The Gynaecological Papyrus Kahun

The Papyrus Kahun is oldest known Egyptian medical document addressing issues of midwifery, dating back to the second Millennium BC. Here it follows a study of the papyrus, featuring hieroglyphic text and its transliteration and translation versions. This work also features commentaries regarding the papyrus’ medical substances and some linguistic evidences on the intimacy between spiritual and physical spheres in the Egyptian therapeutics. After the papyrus text, there is an Egyptian-English glossary.

Cannabis Personalization: Curating Personalized Cannabis Experiences Through Machine Learning

With the recent legalization of cannabis in Canada on October 17, 2018, the opportunity for emerging tech to complement and improve the cannabis experience is vast. The legalization of an industry that has been operating in the dark for decades means ample newfound opportunity for government and corporate-funded collaboration, development and research. A specific area of opportunity for growth within the cannabis sector is through personalization. Personalization is often performed via artificial intelligence—specifically machine learning—to develop a customized experience for users on various platforms. This is usually with the intention of targeted marketing. And while mass data collection serves the user by streamlining content to their assumed preferences, which then often directs them to businesses and products, product-tailoring still has vast potential for growth. Though a medical document for cannabis from a health practitioner may include broadband components to look out for, like “THC” and “CBD”, or even suggest ratios of those cannabinoids there is typically no specification on strain type and best consumption methods. Because the effects that cannabis has on a user varies from individual to individual and is dependent on not only their biometrics, but the various other terpenes and cannabinoids that exist in each strain beyond THC and CBD, cannabis users are missing out on opportunities to make the most of their use. Especially for those interested in cannabis to relieve specific symptoms, testing the vast amount of strains that exist and being able to identify the ideal product would be an arduous task on one’s own. Jibed is an app that would use the aggregation of user data to prescribe the most suitable strain of cannabis for that individual based on their specific conditions and body metrics. As the majority of the target audience (cannabis users in Canada) are already logged on to a multitude of data collecting apps (music, health, social, etc.), there is no shortage of data. The app would consider all the implications of the data, from one’s health to mood deduced from the music they're listening to -- just to name some -- in order to achieve optimal prescriptions.

Cannabis Personalization: Curating Personalized Cannabis Experiences Through Machine Learning

With the recent legalization of cannabis in Canada on October 17, 2018, the opportunity for emerging tech to complement and improve the cannabis experience is vast. The legalization of an industry that has been operating in the dark for decades means ample newfound opportunity for government and corporate-funded collaboration, development and research. A specific area of opportunity for growth within the cannabis sector is through personalization. Personalization is often performed via artificial intelligence—specifically machine learning—to develop a customized experience for users on various platforms. This is usually with the intention of targeted marketing. And while mass data collection serves the user by streamlining content to their assumed preferences, which then often directs them to businesses and products, product-tailoring still has vast potential for growth. Though a medical document for cannabis from a health practitioner may include broadband components to look out for, like “THC” and “CBD”, or even suggest ratios of those cannabinoids there is typically no specification on strain type and best consumption methods. Because the effects that cannabis has on a user varies from individual to individual and is dependent on not only their biometrics, but the various other terpenes and cannabinoids that exist in each strain beyond THC and CBD, cannabis users are missing out on opportunities to make the most of their use. Especially for those interested in cannabis to relieve specific symptoms, testing the vast amount of strains that exist and being able to identify the ideal product would be an arduous task on one’s own. Jibed is an app that would use the aggregation of user data to prescribe the most suitable strain of cannabis for that individual based on their specific conditions and body metrics. As the majority of the target audience (cannabis users in Canada) are already logged on to a multitude of data collecting apps (music, health, social, etc.), there is no shortage of data. The app would consider all the implications of the data, from one’s health to mood deduced from the music they're listening to -- just to name some -- in order to achieve optimal prescriptions.

A Pre-Training Technique to Localize Medical BERT and to Enhance Biomedical BERT

Background: Pre-training large-scale neural language models on raw texts has been shown to make a significant contribution to a strategy for transfer learning in natural language processing (NLP). With the introduction of transformer-based language models, such as Bidirectional Encoder Representations from Transformers (BERT), the performance of information extraction from free text by NLP has significantly improved for both the general domain and the medical domain; however, it is difficult for languages in which there are few publicly available medical databases with a high quality and a large size to train medical BERT models that perform well.Method: We introduce a method to train a BERT model using a small medical corpus both in English and in Japanese. Our proposed method consists of two interventions: simultaneous pre-training, which is intended to encourage masked language modeling and next-sentence prediction on the small medical corpus, and amplified vocabulary, which helps with suiting the small corpus when building the customized corpus by byte-pair encoding. Moreover, we used whole PubMed abstracts and developed a high-performance BERT model, Bidirectional Encoder Representations from Transformers for Biomedical Text Mining by Osaka University (ouBioBERT), in English via our method. We then evaluated the performance of our BERT models and publicly available baselines and compared them.Results: We confirmed that our Japanese medical BERT outperforms conventional baselines and the other BERT models in terms of the medical-document classification task and that our English BERT pre-trained using both the general and medical domain corpora performs sufficiently for practical use in terms of the biomedical language understanding evaluation (BLUE) benchmark. Moreover, ouBioBERT shows that the total score of the BLUE benchmark is 1.1 points above that of BioBERT and 0.3 points above that of the ablation model trained without our proposed method.Conclusions: Our proposed method makes it feasible to construct a practical medical BERT model in both Japanese and English, and it has a potential to produce higher performing models for biomedical shared tasks.

Ontology-Guided Data Augmentation for Medical Document Classification

Extracting meaningful features from unstructured text is one of the most challenging tasks in medical document classification. The various domain specific expressions and synonyms in the clinical discharge notes make it more challenging to analyse them. The case becomes worse for short texts such as abstract documents. These challenges can lead to poor classification accuracy. As the medical input data is often not enough in the real world, in this work a novel ontology-guided method is proposed for data augmentation to enrich input data. Then, three different deep learning methods are employed to analyse the performance of the suggested approach for classification. The experimental results show that the suggested approach achieved substantial improvement in the targeted medical documents classification.

Ontology-Guided Data Augmentation for Medical Document Classification

Extracting meaningful features from unstructured text is one of the most challenging tasks in medical document classification. The various domain specific expressions and synonyms in the clinical discharge notes make it more challenging to analyse them. The case becomes worse for short texts such as abstract documents. These challenges can lead to poor classification accuracy. As the medical input data is often not enough in the real world, in this work a novel ontology-guided method is proposed for data augmentation to enrich input data. Then, three different deep learning methods are employed to analyse the performance of the suggested approach for classification. The experimental results show that the suggested approach achieved substantial improvement in the targeted medical documents classification.

Religious review of Human Organs marketing, And donating status

This paper reviews some standpoints, deep research material and learning keys about organs transplantation, their donation and their economic status in light of Islamic teachings according to Quran, Sunnah, and fiqh(Mazahib e Arba). As we know that Organs transplantation in not a 20th century novelty. Indeed, it was known in one form or another even in prehistoric times. Ancient Hindu surgeons described methods for repai­ring defects of the nose and ears using auto grafts from the neighboring skin, a technique that remains to the present day. Susruta Sanhita, an old Indian medical document written in 700 BC, described the procedure later emulated by the Italian Tagliacozzi in the 16th century, and by British surgeons working in India in the 17th and 18th centuries, Now we also have to learn that if organs transplantation is a method that save humans life then on another hand it may open a wide gateway towards the human kidnapping and their killing crimes, just for earning some money which strongly effect human morals and its society, so that’s why Islam doesn’t support organ transplantation and their donation in each and every case except in some rare conditions which are mentioned in this article.