed the latest research progress of deep learning in the field of biomedical data analysis. Firstly, we introduced the deep learning method and its common framework. Then, focusing on the proposal of biomedical problems, data preprocessing method, model building method and training algorithm, we summarized the specific application of deep learning in biomedical data analysis in the past five years according to the chronological order, and emphasized the application of deep learning in medical assistant diagnosis. Finally, we gave the possible development direction of deep learning in the field of biomedical data analysis in the future.
Algorithms ; Biomedical Technology ; Data Analysis ; Deep Learning

In clinical, manually scoring by technician is the major method for sleep arousal detection. This method is time-consuming and subjective. This study aimed to achieve an end-to-end sleep-arousal events detection by constructing a convolutional neural network based on multi-scale convolutional layers and self-attention mechanism, and using 1 min single-channel electroencephalogram (EEG) signals as its input. Compared with the performance of the baseline model, the results of the proposed method showed that the mean area under the precision-recall curve and area under the receiver operating characteristic were both improved by 7%. Furthermore, we also compared the effects of single modality and multi-modality on the performance of the proposed model. The results revealed the power of single-channel EEG signals in automatic sleep arousal detection. However, the simple combination of multi-modality signals may be counterproductive to the improvement of model performance. Finally, we also explored the scalability of the proposed model and transferred the model into the automated sleep staging task in the same dataset. The average accuracy of 73% also suggested the power of the proposed method in task transferring. This study provides a potential solution for the development of portable sleep monitoring and paves a way for the automatic sleep data analysis using the transfer learning method.
Sleep ; Sleep Stages ; Arousal ; Data Analysis ; Electroencephalography

Background: Injury surveillance is viewed as an important component of injury prevention. Several data systems in the Philippines exist but have not been analyzed together. Analyzing these readily available data can guide policy making. Objective: This report aimed to describe the epidemiology of injuries in the Philippines using secondary datasets. Method: Death data of 2013 from the Philippines Statistics Authority and injury surveillance data of 2014 from the Department of Health were obtained and recoded. Summary statistics were generated. Results: Injured persons mainly come from the young age group. There were a higher number of males compared to females. Provincial variations in death rates for specific injury types existed. There did not seem to be an obvious pattern in injury occurrence according to month and time of day. High numbers of injuries were reported during daytime but admission and death rates peak at night. Injuries were shown to be an anatomically heterogeneous group with dominance in superficial injuries, head trauma, and hand fractures. Conclusion Analysis of secondary datasets revealed the epidemiology of injuries in the Philippines. Results have implications in health policy and injury prevention.
Epidemiology ; Secondary Data Analysis ; Wounds and Injuries

Background: Since 1987, data regarding dental caries prevalence in the Philippines has been shown to be over 90%.

Objective: This study compared the trends of Filipino web searches regarding toothache and milk tea from 2017 to 2019 through spatio-temporal analyses.

Methods: Google Trends searches for the years 2017, 2018, and 2019 were done using three separate search queries using the parameters "toothache" (TA) and "milk tea" (MT) as search terms, Philippines as location, Health as category, and Web Search as database.

Results: The outcome showed a decreasing trend in searches for toothache and an increasing interest for milk tea web searches from 2017 to 2019. A multiple comparison test showed that searches for MT were significantly more than TA in 2017 (p<0.001), 2018 (p<0.001), and 2019 (p<0.001). Searches for TA during the 2nd, 3rd, and 4th quarter compared to the 1st quarter of the year, in Caraga, Eastern Visayas, Western Visayas and Zamboanga Peninsula compared to Manila, were found to be significantly high.

Conclusion: Filipinos' health-seeking behavior show decreasing interest towards TA and increasing for MT.

Key Words: spatio-temporal analysis, data mining, health-seeking behavior, dental care, Philippines

Introduction: Incidence of cancer morbidity and mortality in the Philippines continues to escalate despite the survival rate that much still need to be desired. Few were able to cross the bridge. Still the social processes surrounding cancer survivors’experiences has not been given much attention. Methods: A Grounded Theory was the design selected using in-depth, unstructured interview among ten (10) participants who were considered as cancer survivors. Purposive, snowball and theoretical sampling were used to recruit participants. Interviews were audiotaped or recorded and transcribed verbatim. Data analysis was guided by Creswell’s four major phases; open coding, axial coding, selective coding and visual portraying. Memoing, field notes, member checking, audit trail and validation were all integrated with the study to enhance trustworthiness of study findings. Results: Based from the participants’ story, the primary psychosocial process that emerged can be described as “transformational journey”. This core variable explains the complexity of the journey in the life of a cancer survivor. The researchers identified six (6) iterative phases namely: (1) seeking answers; (2) encountering burden; (3) will to survive; (4) exhausting measures to live; (5) becoming a victor and (6) transitioning. Conclusion The findings from this study elucidates that cancer patients who become victorious after battling the disease traverses a transformational journey that defies and changes their perspective at what life is. Healthcare provider should develop protocol on how to support and to assist patient as they battle through the challenges in the different stages of their journey.
Cancer Survivors ; Neoplasms ; Grounded Theory ; Data Analysis

Analyzing patterns in data points embedded in linear and non-linear feature spaces is considered as one of the common research problems among different research areas, for example: data mining, machine learning, pattern recognition, and multivariate analysis. In this paper, data points are heterogeneous sets of biosequences (composite data points). A composite data point is a set of ordinary data points (e.g., set of feature vectors). We theoretically extend the derivation of the largest generalized eigenvalue-based distance metric D(ij)(γ₁) in any linear and non-linear feature spaces. We prove that D(ij)(γ₁) is a metric under any linear and non-linear feature transformation function. We show the sufficiency and efficiency of using the decision rule δ(Ξi) (i.e., mean of D(ij)(γ₁)) in classification of heterogeneous sets of biosequences compared with the decision rules min(Ξi) and median(Ξi). We analyze the impact of linear and non-linear transformation functions on classifying/clustering collections of heterogeneous sets of biosequences. The impact of the length of a sequence in a heterogeneous sequence-set generated by simulation on the classification and clustering results in linear and non-linear feature spaces is empirically shown in this paper. We propose a new concept: the limiting dispersion map of the existing clusters in heterogeneous sets of biosequences embedded in linear and nonlinear feature spaces, which is based on the limiting distribution of nucleotide compositions estimated from real data sets. Finally, the empirical conclusions and the scientific evidences are deduced from the experiments to support the theoretical side stated in this paper.
Classification ; Cluster Analysis ; Data Mining ; Dataset ; Machine Learning ; Multivariate Analysis