Notes

Use of X-Ray Fluorescence Microscopy with regard to Research upon Analysis Models of Hepatocellular Carcinoma.
Automated abstracts classification could significantly facilitate scientific literature screening. The classification of short texts could be based on their statistical properties. This research aimed to evaluate the quality of short medical abstracts classification primarily based on text statistical features. Twelve experiments with machine learning models over the sets of text features were performed on a dataset of 671 article abstracts. Each experiment was repeated 300 times to estimate the classification quality, ending up with 3600 tests total. We achieved the best result (F1 = 0.775) using a random forest machine learning model with keywords and three-dimensional Word2Vec embeddings. The classification of scientific abstracts might be implemented using straightforward and computationally inexpensive methods presented in this paper. The approach we described is expected to facilitate literature selection by researchers.Biomedical ontologies encode knowledge in a form that makes it computable. The current study used the integration of three large biomedical ontologies-the Disease Ontology (DO), Human Phenotype Ontology (HPO), and Radiology Gamuts Ontology (RGO)-to explore inferred causal relationships between high-level DO and HPO concepts. The principal DO categories were defined as the 7 direct subclasses of the top-level Disease class, excluding Disease of anatomical entity, plus the 12 direct subclasses of the latter term. The principal HPO categories were defined as the 25 direct subclasses of HPO's Phenotypic abnormality class. All causal relationships were tallied between members of the DO and HPO principal categories through their causal relationships in RGO. The analysis provides an understanding of the hierarchical organization of RGO terms, and offers insights into new relationships between DO and HPO classes.Medical artificial intelligence (AI) systems need to learn to recognize synonyms or paraphrases describing the same anatomy, disease, treatment, etc. to better understand real-world clinical documents. Existing linguistic resources focus on variants at the word or sentence level. buy SB239063 To handle linguistic variations on a broader scale, we proposed the Medical Text Radiology Report section Japanese version (MedTxt-RR-JA), the first clinical comparable corpus. MedTxt-RR-JA was built by recruiting nine radiologists to diagnose the same 15 lung cancer cases in Radiopaedia, an open-access radiological repository. The 135 radiology reports in MedTxt-RR-JA were shown to contain word-, sentence- and document-level variations maintaining similarity of contents. MedTxt-RR-JA is also the first publicly available Japanese radiology report corpus that would help to overcome poor data availability for Japanese medical AI systems. Moreover, our methodology can be applied widely to building clinical corpora without privacy concerns.Machine learning algorithms that derive predictive models are useful in predicting patient outcomes under uncertainty. These are often "population" algorithms which optimize a static model to predict well on average for individuals in the population; however, population models may predict poorly for individuals that differ from the average. Personalized machine learning algorithms seek to optimize predictive performance for every patient by tailoring a patient-specific model to each individual. Ensembles of decision trees often outperform single decision tree models, but ensembles of personalized models like decision paths have received little investigation. We present a novel personalized ensemble, called Lazy Random Forest (LazyRF), which consists of bagged randomized decision paths optimized for the individual for whom a prediction will be made. LazyRF outperformed single and bagged decision paths and demonstrated comparable predictive performance to a population random forest method in terms of discrimination on clinical and genomic data while also producing simpler models than the population random forest.Precision oncology is expected to improve selection of targeted therapies, tailored to individual patients and ultimately improve cancer patients' outcomes. Several cancer genetics knowledge databases have been successfully developed for such purposes, including CIViC and OncoKB, with active community-based curations and scoring of genetic-treatment evidences. Although many studies were conducted based on each knowledge base respectively, the integrative analysis across both knowledge bases remains largely unexplored. Thus, there exists an urgent need for a heterogeneous precision oncology knowledge resource with computational power to support drug repurposing discovery in a timely manner, especially for life-threatening cancer. In this pilot study, we built a heterogeneous precision oncology knowledge resource (POKR) by integrating CIViC and OncoKB, in order to incorporate unique information contained in each knowledge base and make associations amongst biomedical entities (e.g., gene, drug, disease) computable and measurable via training POKR graph embeddings. All the relevant codes, database dump files, and pre-trained POKR embeddings can be accessed through the following URL https//github.com/shenfc/POKR.The implementation of a reliable identity process is the basis of any secure patient information sharing system. Indeed, each individual is unique and should be identified by a unique number (identifier). It is with these issues in mind that we have designed and implemented a unique patient identification method adapted to the context of Burkina Faso. The recommended method is inspired by the French method based on the work of the Group for the Modernization of the Hospital Information System (GMSIH) [1]. The developed model allows to assign a "Unique Identifier" (PatientID) to each patient from his profile of identification features (name, date of birth, gender,…). The patient ID is a sequence of 20 characters plus a security "key" of 2 characters. A reliability test of the model has been performed to take into account identity anomalies (duplicate, collision).Substantial advances in methods of collecting and aggregating large amounts of biomedical data have been met with insufficient measures of protecting it from unwarranted access and use. Most of the current layers of protection are merely aimed at ensuring compliance with regulations (e.g., the EU's General Data Protection Regulation) but do not represent a vision of privacy-by-design as an efficient and ethical advantage in biomedical research and clinical applications. This not only slows down the pace of such efforts but also leaves the data exposed to a wide spectrum of cyberattacks. This work presents an overview of recent advancements in data and compuation security, along with a discussion of their limitations and potential for deployement in both health care and research settings.The expanded use of data is part of healthcare transformation that is underway in most countries around the world. While transformation is good for the advancement of healthcare, it presents new challenges for health information professionals. It is critical that the privacy of individual health information be protected throughout the transformation process. In this abstract, we explore how transformation is taking place in various countries and at different stages as paper-based records are digitized, as electronic health records are adopted, and as health data is used in new data-sharing methods for population health, analytics, and patient engagement. It is imperative for all health information stakeholders to learn about emerging trends and new rules that will impact their work to protect the privacy of health information in an increasingly digital, mobile, and global world. These requirements, and more are explored in the whitepaper Privacy of Health Information, an IFHIMA Global Perspective.The objective of this study was to investigate and analyze the most relevant aspects that influence the development and implementation of electronic informed consent for genetic studies. Interviews were conducted with experts in the area within our institution, the different informed consents available and the number of genetic studies requested in the last 5 years were analyzed. Professionals acknowledged the ethical dilemmas related to the genetic studies and the importance of having an electronic informed consent that not only provides the patient with the information necessary to understand the implications of the study, but also be flexible enough to adapt to the various genetic studies today. The development of informed consent is a challenge for health IT professionals, due to the complexity of the information it contains and the ethical implications it represents.The General Data Protection Regulation (GDPR) entered into force on May 25, 2018. Compliance with GDPR is especially relevant to the Digital Health (DH) domain, as it is common to process highly sensitive personal data regarding a person's health. However, GDPR compliance is a very challenging process since it requires implementing several technical and organizational measures to maintain compliance. With the aim to facilitate this process, we reviewed the published best practices in GDPR compliance. Then, we customized the findings to fit into the DH domain and created a toolkit for GDPR implementation and compliance. The Activity Planning Tool (APT) is provided as an example of how this toolkit could be utilized in new application development in mobile health in Austria. In the case of our APT, the toolkit was very helpful in integrating the GDPR technical requirements in addition to creating the corresponding compliance impact assessment, processing agreements, privacy policy, data flowcharts, and compliance checklists.WHO and UNICEF highlight vaccination as the most cost-effective method of prevention of infectious diseases. An effective public health strategy requires efficient tracking of vaccination to assess coverage, safety, and efficacy of these vaccines. Paper-based immunization records are still being used in most low and middle-income countries. Adequate Electronic Logistic Management Information Systems, Immunization Registries and Records are crucial for proper data collection and analysis, and for making better decisions at an individual and at a population level. In this paper we share our experience in the redesign of an interoperable immunization record to track vaccination, including the recently developed vaccines for the novel coronavirus SARS-CoV-2 (COVID-19).Dengue is a main public health issue around the world and is an epidemic in Brazil. As part of the Brazilian national program to fight the disease, every municipality has a Zoonosis Control Center responsible for health and case surveillance, among other actions. The fieldwork includes routine visiting of houses and strategic sites (e.g. industries and vacant lands), water sampling, container elimination, and larvicide administration. However, the field data are gathered and summarized by hand. In this work, our goal is to ease the collection and visualization of field data to support decision-making. We have developed a mobile system to collect and georeference field data which could then be used to build geospatial and geo-temporal visualizations of indices such as House, Container, and Breteau1 indices. This solution could enhance entomological surveillance and leverage action planning and evaluation.
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