scholarly journals Enabling Research and Clinical Use of Patient-Generated Health Data (the mindLAMP Platform): Digital Phenotyping Study

10.2196/30557 ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. e30557
Author(s):  
Aditya Vaidyam ◽  
John Halamka ◽  
John Torous
Keyword(s):  
Data Analysis ◽  
High Performance ◽  
Mobile Apps ◽  
Health Care Systems ◽  
Clinical Care ◽  
Data Representation ◽  
Health Data ◽  
Transport Layer ◽  
Web Standards ◽  
Wide Range

Background There is a growing need for the integration of patient-generated health data (PGHD) into research and clinical care to enable personalized, preventive, and interactive care, but technical and organizational challenges, such as the lack of standards and easy-to-use tools, preclude the effective use of PGHD generated from consumer devices, such as smartphones and wearables. Objective This study outlines how we used mobile apps and semantic web standards such as HTTP 2.0, Representational State Transfer, JSON (JavaScript Object Notation), JSON Schema, Transport Layer Security (version 1.3), Advanced Encryption Standard-256, OpenAPI, HTML5, and Vega, in conjunction with patient and provider feedback to completely update a previous version of mindLAMP. Methods The Learn, Assess, Manage, and Prevent (LAMP) platform addresses the abovementioned challenges in enhancing clinical insight by supporting research, data analysis, and implementation efforts around PGHD as an open-source solution with freely accessible and shared code. Results With a simplified programming interface and novel data representation that captures additional metadata, the LAMP platform enables interoperability with existing Fast Healthcare Interoperability Resources–based health care systems as well as consumer wearables and services such as Apple HealthKit and Google Fit. The companion Cortex data analysis and machine learning toolkit offer robust support for artificial intelligence, behavioral feature extraction, interactive visualizations, and high-performance data processing through parallelization and vectorization techniques. Conclusions The LAMP platform incorporates feedback from patients and clinicians alongside a standards-based approach to address these needs and functions across a wide range of use cases through its customizable and flexible components. These range from simple survey-based research to international consortiums capturing multimodal data to simple delivery of mindfulness exercises through personalized, just-in-time adaptive interventions.

scholarly journals Enabling Research and Clinical Use of Patient-Generated Health Data (the mindLAMP Platform): Digital Phenotyping Study (Preprint)

2021 ◽  
Author(s):  
Aditya Vaidyam ◽  
John Halamka ◽  
John Torous
Keyword(s):  
Data Analysis ◽  
High Performance ◽  
Mobile Apps ◽  
Health Care Systems ◽  
Clinical Care ◽  
Data Representation ◽  
Health Data ◽  
Transport Layer ◽  
Web Standards ◽  
Wide Range

BACKGROUND There is a growing need for the integration of patient-generated health data (PGHD) into research and clinical care to enable personalized, preventive, and interactive care, but technical and organizational challenges, such as the lack of standards and easy-to-use tools, preclude the effective use of PGHD generated from consumer devices, such as smartphones and wearables. OBJECTIVE This study outlines how we used mobile apps and semantic web standards such as HTTP 2.0, Representational State Transfer, JSON (JavaScript Object Notation), JSON Schema, Transport Layer Security (version 1.3), Advanced Encryption Standard-256, OpenAPI, HTML5, and Vega, in conjunction with patient and provider feedback to completely update a previous version of mindLAMP. METHODS The Learn, Assess, Manage, and Prevent (LAMP) platform addresses the abovementioned challenges in enhancing clinical insight by supporting research, data analysis, and implementation efforts around PGHD as an open-source solution with freely accessible and shared code. RESULTS With a simplified programming interface and novel data representation that captures additional metadata, the LAMP platform enables interoperability with existing Fast Healthcare Interoperability Resources–based health care systems as well as consumer wearables and services such as Apple HealthKit and Google Fit. The companion Cortex data analysis and machine learning toolkit offer robust support for artificial intelligence, behavioral feature extraction, interactive visualizations, and high-performance data processing through parallelization and vectorization techniques. CONCLUSIONS The LAMP platform incorporates feedback from patients and clinicians alongside a standards-based approach to address these needs and functions across a wide range of use cases through its customizable and flexible components. These range from simple survey-based research to international consortiums capturing multimodal data to simple delivery of mindfulness exercises through personalized, just-in-time adaptive interventions.

scholarly journals SLOW5: a new file format enables massive acceleration of nanopore sequencing data analysis

2021 ◽  
Author(s):  
Hasindu Gamaarachchi ◽  
Hiruna Samarakoon ◽  
Sasha P. Jenner ◽  
James M Ferguson ◽  
Timothy G. Amos ◽  
...  
Keyword(s):  
Data Analysis ◽  
High Performance ◽  
Nanopore Sequencing ◽  
File Format ◽  
Sequencing Data ◽  
Genome Wide ◽  
Wide Range ◽  
File Structure ◽  
Order Of Magnitude ◽  
Methylation Profiling

Nanopore sequencing is an emerging genomic technology with great potential. However, the storage and analysis of nanopore sequencing data have become major bottlenecks preventing more widespread adoption in research and clinical genomics. Here, we elucidate an inherent limitation in the file format used to store raw nanopore data, known as FAST5, that prevents efficient analysis on high-performance computing (HPC) systems. To overcome this we have developed SLOW5, an alternative file format that permits efficient parallelisation and, thereby, acceleration of nanopore data analysis. For example, we show that using SLOW5 format, instead of FAST5, reduces the time and cost of genome-wide DNA methylation profiling by an order of magnitude on common HPC systems, and delivers consistent improvements on a wide range of different architectures. With a simple, accessible file structure and a ~25% reduction in size compared to FAST5, SLOW5 format will deliver substantial benefits to all areas of the nanopore community.

scholarly journals SLOW5: a new file format enables massive acceleration of nanopore sequencing data analysis

2021 ◽  
Author(s):  
Hasindu Gamaarachchi ◽  
Hiruna Samarakoon ◽  
Sasha Jenner ◽  
James Ferguson ◽  
Timothy Amos ◽  
...  
Keyword(s):  
Data Analysis ◽  
High Performance ◽  
Nanopore Sequencing ◽  
File Format ◽  
Sequencing Data ◽  
Genome Wide ◽  
Wide Range ◽  
File Structure ◽  
Order Of Magnitude ◽  
Methylation Profiling

Abstract Nanopore sequencing is an emerging genomic technology with great potential. However, the storage and analysis of nanopore sequencing data have become major bottlenecks preventing more widespread adoption in research and clinical genomics. Here, we elucidate an inherent limitation in the file format used to store raw nanopore data – known as FAST5 – that prevents efficient analysis on high-performance computing (HPC) systems. To overcome this we have developed SLOW5, an alternative file format that permits efficient parallelisation and, thereby, acceleration of nanopore data analysis. For example, we show that using SLOW5 format, instead of FAST5, reduces the time and cost of genome-wide DNA methylation profiling by an order of magnitude on common HPC systems, and delivers consistent improvements on a wide range of different architectures. With a simple, accessible file structure and a ~25% reduction in size compared to FAST5, SLOW5 format will deliver substantial benefits to all areas of the nanopore community.

scholarly journals BIDS Apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods

2016 ◽  
Author(s):  
Krzysztof J. Gorgolewski ◽  
Fidel Alfaro-Almagro ◽  
Tibor Auer ◽  
Pierre Bellec ◽  
Mihai Capotă ◽  
...  
Keyword(s):  
Data Analysis ◽  
High Performance ◽  
Ease Of Use ◽  
Imaging Data ◽  
User Input ◽  
Analysis Methods ◽  
Wide Range ◽  
Neuroimaging Data ◽  
Magnetic Resonance Imaging Mri ◽  
Data Analysis Methods

AbstractThe rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness richness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.Author SummaryMagnetic Resonance Imaging (MRI) is a non-invasive way to measure human brain structure and activity that has been used for over 25 years. There are thousands MRI studies performed every year generating a substantial amount of data. At the same time, many new data analysis methods are being developed every year. The potential of using new analysis methods on the variety of existing and newly acquired data is hindered by difficulties in software deployment and lack of support for standardized input data. Here we propose to use container technology to make deployment of a wide range of data analysis techniques easy. In addition, we adapt the existing data analysis tools to interface with data organized in a standardized way. We hope that this approach will enable researchers to access a wider range of methods when analyzing their data which will lead to accelerated progress in human neuroscience.

Beveridge, Bismarck and Southern European Health Care Systems: Can We Decide Which is the Best in EU-15? A Statistical Analysis

2017 ◽  
Vol 1 (1) ◽  
pp. 41
Author(s):  
Angeliki Moisidou
Keyword(s):  
Health Care ◽  
Statistical Analysis ◽  
Empirical Analysis ◽  
Access To Health Care ◽  
High Performance ◽  
Social Protection ◽  
Health Care Systems ◽  
Health Sector ◽  
Economic Cost ◽  
Care Systems

A statistical analysis has been conducted with the aim to elucidate the effect of health care systems (HSs) on health inequalities assessed in terms of (a) differential access to health care services and (b) varying health outcomes among different models of HSs in EU-15 ((Beveridge: UK, IE, SE, FI, DK), (Bismarck: DE, FR, BE, LU, AT, NL), (Southern European model: GR, IT, ES, PT)). In the effort to interpret the results of the empirical analysis, we have ascertained systematic differences among the HSs in EU-15. Specifically, it is concluded that countries with Beveridge HS can be characterized more efficient (than average) in the most examined correlations, showing particularly high performance in the health sector. Similarly, countries with Bismarck HS record fairly satisfactory performance, but simultaneously they display more structural weaknesses compared with the Beveridge model. In addition, our empirical analysis has shown that adopting Bismarck model requires higher economic cost, compared with the Beveridge model, which is directly financed by taxation. On the contrary, in the countries with Southern European HS, the lowest performances are generally identified, which can be attributed to the residual social protection that characterizes these countries. The paper concludes with a synthesis of the empirical findings of our research. It proposes some directions for further research and presents a set of implications for policymakers regarding the planning and implementation of appropriate policies in order to tackle health inequality within HSs.

Interventions to change clinician behaviour in relation to suicide prevention care in the emergency department: A scoping review protocol

2020 ◽  
Author(s):  
Hwayeon Danielle Shin ◽  
Christine Cassidy ◽  
Janet Curran ◽  
Lori Weeks ◽  
Leslie Anne Campbell ◽  
...  
Keyword(s):  
Emergency Department ◽  
Suicide Prevention ◽  
Behaviour Change ◽  
Clinical Care ◽  
Grey Literature ◽  
Allied Health Professionals ◽  
Wide Range ◽  
Knowledge And Attitude ◽  
Clinician Behaviour ◽  
Google Search

Objective: This review aims to explore, characterize, and map the literature on interventions implemented to change emergency department (ED) clinicians’ behaviour related to suicide prevention using the Behaviour Change Wheel (BCW) as a guiding theoretical framework. Introduction: An ED is a critical place for suicide prevention. Yet, many patients who present with suicide-related thoughts and behaviours are discharged without proper assessment or appropriate treatment. Supporting clinicians (who provide direct clinical care, including nurses, physicians, allied health professionals) to make the desired behaviour change following evidence-based suicide prevention care is an essential step toward improving patient outcomes. However, reviews to date have yet to take a theoretical approach to investigate interventions implemented to change clinicians’ behaviour. Inclusion criteria: This review will consider literature that includes interventions that target ED clinicians’ behaviour change related to suicide prevention. Behaviour change refers to observable practice changes as well as proxy measures of behaviour change including knowledge and attitude. There are many ways in which an intervention can change clinicians’ behaviour (e.g., education, altering service delivery). This review will include a wide range of interventions that target behaviour change regardless of the type but exclude interventions that exclusively target patients.Methods: Multiple databases will be searched: PubMed, PsycInfo, CINAHL and Embase. We will also include grey literature, including Google search, ProQuest Dissertations and Theses Global, and Scopus conference papers. Full text of included studies will be reviewed, critically appraised and extracted. Extracted data will be coded to identify intervention functions using the BCW. Findings will be summarized in tables accompanied by narrative reports.

Validated Stability Indicating HPTLC Method for the Estimation of Amoxapine in Different Stress Conditions

2019 ◽  
Vol 15 (3) ◽  
pp. 273-279
Author(s):  
Shweta G. Rangari ◽  
Nishikant A. Raut ◽  
Pradip W. Dhore
Keyword(s):  
High Performance ◽  
Limit Of Detection ◽  
Degradation Products ◽  
Analysis Data ◽  
Peak Height ◽  
Good Resolution ◽  
Stability Indicating ◽  
Limit Of Quantitation ◽  
Wide Range ◽  
Hptlc Method

Background:The unstable and/or toxic degradation products may form due to degradation of drug which results into loss of therapeutic activity and lead to life threatening condition. Hence, it is important to establish the stability characteristics of drug in various conditions such as in temperature, light, oxidising agent and susceptibility across a wide range of pH values.Introduction:The aim of the proposed study was to develop simple, sensitive and economic stability indicating high performance thin layer chromatography (HPTLC) method for the quantification of Amoxapine in the presence of degradation products.Methods:Amoxapine and its degraded products were separated on precoated silica gel 60F254 TLC plates by using mobile phase comprising of methanol: toluene: ammonium acetate (6:3:1, v/v/v). The densitometric evaluation was carried out at 320 nm in reflectance/absorbance mode. The degradation products obtained as per ICH guidelines under acidic, basic and oxidative conditions have different Rf values 0.12, 0.26 and 0.6 indicating good resolution from each other and pure drug with Rf: 0.47. Amoxapine was found to be stable under neutral, thermal and photo conditions.Results:The method was validated as per ICH Q2 (R1) guidelines in terms of accuracy, precision, ruggedness, robustness and linearity. A good linear relationship between concentration and response (peak area and peak height) over the range of 80 ng/spot to 720 ng/spot was observed from regression analysis data showing correlation coefficient 0.991 and 0.994 for area and height, respectively. The limit of detection (LOD) and limit of quantitation (LOQ) for area were found to be 1.176 ng/mL and 3.565 ng/mL, whereas for height, 50.063 ng/mL and 151.707 ng/mL respectively.Conclusion:The statistical analysis confirmed the accuracy, precision and selectivity of the proposed method which can be effectively used for the analysis of amoxapine in the presence of degradation products.

Phytochemical, Analytical and Medicinal Studies of Holoptelea integrifolia Roxb. Planch - A Review

2019 ◽  
Vol 5 (4) ◽  
pp. 270-277 ◽  
Author(s):  
Vijay Kumar ◽  
Simranjeet Singh ◽  
Ragini Bhadouria ◽  
Ravindra Singh ◽  
Om Prakash
Keyword(s):  
Liquid Chromatography ◽  
Thin Layer ◽  
Thin Layer Chromatography ◽  
High Performance ◽  
Biologically Active ◽  
Toxicological Studies ◽  
Wide Range ◽  
Layer Chromatography

Holoptelea integrifolia Roxb. Planch (HI) has been used to treat various ailments including obesity, osteoarthritis, arthritis, inflammation, anemia, diabetes etc. To review the major phytochemicals and medicinal properties of HI, exhaustive bibliographic research was designed by means of various scientific search engines and databases. Only 12 phytochemicals have been reported including biologically active compounds like betulin, betulinic acid, epifriedlin, octacosanol, Friedlin, Holoptelin-A and Holoptelin-B. Analytical methods including the Thin Layer Chromatography (TLC), High-Performance Thin Layer Chromatography (HPTLC), High-Performance Liquid Chromatography (HPLC) and Liquid Chromatography With Mass Spectral (LC-MS) analysis have been used to analyze the HI. From medicinal potency point of view, these phytochemicals have a wide range of pharmacological activities such as antioxidant, antibacterial, anti-inflammatory, and anti-tumor. In the current review, it has been noticed that the mechanism of action of HI with biomolecules has not been fully explored. Pharmacology and toxicological studies are very few. This seems a huge literature gap to be fulfilled through the detailed in-vivo and in-vitro studies.

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