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General General

miRNASNP-v3: a comprehensive database for SNPs and disease-related variations in miRNAs and miRNA targets.

In Nucleic acids research ; h5-index 217.0

MicroRNAs (miRNAs) related single-nucleotide variations (SNVs), including single-nucleotide polymorphisms (SNPs) and disease-related variations (DRVs) in miRNAs and miRNA-target binding sites, can affect miRNA functions and/or biogenesis, thus to impact on phenotypes. miRNASNP is a widely used database for miRNA-related SNPs and their effects. Here, we updated it to miRNASNP-v3 (http://bioinfo.life.hust.edu.cn/miRNASNP/) with tremendous number of SNVs and new features, especially the DRVs data. We analyzed the effects of 7 161 741 SNPs and 505 417 DRVs on 1897 pre-miRNAs (2630 mature miRNAs) and 3'UTRs of 18 152 genes. miRNASNP-v3 provides a one-stop resource for miRNA-related SNVs research with the following functions: (i) explore associations between miRNA-related SNPs/DRVs and diseases; (ii) browse the effects of SNPs/DRVs on miRNA-target binding; (iii) functional enrichment analysis of miRNA target gain/loss caused by SNPs/DRVs; (iv) investigate correlations between drug sensitivity and miRNA expression; (v) inquire expression profiles of miRNAs and their targets in cancers; (vi) browse the effects of SNPs/DRVs on pre-miRNA secondary structure changes; and (vii) predict the effects of user-defined variations on miRNA-target binding or pre-miRNA secondary structure. miRNASNP-v3 is a valuable and long-term supported resource in functional variation screening and miRNA function studies.

Liu Chun-Jie, Fu Xin, Xia Mengxuan, Zhang Qiong, Gu Zhifeng, Guo An-Yuan

2020-Sep-29

Radiology Radiology

An extensible big data software architecture managing a research resource of real-world clinical radiology data linked to other health data from the whole Scottish population.

In GigaScience

AIM : To enable a world-leading research dataset of routinely collected clinical images linked to other routinely collected data from the whole Scottish national population. This includes more than 30 million different radiological examinations from a population of 5.4 million and >2 PB of data collected since 2010.

METHODS : Scotland has a central archive of radiological data used to directly provide clinical care to patients. We have developed an architecture and platform to securely extract a copy of those data, link it to other clinical or social datasets, remove personal data to protect privacy, and make the resulting data available to researchers in a controlled Safe Haven environment.

RESULTS : An extensive software platform has been developed to host, extract, and link data from cohorts to answer research questions. The platform has been tested on 5 different test cases and is currently being further enhanced to support 3 exemplar research projects.

CONCLUSIONS : The data available are from a range of radiological modalities and scanner types and were collected under different environmental conditions. These real-world, heterogenous data are valuable for training algorithms to support clinical decision making, especially for deep learning where large data volumes are required. The resource is now available for international research access. The platform and data can support new health research using artificial intelligence and machine learning technologies, as well as enabling discovery science.

Nind Thomas, Sutherland James, McAllister Gordon, Hardy Douglas, Hume Ally, MacLeod Ruairidh, Caldwell Jacqueline, Krueger Susan, Tramma Leandro, Teviotdale Ross, Abdelatif Mohammed, Gillen Kenny, Ward Joe, Scobbie Donald, Baillie Ian, Brooks Andrew, Prodan Bianca, Kerr William, Sloan-Murphy Dominic, Herrera Juan F R, McManus Dan, Morris Carole, Sinclair Carol, Baxter Rob, Parsons Mark, Morris Andrew, Jefferson Emily

2020-Sep-29

AI, Big Data, ML, Radiology

General General

Artificial Intelligence in Healthcare.

In Studies in health technology and informatics ; h5-index 23.0

Modern technology development created significant innovations in delivery of healthcare. Artificial intelligence as "a branch of computer science dealing with the simulation of intelligent behaviour in computers" when applied in health care resulted in intelligent support to decision-making, optimised business processes, increased quality, monitoring and delivering of personalised treatment plans and many other applications. Even though the benefits are clear and numerous, there are still open issues in creating automation of healthcare processes, ensuring data protection and integrity, reduction of medical waste etc. However, due to rapid development of AI techniques, more advances and improvements are still expected.

Ognjanovic Ivana

2020-Sep-25

Artificial intelligence, expert systems, machine learning, natural processing, speech recognition

General General

eHealth and Clinical Documentation Systems.

In Studies in health technology and informatics ; h5-index 23.0

eHealth is the use of modern information and communication technology (ICT) for trans-institutional healthcare purposes. Important subtopics of eHealth are health data sharing and telemedicine. Most of the clinical documentation to be shared is collected in patient records to support patient care. More sophisticated approaches to electronic patient records are trans-institutional or (inter-)national. Other aims for clinical documentation are quality management, reimbursement, legal issues, and medical research. Basic prerequisite for eHealth is interoperability, which can be divided into technical, semantic and process interoperability. There is a variety of international standards to support interoperability. Telemedicine is a subtopic of eHealth, which bridges spatial distance by using ICT for medical (inter-)actions. We distinguish telemedicine among healthcare professionals and telemedicine between health care professionals and patients. Both have a great potential to face the challenges of aging societies, the increasing number of chronically ill patients, multimorbidity and low number of physicians in remote areas. With ongoing digitalization more and more data are available digitally. Clinical documentation is an important source for big data analysis and artificial intelligence. The patient has an important role: Telemonitoring, wearable technologies, and smart home devices provide digital health data from daily life. These are high-quality data which can be used for medical decisions.

Knaup Petra, Benning Nils-Hendrik, Seitz Max Wolfgang, Eisenmann Urs

2020-Sep-25

eHealth, interoperability, medical documentation, patient records, telemedicine

General General

Healthcare Data Analytics.

In Studies in health technology and informatics ; h5-index 23.0

Health analytics is a branch of analysis that focuses on the analysis of complex and large amounts of health data that are characterized by high dimensionality, irregularities and rarities. Their aim is to improve and increase the efficiency of the process of healthcare providers, working with patients, managing costs and resources, improve diagnostic procedures and treatments, etc. The prime focus is investigating historical data and finding templates for different scenarios. As a final product, usually different visualisation tools are produced to support practitioners in patient care to provide better services, and to improve existing procedures.

Ognjanovic Ivana

2020-Sep-25

Health analytics, health data, machine learning, patient similarity, phenotyping, predictive modelling

General General

Using Machine Learning and Smartphone and Smartwatch Data to Detect Emotional States and Transitions: Exploratory Study.

In JMIR mHealth and uHealth

BACKGROUND : Emotional state in everyday life is an essential indicator of health and well-being. However, daily assessment of emotional states largely depends on active self-reports, which are often inconvenient and prone to incomplete information. Automated detection of emotional states and transitions on a daily basis could be an effective solution to this problem. However, the relationship between emotional transitions and everyday context remains to be unexplored.

OBJECTIVE : This study aims to explore the relationship between contextual information and emotional transitions and states to evaluate the feasibility of detecting emotional transitions and states from daily contextual information using machine learning (ML) techniques.

METHODS : This study was conducted on the data of 18 individuals from a publicly available data set called ExtraSensory. Contextual and sensor data were collected using smartphone and smartwatch sensors in a free-living condition, where the number of days for each person varied from 3 to 9. Sensors included an accelerometer, a gyroscope, a compass, location services, a microphone, a phone state indicator, light, temperature, and a barometer. The users self-reported approximately 49 discrete emotions at different intervals via a smartphone app throughout the data collection period. We mapped the 49 reported discrete emotions to the 3 dimensions of the pleasure, arousal, and dominance model and considered 6 emotional states: discordant, pleased, dissuaded, aroused, submissive, and dominant. We built general and personalized models for detecting emotional transitions and states every 5 min. The transition detection problem is a binary classification problem that detects whether a person's emotional state has changed over time, whereas state detection is a multiclass classification problem. In both cases, a wide range of supervised ML algorithms were leveraged, in addition to data preprocessing, feature selection, and data imbalance handling techniques. Finally, an assessment was conducted to shed light on the association between everyday context and emotional states.

RESULTS : This study obtained promising results for emotional state and transition detection. The best area under the receiver operating characteristic (AUROC) curve for emotional state detection reached 60.55% in the general models and an average of 96.33% across personalized models. Despite the highly imbalanced data, the best AUROC curve for emotional transition detection reached 90.5% in the general models and an average of 88.73% across personalized models. In general, feature analyses show that spatiotemporal context, phone state, and motion-related information are the most informative factors for emotional state and transition detection. Our assessment showed that lifestyle has an impact on the predictability of emotion.

CONCLUSIONS : Our results demonstrate a strong association of daily context with emotional states and transitions as well as the feasibility of detecting emotional states and transitions using data from smartphone and smartwatch sensors.

Sultana Madeena, Al-Jefri Majed, Lee Joon

2020-Sep-29

artificial intelligence, digital biomarkers, digital phenotyping, emotion detection, emotional transition detection, mHealth, mental health, mobile phone, spatiotemporal context, supervised machine learning