Index Of Khushi
Harald Atta
He scanned the premises of the temple, hoping for a glimpse of his smiling girl. He looked closely at the people climbing the steps to the main building of the temple. He peered at the people exiting the temple. Maybe she had finished praying and was buying pictures of gods at the stalls there. He marched over to the shops and looked around. The devotees gave the stern-looking man in formal clothes, his eyes hidden by dark glasses, a wide berth. Maybe she was buying flowers and coconuts and other materials for the pooja. He looked at each face in the crowd there. She was absent. He clenched his fists.
Fury, the likes of which he had never known before, rushed through his veins. Panic was close behind. The feeling of something precious slipping through his hands was very strong. How dare she smile lovingly thinking of some man!
With a triumphant grunt, she pulled out what she had been looking for. Her purse. A small, red purse. Full of beads & sequins. A cheap, shining purse. She unzipped it and took a crumpled 100 rupee note from it. She tried to straighten it and do away with the wrinkles unsuccessfully, and then placed it in front of him, on his table.
I am still going strong, my dear. It is lovely to see you here. I have moved from IF to my blog. There were too many technical issues on IF and battling them was taking too much time. Get me on smita.ram...@gmail.com if you want the link to the blog.
smita, when u find time, please give me a solution dear. I have not faced this problem with any other blogs. Iam not able to login using my gmail id in ur blog. but thank God since the blog is not a protected site, iam still enjoying reading ur updates. Ur stories are a stress reliever to me smita. I enjoy reading ur stories. Will look into your index once again and will start re-reading your stories once again.
Thank you for your awesome work.
Thank you for sharing this index here. Smita is one of my favorite writer and her stories can be read over and over again and I would still be wanting more ? Keeping Khushi and Unwanted wife are my all time favorites. Thank you for sharing this index!!
Hi simta Ji . I am a fan of your stories can yu please give me access to your blogs and word press my Id is cornett90 @gmail.com and I am a silent reader . my Id in WordPress is arjunsam and also I am new to this site. So please guide me how to use this site.
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Introduction: The study aims to determine whether body mass index (BMI), metabolic syndrome (MS) or its individual components (primary hypertension, type 2 diabetes mellitus and dyslipidemias) are risk factors for common urological diseases.
Materials and methods: Cross-sectional study with data collected on February 28, 2022 from the TriNetX Research Network. Patients were divided in cohorts according to their BMI, presence of MS (BMI > 30 kg/m2, type 2 diabetes mellitus, primary hypertension and disorders of lipoprotein metabolism) and its individual components and its association with common urological conditions was determined. For each analysis, odds ratio (OR) with 95% confidence intervals were calculated. Statistical significance was assessed at p < .05.
Conclusions: MS and its individual components were significant risk factors for common urological conditions. Hence holistic approaches with lifestyle modification might prevent common urological disease.Key messagesOverall, metabolic syndrome is the strongest risk factor for all the analysed urological diseases.Abnormally high body mass index can be a risk or protective factor depending on the threshold and urological disease that are being evaluated.Metabolic syndrome and increased BMI should be considered important factors associated to the prevalence of common urological diseases.
In Rajasthan, India, 50,000 infants do not see their first birthday and 420,000 mothers go through high-risk deliveries - every year. We are developing AI models for early prediction of adverse maternal and neonatal care outcomes for 2.5M actively tracked mothers and infants across the state, so that 17,000 frontline nurses can prioritize their duelists and initiate early intervention.
In Rajasthan, India, 50,000 infants do not see their first birthday and 420,000 mothers go through high-risk deliveries - every year. While medical literature and local intuition can help guide which pregnant women and children are at higher risk of adverse outcomes, subtle nuances may go unnoticed leading to unexpected maternal and infant deaths. Rajasthan maintains records of over 80% of pregnancies (1.5 million) across the state, digitally, every year. However, data quality is cause for concern. Parameters for antenatal blood pressure, hemoglobin, and urine protein are often manipulated. Early and nuanced risk stratification of pregnant women therefore is hampered by poor quality inputs, despite the large volume of maternal and child health data being tracked.
Our solution is a series of machine-learning models to predict adverse maternal and child health outcomes. The solution comes in two-parts. First we work to solve the data-quality issue by training our models on gold-standard data (over 30,000 beneficiary records with 150 inputs each on health and social determinants from our rural laboratory in Udaipur, India). On this data set we have been able to fine-tune a set of over 30 data quality controls that give a composite index of the data being used in our prediction models. Now each input is weighted on the basis of it's reliability when used to train our prediction model.
Second, we design the prediction models using sophisticated neural networks which are fine-tuned over time as more data are provided. These models provide a probability of an adverse outcome for each pregnant woman and infant - allowing for prioritization of the health workers' due lists. Prioritized due-lists are conveniently shared along with action items to frontline nurses over WhatsApp groups. Our models which have shown 75% accuracy in predicting acute infant malnutrition are being now further refined in preparation to be applied to the State's repository of 2.5M beneficiaries.
Our solution primarily aims to empowers 17,000 frontline nurses (Auxiliary Nurse Midwives) who are responsible for caring for 2.5M pregnant women and infants living in rural Rajasthan, every year. Through timely delivery of prioritized due-lists, we hope to avert 2500 adverse infant outcomes by catalyzing timely intervention. The models structure is designed based on our understanding of behavior patterns over 4 years of closely working alongside 150 ANMs in rural Udaipur, Rajathan. The model outputs are equally catered to be delivered in a format that is both relevant and timely for the frontline nurse to take action.
Our solution seeks to expand access to high-quality, affordable maternal and newborn health care by helping frontline health workers identify which pregnant women and infants are at greatest risk of adverse outcomes, as suggested by our AI/ML models, which are locally refined over time. This prognostic tool may be used by frontline nurses to prioritize early intervention for these beneficiaries - advanced gynecological care referral, advanced planning for an institutional delivery, enhanced follow-up of maternal nutrition and adherence to micro-nutrient supplementation
Precision public health approaches have yet to overcome a major hurdle of poor data quality from last mile settings. Khushi AI is working with partners at Google AI for Social Good and Singapore Management University to develop an automated data quality index for each data point reported by frontline nurses on maternal and child health data. This composite index considers 40 data quality rules, derived from deep understanding of frontline health worker usage patterns (e.g. manipulating all blood pressure values to show 120/80 even though the equipment is known to be faulty) from direct field experience and by applying statistical anomaly detectors to novel representations of the maternal and child health data set. This data quality index which precedes implementation of the machine learning model is the unique differentiator when compared to other models that (prematurely) assume the ground truth of the data being used to train the model.
The core technology behind Khushi AI involves a neural network algorithm, with a specialized filter for data quality, to predict maternal and child health outcomes from noisy and manipulated data entered from the field. This algorithm is applied to multiple distinct datasets including geolocation, social determinants and health factors. Health behavior outcomes and health outcomes are independently predicted with probabilistic scores. These scores are used to rank and feedback data to the frontline health worker via their mobile application for data collection and/or WhatsApp. Health workers are also incentivized to improve their data quality over time with social interventions. After recording the early interventions initiated, a separate algorithm is trained to minimize the likelihood of the previously established probability for the adverse maternal and child health outcome.
By weighing each data input through our data quality index, and with sufficient diversity of inputs (including those from geographic, health worker, and beneficiary factors), we believe early signals can be identified for adverse maternal and child health outcomes, as evidenced by our early success with predicting acute malnutrition in infants.
Sharing these prioritized due lists in an automated manner with frontline nurses can allow them to prioritize follow-up to the top 10% of their 200-500 mother cohort. This early intervention is expected to lead to timely counseling, advanced gynecological screening, improved nutrition and medication adherence, and advanced preparation for delivery ultimately averting maternal and neonatal adverse events.
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