MOBILE BASED SYMPTOM MANAGEMENT FOR PALLIATIVE CARE

The goal of palliative care is to improve the quality of life of terminally ill patients through the management of pain and other symptoms. Though the term `palliative care\u27 is well known in the developed world, it is relatively a new term in the developing world. According to WHO, each year 4.8 million people suffering from severe pain caused by cancer, fail to receive treatment due to lack of resources and other barriers. In this thesis we have elaborated on the challenges faced by the rural breast cancer (BC) patients of Bangladesh and a solution for their palliative care treatment. Although breast cancer is commonly thought of as a disease of the developed world, the WHO statistics show that 69% of all BC deaths occur in developing countries. Unlike western countries where 89% of the women have a survival rate of more than 5 years, most BC patients in Bangladesh die because the majority of cases are diagnosed in late stages. These patients need palliative care which is almost absent in rural Bangladesh. These issues show the desperate need of a low cost palliative care system solution for the terminally ill patients of the developing world. Based on detailed field studies, we have developed and deployed a mobile based remote symptom monitoring and management system named e-ESAS. Design of e-ESAS has evolved through continuous feedback from both the patients and doctors. e-ESAS is being used by 10 breast cancer patients to submit symptom values from their home for the last 10 months (Nov\u2711- Sep \u2712). Our results show how e-ESAS with motivational videos not only helped the patients to have a `dignified\u27 life but also helped the doctors to achieve the goals of palliative care. Also the analyzed results are shown in 4 categories to appropriately measure the contribution of e-ESAS in improving the QoL. This thesis also focuses on developing a mobile based pain intensity detection tool which is a first step in replacing the manual paper based scale for measuring pain. The tool also might play a big role in assessing the pain level of verbally impaired patients

SmartHeLP: Smartphone-based Hemoglobin Level Prediction Using an Artificial Neural Network

Blood hemoglobin level (Hgb) measurement has a vital role in the diagnosis, evaluation, and management of numerous diseases. We describe the use of smartphone video imaging and an artificial neural network (ANN) system to estimate Hgb levels non-invasively. We recorded 10 second-300 frame fingertip videos using a smartphone in 75 adults. Red, green, and blue pixel intensities were estimated for each of 100 area blocks in each frame and the patterns across the 300 frames were described. ANN was then used to develop a model using the extracted video features to predict hemoglobin levels. In our study sample, with patients 20-56 years of age, and gold standard hemoglobin levels of 7.6 to 13.5 g/dL., we observed a 0.93 rank order of correlation between model and gold standard hemoglobin levels. Moreover, we identified specific regions of interest in the video images which reduced the required feature space

Mother’s dietary diversity and association with stunting among children <2 years old in a low socio‐economic environment: A case–control study in an urban care setting in Dhaka, Bangladesh

Mothers are often responsible for preparing nutritious foods in their households. However, the quality of mother’s diets is often neglected, which may affect both mother’s and child’s nutrition. Because no single food contains all necessary nutrients, diversity in dietary sources is needed to ensure a quality diet. We aimed to study the association between mother’s dietary diversity and stunting in children <2 years attending Dhaka Hospital of icddr,b, a diarrhoeal disease hospital in Dhaka, Bangladesh. A case–control study (n = 296) was conducted from November 2016 to February 2017. Data were collected from mothers of stunted children <2 years (length‐for‐age z score [LAZ] < −2) as “cases” and nonstunted (LAZ ≥ −1) children <2 years as “controls.” Mothers were asked to recall consumption of 10 defined food groups 24 hr prior to the interview as per Guidelines for Minimum Dietary Diversity for Women. Among the mothers of cases, 58% consumed <5 food groups during the last 24 hr, compared with 45% in control mothers (P = 0.03). Children whose mothers consumed <5 food groups were 1.7 times more likely to be stunted than children whose mothers consumed ≥5 food groups (P = 0.04). Intake of food groups such as pulses, dairy, eggs, and vitamin A rich fruit was higher in control mothers. Proportion of mother’s illiteracy, short stature, monthly family income <BDT 11,480, absence of bank account, and poor sanitation was also found to be higher in stunted group. Further study particularly intervention or longitudinal study to see the causality of mother’s dietary diversity with child stunting is recommended.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148421/1/mcn12665.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148421/2/mcn12665_am.pd

e-ESAS: Evolution of a Participatory Design-based Solution for Breast Cancer (BC) Patients in Rural Bangladesh

Healthcare facility is scarce for rural women in the developing world. The situation is worse for patients who are suffering from diseases that require long-term feedback-oriented monitoring such as breast cancer. Lack of motivation to go to the health centers on patients’ side due to sociocultural barriers, financial restrictions and transportation hazards results in inadequate data for proper assessment. Fortunately, mobile phones have penetrated the masses even in rural communities of the developing countries. In this scenario, a mobile phone-based remote symptom monitoring system (RSMS) with inspirational videos can serve the purpose of both patients and doctors. Here, we present the findings of our field study conducted on 39 breast cancer patients in rural Bangladesh. Based on the results of extensive field studies, we have categorized the challenges faced by patients in different phases of the treatment process. As a solution, we have designed, developed and deployed e-ESAS—the first mobile-based RSMS in rural context. Along with the detail need assessment of such a system, we describe the evolution of e-ESAS and the deployment results. We have included the unique and useful design lessons that we learned as e-ESAS evolved through participatory design process. The findings show how e-ESAS addresses several challenges faced by patients and doctors and positively impact their lives

An mCARE Study on Patterns of Risk and Resilience for Children with ASD in Bangladesh

Community-wide lockdowns in response to COVID-19 influenced many families, but the developmental cascade for children with autism spectrum disorder (ASD) may be especially detrimental. Our objective was to evaluate behavioral patterns of risk and resilience for children with ASD across parent-report assessments before (from November 2019 to February 2020), during (March 2020 to May 2020), and after (June 2020 to November 2020) an extended COVID-19 lockdown. In 2020, our study Mobile-based care for children with ASD using remote experience sampling method (mCARE) was inactive data collection before COVID-19 emerged as a health crisis in Bangladesh. Here we deployed “Cohort Studies”, where we had in total 300 children with ASD (150 test group and 150 control group) to collect behavioral data. Our data collection continued through an extended COVID-19 lockdown and captured parent reports of 30 different behavioral parameters (e.g., self-injurious behaviors, aggression, sleep problems, daily living skills, and communication) across 150 children with ASD (test group). Based on the children’s condition, 4–6 behavioral parameters were assessed through the study. A total of 56,290 behavioral data points was collected (an average of 152.19 per week) from parent cell phones using the mCARE platform. Children and their families were exposed to an extended COVID-19 lockdown. The main outcomes used for this study were generated from parent reports child behaviors within the mCARE platform. Behaviors included of child social skills, communication use, problematic behaviors, sensory sensitivities, daily living, and play. COVID-19 lockdowns for children with autism and their families are not universally negative but supports in the areas of “Problematic Behavior” could serve to mitigate future risk

Privacy Challenges in Context-sensitive Access Control for Pervasive Computing Environment

The widespread prevalence of pervasive devices and applications has raised the concerns of privacy. Granting Access to Resources and Context sensitive information causes information leakage through inference or obfuscation. Again, the open and dynamic collaborative environment of pervasive computing has rendered the traditional access control models like Role based models to be unfit. Even though Trust based models came to rescue in such circumstances, privacy is mostly compromised in the big picture. In this paper, we have drawn examples from pervasive computing environment and illustrated some scenarios of privacy violation. We have presented a trust based access control model for pervasive healthcare environment to prevent information leakage on accessing constraint information that yields privacy violation in the end. We have addressed information leak from three perspectives of constraint information satisfaction to grant access to the resources. They are satisfy any, satisfy all and hierarchical constraints. Furthermore, the model eliminates requirements for maintaining any keys or access rights certificates for privacy protection. This lightweight model helps ensure the resource constrained small pervasive devices like PDA, cell phones use the access control model effectively and efficiently with privacy of the individuals preserved in the first place

Towards TTP-Free Lightweight Solution for Location Privacy Using Location-Based Anonymity Prediction

Location-based services are becoming increasingly popular with the proliferation of location aware devices. It is not possible to access location-based services and preserve privacy at the same time when the user provides exact location information. Cloaking or obfuscating location data is the only way to protect location-privacy. To do that, most of the systems use third party location anonymizer. In this paper, we propose a novel location privacy obfuscation framework without any trusted third party (TTP). Most of the existing solutions attempt to satisfy k-anonymity. In this paper we present the problems of using fixed and user defined k. In order to solve the problems our proposed solution aims to meet probabilistic k-anonymity. Based on historic data expected number of users present in a place is predicted which is used as probabilistic anonymity level. Thus we eliminate the use of any TTP which results into improvement of query-processing time and provides fewer query results for the user to process minimizing the overall response time. Users\u27 exact location information is not revealed in either communication or computation process

Breast cancer prediction: A comparative study using machine learning techniques

Early detection of disease has become a crucial problem due to rapid population growth in medical research in recent times. With the rapid population growth, the risk of death incurred by breast cancer is rising exponentially. Breast cancer is the second most severe cancer among all of the cancers already unveiled. An automatic disease detection system aids medical staffs in disease diagnosis and offers reliable, effective, and rapid response as well as decreases the risk of death. In this paper, we compare five supervised machine learning techniques named support vector machine (SVM), K-nearest neighbors, random forests, artificial neural networks (ANNs) and logistic regression. The Wisconsin Breast Cancer dataset is obtained from a prominent machine learning database named UCI machine learning database. The performance of the study is measured with respect to accuracy, sensitivity, specificity, precision, negative predictive value, false-negative rate, false-positive rate, F1 score, and Matthews Correlation Coefficient. Additionally, these techniques were appraised on precision–recall area under curve and receiver operating characteristic curve. The results reveal that the ANNs obtained the highest accuracy, precision, and F1 score of 98.57%, 97.82%, and 0.9890, respectively, whereas 97.14%, 95.65%, and 0.9777 accuracy, precision, and F1 score are obtained by SVM, respectively

Shrimp Culture Impact on the Surface and Ground Water of Bangladesh

Abstract A case study was carried out to see the impacts of shrimp culture on the surface (pond) and ground water (tube-well) quality in three coastal sub-districts of Bagherhat Sadar, Rampal and Morrelganj of Bangladesh. The people of Rampal (100%), Morrelgonj (87.5%) and Bagherhat (75.5%) expressed that salinity of both surface and ground water increased after shrimp culture, and water becomes more turbid, odorous and less tasty compared to pre-shrimp culture scenario. The ground water pH was foo be slightly acidic (6.07-6.71) but the surface water was mildly alkaline in nature (7.00-7.46). Ground water was more saline (1893-2673ppm) than surface water (513-2253ppm). Potassium level of surface water was very high (97-242ppm) compared to the ground water (11.73-27.37 ppm). This exceeds the WHO Guideline Value (10ppm) and the Bangladesh Standard for Drinking Water (12ppm). The pollution levels of phosphorous and iron were found to be a little higher but other pollutants like nitrate, boron and zinc were found to be very low in surface and ground water in the shrimp culture area of Bangladesh