Digital Hospital and Patient Monitoring System

Diagnosis, and monitoring of health is a very important task in healthcare industry. Due to time constraint, people are not visiting hospitals, which might and possibly lead to a lot of health issues in one instant of time. Predominantly most of the healthcare systems have been developed to predict and diagnose the health of the patients by which people who are busy in their schedule can also monitor their health at regular intervals. Many studies show that early prediction is the best way to cure health because early diagnosis will help and alert the patients to know the health status. Healthcare being a global issue more particularly India being a most populated nation where majority of which live in villages deprived of healthcare facilities on real time basis continuously and regularly. With the increasing use of technology, there is an urgent need to have such a smart health monitoring system that can communicate between network devices and application which will help the patients and doctors to monitor, track and record the patient�s sensitive data containing medical information. This paper depicts the idea of solving health issues using the latest technology, Internet of Things (IoT). It presents the architectural review of smart healthcare system using Internet of Things(IoT) which is aimed to provide a Better HealthCare to everyone. Using this system architecture, patient�s body parameters can be measured in real time

DOCmT5: Document-Level Pretraining of Multilingual Language Models

In this paper, we introduce DOCmT5, a multilingual sequence-to-sequence language model pretrained with large scale parallel documents. While previous approaches have focused on leveraging sentence-level parallel data, we try to build a general-purpose pretrained model that can understand and generate long documents. We propose a simple and effective pretraining objective - Document reordering Machine Translation (DrMT), in which the input documents that are shuffled and masked need to be translated. DrMT brings consistent improvements over strong baselines on a variety of document-level generation tasks, including over 12 BLEU points for seen-language-pair document-level MT, over 7 BLEU points for unseen-language-pair document-level MT and over 3 ROUGE-1 points for seen-language-pair cross-lingual summarization. We achieve state-of-the-art (SOTA) on WMT20 De-En and IWSLT15 Zh-En document translation tasks. We also conduct extensive analysis on various factors for document pretraining, including (1) The effects of pretraining data quality and (2) The effects of combining mono-lingual and cross-lingual pretraining. We plan to make our model checkpoints publicly available.Comment: NAACL 2022 Finding

Anal mucosal melanoma presenting as per rectal bleed: a case report

Anorectal mucosal melanoma is a rare, malignant and aggressive tumor that usually presents late. It primarily arises from the melanocytes but can also arise from the mucosal surface. It also carries poor survival rates. Early diagnosis of the disease and prompt treatment is necessary. Overall 5-year survival rate for anal melanoma is below 10%. We present a case of a 77 years old male patient who presented with chief complaints of per rectal bleeding and decreased appetite. Patient’s symptoms were initially confused for benign conditions like hemorrhoids. He was diagnosed with anal mucosal melanoma on per rectal biopsy. Patient’s radiological investigations including PET scan and MRCP were suggestive of liver and lung metastasis. In view of the advanced stage of the disease, the decision was taken to treat the patient conservatively. He was started on imatinib therapy and a regular follow up was kept and palliative care was provided