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1.
i-Manager's Journal on Management ; 16(3):29-36, 2022.
Article in English | ProQuest Central | ID: covidwho-2056929

ABSTRACT

Cashless transactions are common in business sectors and in cities to a large extent. But after the announcement of the demonetization of all Rs. 500 and Rs. 1,000 banknotes on November 8, 2016 by the Government of India, all the sectors to the maximum extent adopted cashless transactions. SHGs too have adopted this culture by installing mobile apps on their smart phones. During 2015-16, NABARD, through its project Eshakti digitisation of SHGs, has made an attempt to update the SHG bookkeeping on a real time basis to bring transparency and credibility. The project EShakti had the advantage of addressing the problem of book keeping through available technology, knowing the credit history of SHG members, generating grades for SHGs based on their financial and non-financial records and making them available to all important stakeholders. The stakeholders, namely bankers can now take informed decisions on extending credit linkages on the basis of reports generated through EShakti. In this paper, an attempt is made to study the adoption of cashless transactions in SHGs in the Konaseema region.

2.
PLoS Comput Biol ; 17(12): e1009712, 2021 12.
Article in English | MEDLINE | ID: covidwho-1581905

ABSTRACT

Hypoxemia is a significant driver of mortality and poor clinical outcomes in conditions such as brain injury and cardiac arrest in critically ill patients, including COVID-19 patients. Given the host of negative clinical outcomes attributed to hypoxemia, identifying patients likely to experience hypoxemia would offer valuable opportunities for early and thus more effective intervention. We present SWIFT (SpO2 Waveform ICU Forecasting Technique), a deep learning model that predicts blood oxygen saturation (SpO2) waveforms 5 and 30 minutes in the future using only prior SpO2 values as inputs. When tested on novel data, SWIFT predicts more than 80% and 60% of hypoxemic events in critically ill and COVID-19 patients, respectively. SWIFT also predicts SpO2 waveforms with average MSE below .0007. SWIFT predicts both occurrence and magnitude of potential hypoxemic events 30 minutes in the future, allowing it to be used to inform clinical interventions, patient triaging, and optimal resource allocation. SWIFT may be used in clinical decision support systems to inform the management of critically ill patients during the COVID-19 pandemic and beyond.


Subject(s)
COVID-19/physiopathology , Critical Illness , Deep Learning , Hypoxia/blood , COVID-19/epidemiology , COVID-19/virology , Humans , Intensive Care Units , Pandemics , SARS-CoV-2/isolation & purification
3.
medrxiv; 2021.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2021.02.25.21252234

ABSTRACT

Hypoxemia is a significant driver of mortality and poor clinical outcomes in conditions such as brain injury and cardiac arrest in critically ill patients, including COVID-19 patients. Given the host of negative clinical outcomes attributed to hypoxemia, identifying patients likely to experience hypoxemia would offer valuable opportunities for early and thus more effective intervention. We present SWIFT (SpO2 Waveform ICU Forecasting Technique), a deep learning model that predicts blood oxygen saturation (SpO2) waveforms 5 and 30 minutes in the future using only prior SpO2 values as inputs. When tested on novel data, SWIFT predicts more than 80% and 60% of hypoxemic events in critically ill and COVID-19 patients, respectively. SWIFT also predicts SpO2 waveforms with average MSE below .0007. SWIFT provides information on both occurrence and magnitude of potential hypoxemic events 30 minutes in advance, allowing it to be used to inform clinical interventions, patient triaging, and optimal resource allocation. SWIFT may be used in clinical decision support systems to inform the management of critically ill patients during the COVID-19 pandemic and beyond.


Subject(s)
Heart Arrest , Critical Illness , COVID-19 , Hypoxia , Learning Disabilities , Brain Diseases
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