The document describes Phenoflow, a clinical natural language processing tool for defining electronic health record (EHR)-based phenotypes. It discusses the need for clear and reusable phenotype definitions that connect a definition to its computable form. The document proposes Phenoflow's workflow-based phenotype model which separates phenotype logic from implementation. This model structure connects definitions to multiple computational implementations while prioritizing clarity and flexibility.
I gave this talk in the EDBT 2014 conference, which tool place in Athens, Greece.
I show how data examples can be used to characterize the behavior of scientific modules. I present a new methods that automatically generate the data examples, and show that such data examples are useful for the human user to understand the task of the modules, and that they can be used to assist curators in repairing broken workflows (i.e., workflows for which one or more modules are no longer supplied by their providers)
I gave this talk in the EDBT 2014 conference, which tool place in Athens, Greece.
I show how data examples can be used to characterize the behavior of scientific modules. I present a new methods that automatically generate the data examples, and show that such data examples are useful for the human user to understand the task of the modules, and that they can be used to assist curators in repairing broken workflows (i.e., workflows for which one or more modules are no longer supplied by their providers)
SBML FOR OPTIMIZING DECISION SUPPORT'S TOOLScsandit
Many theoretical works and tools on epidemiological field reflect the emphasis on decisionmaking
tools by both public health and the scientific community, which continues to increase.
Indeed, in the epidemiological field, modeling tools are proving a very important way in helping
to make decision. However, the variety, the large volume of data and the nature of epidemics
lead us to seek solutions to alleviate the heavy burden imposed on both experts and developers.
In this paper, we present a new approach: the passage of an epidemic model realized in Bio-
PEPA to a narrative language using the basics of SBML language. Our goal is to allow on one
hand, epidemiologists to verify and validate the model, and the other hand, developers to
optimize the model in order to achieve a better model of decision making. We also present some
preliminary results and some suggestions to improve the simulated model.
Bioinformatics may be defined as the field of science
in which biology, computer science, and information
technology merge to form a single discipline. Its ultimate
goal is to enable the discovery of new biological insights as
well as to create a global perspective from which unifying
principles in biology can be discerned by means of
bioinformatics tools for storing, retrieving, organizing and
analyzing biological data. Also most of these tools possess
very distinct features and capabilities making a direct
comparison difficult to be done. In this paper we propose
taxonomy for characterizing bioinformatics tools and briefly
surveys major bioinformatics tools under each categories.
Hopefully this study will stimulate other designers
and
experienced end users understand the details of particular
tool categories/tools, enabling them to make the best choices
for their particular research interests.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Novel Database-Centric Framework for Incremental Information Extractionijsrd.com
Information extraction (IE) has been an active research area that seeks techniques to uncover information from a large collection of text. IE is the task of automatically extracting structured information from unstructured and/or semi structured machine-readable documents. In most of the cases this activity concerns processing human language texts by means of natural language processing (NLP). Recent activities in document processing like automatic annotation and content extraction could be seen as information extraction. Many applications call for methods to enable automatic extraction of structured information from unstructured natural language text. Due to the inherent challenges of natural language processing, most of the existing methods for information extraction from text tend to be domain specific. In this project a new paradigm for information extraction. In this extraction framework, intermediate output of each text processing component is stored so that only the improved component has to be deployed to the entire corpus. Extraction is then performed on both the previously processed data from the unchanged components as well as the updated data generated by the improved component. Performing such kind of incremental extraction can result in a tremendous reduction of processing time and there is a mechanism to generate extraction queries from both labeled and unlabeled data. Query generation is critical so that casual users can specify their information needs without learning the query language.
ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : ...IJNSA Journal
In health research, one of the major tasks is to retrieve, and analyze heterogeneous databases containing
one single patient’s information gathered from a large volume of data over a long period of time. The
main objective of this paper is to represent our ontology-based information retrieval approach for
clinical Information System. We have performed a Case Study in the real life hospital settings. The results
obtained illustrate the feasibility of the proposed approach which significantly improved the information
retrieval process on a large volume of data over a long period of time from August 2011 until January
2012
Clustering and Classification of Cancer Data Using Soft Computing Technique IOSR Journals
Clustering and classification of cancer data has been used with success in field of medical side. In
this paper the two algorithm K-means and fuzzy C-means proposed for the comparison and find the accuracy of
the result. this paper address the problem of learning to classify the cancer data with two different method and
information derived from the training and testing .various soft computing based classification and show the
comparison of classification technique and classification of this health care data .this paper present the
accuracy of the result in cancer data.
A consistent and efficient graphical User Interface Design and Querying Organ...CSCJournals
We propose a software layer called GUEDOS-DB upon Object-Relational Database Management System ORDMS. In this work we apply it in Molecular Biology, more precisely Organelle complete genome. We aim to offer biologists the possibility to access in a unified way information spread among heterogeneous genome databanks. In this paper, the goal is firstly, to provide a visual schema graph through a number of illustrative examples. The adopted, human-computer interaction technique in this visual designing and querying makes very easy for biologists to formulate database queries compared with linear textual query representation.
TWO LEVEL SELF-SUPERVISED RELATION EXTRACTION FROM MEDLINE USING UMLSIJDKP
The biomedical research literature is one among many other domains that hides a precious knowledge, and
the biomedical community made an extensive use of this scientific literature to discover the facts of
biomedical entities, such as disease, drugs,etc.MEDLINE is a huge database of biomedical research
papers which remain a significantly underutilized source of biological information. Discovering the useful
knowledge from such huge corpus leads to various problems related to the type of information such as the
concepts related to the domain of texts and the semantic relationship associated with them. In this paper,
we propose a Two-level model for Self-supervised relation extraction from MEDLINE using Unified
Medical Language System (UMLS) Knowledge base. The model uses a Self-supervised Approach for
Relation Extraction (RE) by constructing enhanced training examples using information from UMLS. The
model shows a better result in comparison with current state of the art and naïve approaches
The Next Generation Open Targets PlatformHelenaCornu
The next-generation version of the Open Targets Platform — the culmination of two years of work — is now officially live! It replaces our previous version, with a fresh new look, brand new features, and streamlined processes.
It is available at platform.opentargets.org
This presentation goes through the main changes to the Platform, and introduces the new Open Targets Community forum. Join now at community.opentargets.org.
Open Targets is an innovative, large-scale, multi-year, public-private partnership that uses human genetics and genomics data for systematic drug target identification and prioritisation. Find out more at opentargets.org
Requirements Variability Specification for Data Intensive Software ijseajournal
Nowadays, the use of feature modeling technique, in software requirements specification, increased the variation support in Data Intensive Software Product Lines (DISPLs) requirements modeling. It is considered the easiest and the most efficient way to express commonalities and variability among different
products requirements. Several recent works, in DISPLs requirements, handled data variability by different models which are far from real world concepts. This,leaded to difficulties in analyzing, designing, implementing, and maintaining this variability. However, this work proposes a software requirements
specification methodology based on concepts more close to the nature and which are inspired from genetics. This bio-inspiration has carried out important results in DISPLs requirements variability specification with feature modeling, which were not approached by the conventional approaches.The feature model was enriched with features and relations, facilitating the requirements variation management, not yet considered in the current relevant works.The use of genetics-based methodology
seems to be promising in data intensive software requirements variability specification
Prof. Luciana Tricai Cavalini, MD, PhD. presents the Multi-Level Healthcare Information Modelling specifications for Third International Symposium on Foundations of Health Information Engineering and Systems (FHIES) 2013 conference. There is also a video on YouTube http://goo.gl/9QPW5x
It is based on the paper: "Use of XML Schema Definition for the Development of Semantically Interoperable Healthcare Applications" to be published in an upcoming issue of Springer LNCS.
AeHIN 28 August, 2014 - Innovation in Healthcare IT Standards: The Path to Bi...Timothy Cook
AeHIN Hour is our network's regular webinar where we feature topics on eHealth, HIS, and Civil Registration and Vital Statistics.
This presentation was from Dr. Luciana Cavalini, PhD. and Timothy Cook, MSc.
Profa. Luciana Tricai Cavalini, MD, PhD.
Luciana is a physician with PhD in Public Health. She is a Professor at the Department of Health Information Technologies, Medical Sciences College, Rio de Janeiro State University, Brazil and Professor at the Department of Epidemiology and Biostatistics, Community Health Institute, Fluminense Federal University, Brazil.
Luciana is also the Coordinator of the Technological Development Unit in Multilevel Healthcare Information Modeling and Coordinator of the Emergent Group in Research and Innovation on Healthcare Information Technologies. br.linkedin.com/pub/luciana-tricai-cavalini/88/8b6/533/en
Timothy Wayne Cook, MSc.
Tim is an Advanced Electronics Technologist with a MSc in Health Informatics.
He is the creator and core developer of the Multilevel Healthcare Information Modeling (MLHIM) specifications and Chief Technology Officer at MedWeb 3.0 (The Semantic Med Web). He also serves as International Collaborator at the National Institute of Science and Technology – Medicine Assisted by Scientific Computing, Brazil. https://www.linkedin.com/in/timothywaynecook
SBML FOR OPTIMIZING DECISION SUPPORT'S TOOLS cscpconf
Many theoretical works and tools on epidemiological field reflect the emphasis on decisionmaking tools by both public health and the scientific community, which continues to increase.
Indeed, in the epidemiological field, modeling tools are proving a very important way in helping to make decision. However, the variety, the large volume of data and the nature of epidemics
lead us to seek solutions to alleviate the heavy burden imposed on both experts and developers. In this paper, we present a new approach: the passage of an epidemic model realized in BioPEPA to a narrative language using the basics of SBML language. Our goal is to allow on one hand, epidemiologists to verify and validate the model, and the other hand, developers to
optimize the model in order to achieve a better model of decision making. We also present some preliminary results and some suggestions to improve the simulated model.
SBML FOR OPTIMIZING DECISION SUPPORT'S TOOLScsandit
Many theoretical works and tools on epidemiological field reflect the emphasis on decisionmaking
tools by both public health and the scientific community, which continues to increase.
Indeed, in the epidemiological field, modeling tools are proving a very important way in helping
to make decision. However, the variety, the large volume of data and the nature of epidemics
lead us to seek solutions to alleviate the heavy burden imposed on both experts and developers.
In this paper, we present a new approach: the passage of an epidemic model realized in Bio-
PEPA to a narrative language using the basics of SBML language. Our goal is to allow on one
hand, epidemiologists to verify and validate the model, and the other hand, developers to
optimize the model in order to achieve a better model of decision making. We also present some
preliminary results and some suggestions to improve the simulated model.
Bioinformatics may be defined as the field of science
in which biology, computer science, and information
technology merge to form a single discipline. Its ultimate
goal is to enable the discovery of new biological insights as
well as to create a global perspective from which unifying
principles in biology can be discerned by means of
bioinformatics tools for storing, retrieving, organizing and
analyzing biological data. Also most of these tools possess
very distinct features and capabilities making a direct
comparison difficult to be done. In this paper we propose
taxonomy for characterizing bioinformatics tools and briefly
surveys major bioinformatics tools under each categories.
Hopefully this study will stimulate other designers
and
experienced end users understand the details of particular
tool categories/tools, enabling them to make the best choices
for their particular research interests.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Novel Database-Centric Framework for Incremental Information Extractionijsrd.com
Information extraction (IE) has been an active research area that seeks techniques to uncover information from a large collection of text. IE is the task of automatically extracting structured information from unstructured and/or semi structured machine-readable documents. In most of the cases this activity concerns processing human language texts by means of natural language processing (NLP). Recent activities in document processing like automatic annotation and content extraction could be seen as information extraction. Many applications call for methods to enable automatic extraction of structured information from unstructured natural language text. Due to the inherent challenges of natural language processing, most of the existing methods for information extraction from text tend to be domain specific. In this project a new paradigm for information extraction. In this extraction framework, intermediate output of each text processing component is stored so that only the improved component has to be deployed to the entire corpus. Extraction is then performed on both the previously processed data from the unchanged components as well as the updated data generated by the improved component. Performing such kind of incremental extraction can result in a tremendous reduction of processing time and there is a mechanism to generate extraction queries from both labeled and unlabeled data. Query generation is critical so that casual users can specify their information needs without learning the query language.
ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : ...IJNSA Journal
In health research, one of the major tasks is to retrieve, and analyze heterogeneous databases containing
one single patient’s information gathered from a large volume of data over a long period of time. The
main objective of this paper is to represent our ontology-based information retrieval approach for
clinical Information System. We have performed a Case Study in the real life hospital settings. The results
obtained illustrate the feasibility of the proposed approach which significantly improved the information
retrieval process on a large volume of data over a long period of time from August 2011 until January
2012
Clustering and Classification of Cancer Data Using Soft Computing Technique IOSR Journals
Clustering and classification of cancer data has been used with success in field of medical side. In
this paper the two algorithm K-means and fuzzy C-means proposed for the comparison and find the accuracy of
the result. this paper address the problem of learning to classify the cancer data with two different method and
information derived from the training and testing .various soft computing based classification and show the
comparison of classification technique and classification of this health care data .this paper present the
accuracy of the result in cancer data.
A consistent and efficient graphical User Interface Design and Querying Organ...CSCJournals
We propose a software layer called GUEDOS-DB upon Object-Relational Database Management System ORDMS. In this work we apply it in Molecular Biology, more precisely Organelle complete genome. We aim to offer biologists the possibility to access in a unified way information spread among heterogeneous genome databanks. In this paper, the goal is firstly, to provide a visual schema graph through a number of illustrative examples. The adopted, human-computer interaction technique in this visual designing and querying makes very easy for biologists to formulate database queries compared with linear textual query representation.
TWO LEVEL SELF-SUPERVISED RELATION EXTRACTION FROM MEDLINE USING UMLSIJDKP
The biomedical research literature is one among many other domains that hides a precious knowledge, and
the biomedical community made an extensive use of this scientific literature to discover the facts of
biomedical entities, such as disease, drugs,etc.MEDLINE is a huge database of biomedical research
papers which remain a significantly underutilized source of biological information. Discovering the useful
knowledge from such huge corpus leads to various problems related to the type of information such as the
concepts related to the domain of texts and the semantic relationship associated with them. In this paper,
we propose a Two-level model for Self-supervised relation extraction from MEDLINE using Unified
Medical Language System (UMLS) Knowledge base. The model uses a Self-supervised Approach for
Relation Extraction (RE) by constructing enhanced training examples using information from UMLS. The
model shows a better result in comparison with current state of the art and naïve approaches
The Next Generation Open Targets PlatformHelenaCornu
The next-generation version of the Open Targets Platform — the culmination of two years of work — is now officially live! It replaces our previous version, with a fresh new look, brand new features, and streamlined processes.
It is available at platform.opentargets.org
This presentation goes through the main changes to the Platform, and introduces the new Open Targets Community forum. Join now at community.opentargets.org.
Open Targets is an innovative, large-scale, multi-year, public-private partnership that uses human genetics and genomics data for systematic drug target identification and prioritisation. Find out more at opentargets.org
Requirements Variability Specification for Data Intensive Software ijseajournal
Nowadays, the use of feature modeling technique, in software requirements specification, increased the variation support in Data Intensive Software Product Lines (DISPLs) requirements modeling. It is considered the easiest and the most efficient way to express commonalities and variability among different
products requirements. Several recent works, in DISPLs requirements, handled data variability by different models which are far from real world concepts. This,leaded to difficulties in analyzing, designing, implementing, and maintaining this variability. However, this work proposes a software requirements
specification methodology based on concepts more close to the nature and which are inspired from genetics. This bio-inspiration has carried out important results in DISPLs requirements variability specification with feature modeling, which were not approached by the conventional approaches.The feature model was enriched with features and relations, facilitating the requirements variation management, not yet considered in the current relevant works.The use of genetics-based methodology
seems to be promising in data intensive software requirements variability specification
Prof. Luciana Tricai Cavalini, MD, PhD. presents the Multi-Level Healthcare Information Modelling specifications for Third International Symposium on Foundations of Health Information Engineering and Systems (FHIES) 2013 conference. There is also a video on YouTube http://goo.gl/9QPW5x
It is based on the paper: "Use of XML Schema Definition for the Development of Semantically Interoperable Healthcare Applications" to be published in an upcoming issue of Springer LNCS.
AeHIN 28 August, 2014 - Innovation in Healthcare IT Standards: The Path to Bi...Timothy Cook
AeHIN Hour is our network's regular webinar where we feature topics on eHealth, HIS, and Civil Registration and Vital Statistics.
This presentation was from Dr. Luciana Cavalini, PhD. and Timothy Cook, MSc.
Profa. Luciana Tricai Cavalini, MD, PhD.
Luciana is a physician with PhD in Public Health. She is a Professor at the Department of Health Information Technologies, Medical Sciences College, Rio de Janeiro State University, Brazil and Professor at the Department of Epidemiology and Biostatistics, Community Health Institute, Fluminense Federal University, Brazil.
Luciana is also the Coordinator of the Technological Development Unit in Multilevel Healthcare Information Modeling and Coordinator of the Emergent Group in Research and Innovation on Healthcare Information Technologies. br.linkedin.com/pub/luciana-tricai-cavalini/88/8b6/533/en
Timothy Wayne Cook, MSc.
Tim is an Advanced Electronics Technologist with a MSc in Health Informatics.
He is the creator and core developer of the Multilevel Healthcare Information Modeling (MLHIM) specifications and Chief Technology Officer at MedWeb 3.0 (The Semantic Med Web). He also serves as International Collaborator at the National Institute of Science and Technology – Medicine Assisted by Scientific Computing, Brazil. https://www.linkedin.com/in/timothywaynecook
SBML FOR OPTIMIZING DECISION SUPPORT'S TOOLS cscpconf
Many theoretical works and tools on epidemiological field reflect the emphasis on decisionmaking tools by both public health and the scientific community, which continues to increase.
Indeed, in the epidemiological field, modeling tools are proving a very important way in helping to make decision. However, the variety, the large volume of data and the nature of epidemics
lead us to seek solutions to alleviate the heavy burden imposed on both experts and developers. In this paper, we present a new approach: the passage of an epidemic model realized in BioPEPA to a narrative language using the basics of SBML language. Our goal is to allow on one hand, epidemiologists to verify and validate the model, and the other hand, developers to
optimize the model in order to achieve a better model of decision making. We also present some preliminary results and some suggestions to improve the simulated model.
FAIR Data and Model Management for Systems Biology(and SOPs too!)Carole Goble
MultiScale Biology Network Springboard meeting, Nottingham, UK, 1 June 2015
FAIR Data and model management for Systems Biology
Over the past 5 years we have seen a change in expectations for the management of all the outcomes of research – that is the “assets” of data, models, codes, SOPs and so forth. Don’t stop reading. Yes, data management isn’t likely to win anyone a Nobel prize. But publications should be supported and accompanied by data, methods, procedures, etc. to assure reproducibility of results. Funding agencies expect data (and increasingly software) management retention and access plans as part of the proposal process for projects to be funded. Journals are raising their expectations of the availability of data and codes for pre- and post- publication. And the multi-component, multi-disciplinary nature of Systems Biology demands the interlinking and exchange of assets and the systematic recording of metadata for their interpretation.
Data and model management for the Systems Biology community is a multi-faceted one including: the development and adoption appropriate community standards (and the navigation of the standards maze); the sustaining of international public archives capable of servicing quantitative biology; and the development of the necessary tools and know-how for researchers within their own institutes so that they can steward their assets in a sustainable, coherent and credited manner while minimizing burden and maximising personal benefit.
The FAIRDOM (Findable, Accessible, Interoperable, Reusable Data, Operations and Models) Initiative has grown out of several efforts in European programmes (SysMO and EraSysAPP ERANets and the ISBE ESRFI) and national initiatives (de.NBI, German Virtual Liver Network, SystemsX, UK SynBio centres). It aims to support Systems Biology researchers with data and model management, with an emphasis on standards smuggled in by stealth.
This talk will use the FAIRDOM Initiative to discuss the FAIR management of data, SOPs, and models for Sys Bio, highlighting the challenges multi-scale biology presents.
http://www.fair-dom.org
http://www.fairdomhub.org
http://www.seek4science.org
If you're looking to land a technical job, then you already know that the interview process can be rigorous and challenging. That's why we've created an eBook that focuses on the Top 30 Technical Interview Questions that you're likely to face during the interview process.
Our eBook is a comprehensive guide that covers all the technical aspects of the interview, including software development, database administration, system administration, and much more. With each question, we've included a detailed explanation of the concept and a step-by-step solution to help you answer the question with confidence.
The Top 30 Technical Interview Questions eBook is perfect for anyone who is preparing for a technical interview. Whether you're a student, a recent graduate, or an experienced professional, our eBook will provide you with the knowledge and skills you need to succeed in your interview.
Presentation of Waseda university openEHR meeting, regarding implementing archetype based systems. This presentation also includes an explanation of MML-openEHR project and IBIME group developments.
Implementing an HL7 version 3 modeling tool from an Ecore modelSnow Owl
One of the main challenges of achieving interoperability using the HL7 V3 healthcare standard is the lack of clear definition and supporting tools for modeling, testing, and conformance checking. Currently, the knowledge defining the modeling is scattered around in MIF schemas, tools and specifications or simply with the domain experts. Modeling core HL7 concepts, constraints, and semantic relationships in Ecore/EMF encapsulates the domain-specific knowledge in a transparent way while unifying Java, XML, and UML in an abstract, high-level representation. Moreover, persisting and versioning the core HL7 concepts as a single Ecore context allows modelers and implementers to create, edit and validate message models against a single modeling context. The solution discussed in this paper is implemented in the new HL7 Static Model Designer as an extensible toolset integrated as a standalone Eclipse RCP application.
Please see our website http://b2i.sg for further information.
Resource Description Framework Approach to Data Publication and FederationPistoia Alliance
Bob Stanley, CEO, IO Informatics, explains the utility to RDF as a standard way of defining and redefining data that could have utility in managing life science information.
There are many design principles that have become best practices over the years. Using these principles we can develop scalable and maintainable enterprise application with time tested approaches.
We consider knowledge as a refined kind of information, more general than that found in convention databases. But it may be incomplete or fuzzy as well. We may think of knowledge as a collection of related facts, procedures, models and heuristics that can be used in problem solving or inference systems.[
Reference Domain Ontologies and Large Medical Language Models.pptxChimezie Ogbuji
Large Language Models (LLMs) have exploded into the modern research and development consciousness and triggered an artificial intelligence revolution. They are well-positioned to have a major impact on Medical Informatics. However, much of the data used to train these revolutionary models are general-purpose and, in some cases, synthetically generated from LLMs. Ontologies are a shared and agreed-upon conceptualization of a domain and facilitate computational reasoning. They have become important tools in biomedicine, supporting critical aspects of healthcare and biomedical research, and are integral to science. In this talk, we will delve into ontologies, their representational and reasoning power, and how terminology systems such as SNOMED-CT, an international master terminology providing comprehensive coverage of the entire domain of medicine, can be used with Controlled Natural Languages (CNL) to advance how LLMs are used and trained.
Principles of Health Informatics: Artificial intelligence and machine learningMartin Chapman
Principles of Health Informatics: Artificial intelligence and machine learning. Last delivered in 2024. All educational material listed or linked to on these pages in relation to King's College London may be provided for reference only, and therefore does not necessarily reflect the current course content.
Principles of Health Informatics: Clinical decision support systemsMartin Chapman
Principles of Health Informatics: Clinical decision support systems. Last delivered in 2024. All educational material listed or linked to on these pages in relation to King's College London may be provided for reference only, and therefore does not necessarily reflect the current course content.
Principles of Health Informatics: Evaluating medical softwareMartin Chapman
Principles of Health Informatics: Evaluating medical software. Last delivered in 2023. All educational material listed or linked to on these pages in relation to King's College London may be provided for reference only, and therefore does not necessarily reflect the current course content.
Principles of Health Informatics: Usability of medical softwareMartin Chapman
Principles of Health Informatics: Usability of medical software. Last delivered in 2023. All educational material listed or linked to on these pages in relation to King's College London may be provided for reference only, and therefore does not necessarily reflect the current course content.
Principles of Health Informatics: Social networks, telehealth, and mobile healthMartin Chapman
Principles of Health Informatics: Social networks, telehealth, and mobile health. Last delivered in 2023. All educational material listed or linked to on these pages in relation to King's College London may be provided for reference only, and therefore does not necessarily reflect the current course content.
Principles of Health Informatics: Communication systems in healthcareMartin Chapman
Principles of Health Informatics: Communication systems in healthcare. Last delivered in 2023. All educational material listed or linked to on these pages in relation to King's College London may be provided for reference only, and therefore does not necessarily reflect the current course content.
Principles of Health Informatics: Terminologies and classification systemsMartin Chapman
Principles of Health Informatics: Terminologies and classification systems. Last delivered in 2023. All educational material listed or linked to on these pages in relation to King's College London may be provided for reference only, and therefore does not necessarily reflect the current course content.
Principles of Health Informatics: Representing medical knowledgeMartin Chapman
Principles of Health Informatics: Representing medical knowledge. Last delivered in 2023. All educational material listed or linked to on these pages in relation to King's College London may be provided for reference only, and therefore does not necessarily reflect the current course content.
Principles of Health Informatics: Informatics skills - searching and making d...Martin Chapman
Principles of Health Informatics: Informatics skills - searching and making decisions. Last delivered in 2023. All educational material listed or linked to on these pages in relation to King's College London may be provided for reference only, and therefore does not necessarily reflect the current course content.
Principles of Health Informatics: Informatics skills - communicating, structu...Martin Chapman
Principles of Health Informatics: Informatics skills - communicating, structuring, and questioning. Last delivered in 2023. All educational material listed or linked to on these pages in relation to King's College London may be provided for reference only, and therefore does not necessarily reflect the current course content.
Principles of Health Informatics: Models, information, and information systemsMartin Chapman
Principles of Health Informatics: Models, information, and information systems. Last delivered in 2023. All educational material listed or linked to on these pages in relation to King's College London may be provided for reference only, and therefore does not necessarily reflect the current course content.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
1. Phenoflow
Clinical Natural Language Processing Group, University of Edinburgh
Martin Chapman, Research Fellow in Phenomics (GSTT BRC and HDR UK)
King’s College London
4. Definition: EHR-based phenotype definition i
An electronic health record (EHR)-based phenotype definition is an abstract specification
that details how to extract a cohort of patients from a set of health records who all exhibit the
same disease or condition.
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5. Definition: EHR-based phenotype definition ii
Table 1: Phenotype definition formats
Format Description Example Category
Code list A set of codes that must exist in a pa-
tient’s health record in order to include
them within a phenotype cohort
COVID-19 ICD-10 code U07.1 Rule-based
Simple data ele-
ments
Formalising the relationship between
code-based data elements using logical
connectives
COVID-19 ICD-10 code U07.1
AND ICD-11 code RA01.0
Rule-based
Complex data ele-
ments
Formalising the relationship between
complex data elements, such as those de-
rived via NLP.
Patient’s blood pressure reading >
140 OR patient notes contain ‘high
BP’
Rule-based
Temporal Prefix rules with temporal qualifiers Albumin levels increased by 25%
over 6 hours, high blood pressure
reading has to occur during hospi-
talisation.
Rule-based
Trained classifier Use rule-based definitions as the basis for
constructing a classifier for future (or ad-
ditional) cohorts
A k-fold cross validated classifier
capable of identifying COVID-19
patients
Probabilistic
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6. Definition: Computable phenotype i
Each definition is realised as one or more computable phenotypes for a given dataset (e.g. an
SQL script, Python code, etc.).
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7. Definition: Computable phenotype ii
A Type 2 Diabetes (T2DM) phenotype:
SELECT UserID, COUNT(DISTINCT AbnormalLab) AS abnormal lab
FROM Patients
GROUP BY UserID
HAVING abnormal lab > 0;
...
Definition Computable forms
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8. Phenotype definition landscape i
portal.caliberresearch.org phekb.org
Computable form often omitted – this makes it unclear how to implementation and execute
a definition in practice against a dataset, particularly for non-technical users.
(We’ll revisit CALIBER’s implementation tab shortly.)
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9. Phenotype definition landscape ii
Conversely, if included, the definition and computable form are often conflated as a single
executable - an R script on Github is not suitably abstract to be a phenotype definition itself.
Chapman, Martin, et al. ”Desiderata for the development of next-generation electronic health
record phenotype libraries.” GigaScience, 2021.
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10. Phenotype definition landscape iii
Otherwise simple definitions are often made complex by idiosyncratic terminology and a
convoluted structure.
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11. Phenotype definition landscape iv
https://data.ohdsi.org/PhenotypeLibrary
Tied to a single standard, e.g. OHDSI’s gold standard phenotype library and the OMOP
CDM.
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12. Why are these things a problem?
We want to be able to reuse definitions as much as possible, to enable cohorts of patients with
a given condition to be identified as efficiently and consistently as possible, within the same
domain (e.g. research, clinical trials, decision-support).
• We are not looking for a single, canonical version of each definition across domains – it is
perfectly possible for there to be multiple definitions for the same condition depending
on use case.
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13. Why are these things a problem?
We want to be able to reuse definitions as much as possible, to enable cohorts of patients with
a given condition to be identified as efficiently and consistently as possible, within the same
domain (e.g. research, clinical trials, decision-support).
• We are not looking for a single, canonical version of each definition across domains – it is
perfectly possible for there to be multiple definitions for the same condition depending
on use case.
The current landscape is not always conducive to reuse:
• The lack of a computable form, or guidance on how to derive one, reduces definition
portability (the ease with which a definition can be implemented).
• A convoluted structure reduces definition reproducibility (the accuracy with which a
definition can be implemented).
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15. Phenotype models i
Phenotype models govern the information required for, and the structure of, phenotype
definitions.
• They may, for example, govern the logical connectives available to a definition author
when producing a definition (e.g. conjunction and disjunction).
• Many definitions have an inherent model, such as the fields that are required when the
definitions is stored in a phenotype library.
• Models may also be derived from existing (non-executable) modelling languages, such as
the Clinical Quality Language (CQL).
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16. Phenotype models ii
l i b r a r y ”PhEMA Heart F a i l u r e ” v e r s i o n ’ 1 . 0 . 0 ’
using QUICK
codesystem ”ActCodes ”: ’ http :// hl7 . org / f h i r /v3/ActCode ’
v a l u e s e t ”Echo VS”: ’ 2 . 1 6 . 8 4 0 . . . ’
v a l u e s e t ”HF Dx VS”: ’ 2 . 1 6 . 8 4 0 . . . ’
code ” I n p a t i e n t Encounter ”: ’IMP ’ from ”ActCodes”
code ” Outpatient Encounter ”: ’AMB’ from ”ActCodes”
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17. Phenotype model requirements
While a standard structure goes some way towards improving definition clarity, the use of an
explicit phenotype model can help address many of the issues we’ve seen, but to be
effective, a model must fulfil certain requirements:
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18. Phenotype model requirements
While a standard structure goes some way towards improving definition clarity, the use of an
explicit phenotype model can help address many of the issues we’ve seen, but to be
effective, a model must fulfil certain requirements:
1. Needs to connect, yet keep distinct, a phenotype definition and its computable form.
1.1 A definition needs to remain suitably abstract while making provision for an associated
computable counterpart. Ideally facilitate one-to-many connectivity, connecting with
multiple implementations of the same logic.
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19. Phenotype model requirements
While a standard structure goes some way towards improving definition clarity, the use of an
explicit phenotype model can help address many of the issues we’ve seen, but to be
effective, a model must fulfil certain requirements:
1. Needs to connect, yet keep distinct, a phenotype definition and its computable form.
1.1 A definition needs to remain suitably abstract while making provision for an associated
computable counterpart. Ideally facilitate one-to-many connectivity, connecting with
multiple implementations of the same logic.
2. Needs to prioritise clarity to combat the complexity of definitions.
13
20. Phenotype model requirements
While a standard structure goes some way towards improving definition clarity, the use of an
explicit phenotype model can help address many of the issues we’ve seen, but to be
effective, a model must fulfil certain requirements:
1. Needs to connect, yet keep distinct, a phenotype definition and its computable form.
1.1 A definition needs to remain suitably abstract while making provision for an associated
computable counterpart. Ideally facilitate one-to-many connectivity, connecting with
multiple implementations of the same logic.
2. Needs to prioritise clarity to combat the complexity of definitions.
3. Support a variety of target data formats.
13
21. Phenotype model requirements
While a standard structure goes some way towards improving definition clarity, the use of an
explicit phenotype model can help address many of the issues we’ve seen, but to be
effective, a model must fulfil certain requirements:
1. Needs to connect, yet keep distinct, a phenotype definition and its computable form.
1.1 A definition needs to remain suitably abstract while making provision for an associated
computable counterpart. Ideally facilitate one-to-many connectivity, connecting with
multiple implementations of the same logic.
2. Needs to prioritise clarity to combat the complexity of definitions.
3. Support a variety of target data formats.
4. Accommodate (and potentially combine) all definitions types.
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22. Phenoflow workflow-based model i
Phenoflow workflow-based phenotypes are a step of sequential steps, which effectively
transition a population of patients to a cohort that exhibit the condition captured.
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23. Phenoflow workflow-based model ii
Each step in the model consists of three layers:
• Abstract Expresses the logic of that step. Says nothing about implementation.
• Functional Specifies the inputs to, and outputs from, this step (metadata) e.g., the
format of an intermediate cohort.
• Computational Defines an environment for the execution of one or more
implementation units (e.g. a script, data pipeline module, etc.).
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24. Phenoflow workflow-based model iii
number group id description type
step
Input Output
id description id description extensionA
pathA languageA paramsA
implementationUnitA
Computational
Implementation
Units
pathB languageB paramsB
implementationUnitB
Abstract
Functional
Figure 1: Structured phenotype definition model (step) and implementation units.
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25. (1) Separate, yet connect, a phenotype definition with its computable form
The separation of the model into logic and implementation layers provides the required
connectivity with a computable form, without affecting abstraction:
2 - icd10 A case is identified in the presence of pa-
tients associated with the stated icd10
COVID-19 codes.
logic
step
Input Output
covid19 cohort Potential covid19
cases.
covid19 cases icd10 covid19 cases, as
identified by icd10
coding.
csv
icd10.py python -
for row in c s v r e a d e r :
newRow = row . copy ()
for c e l l in row :
i f [ value for value in
row [ c e l l ] . s p l i t ( ” , ” )
i f value in codes ] :
newRow [ ” covid19 ” ] = ”CASE”
...
Computational
Implementation
Units
icd10.js javascript -
for ( row of csvData ){
newRow = row . s l i c e ( ) ;
for ( c e l l of row ){
i f ( c e l l . s p l i t ( ” , ” )
. f i l t e r ( code=>codes .
indexOf ( code )>−1). length ){
newRow . push ( ”CASE” ) ;
...
Abstract
Functional
Figure 2: Individual step of COVID-19 code-based Phenoflow definition and implementation units.
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26. (2) Prioritise clarity to combat the complexity of definitions
On top of definitions now having an expected structure, separation into steps provides a logical
flow.
Each step provides three descriptions of the functionality it contains, to aid clarity:
1. A single ID, providing an overview of the step’s functionality.
2. A longer description of the functionality contained within the step.
3. A classification of the step under a pre-defined ontology, so that even if the ID and
description are not sufficient, a general understanding of the functionality of the step can
still be extracted1
.
Inputs and outputs to each step provide further information.
1As of now we still use simple classification, e.g. logic, but finer a granularity of types is forthcoming.
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27. (3) Support a variety of target data formats
We add additional contraints to the workflow-based structure to dictate that the first step in a
definition is always a data read (and the last is always a cohort output).
Because of the modularity of the model structure, we are able to swap in and out the logic,
and associated implementation, of the data read step – while the other steps remain
unchanged – in order to accommodate multiple data formats.
More on this connector approach shortly.
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28. (4) Accommodate (and potentially combine) all definitions types i
The generality of the model allows it to capture information relating to a wide range of
different definition types.
Similarly, the separation of logic into steps, with clear inputs and outputs, makes each step
self-contained, allowing types to be mixed within a single definition.
• One step may identify patients based on a list of codes, while a subsequent step may
describe the use of more complex NLP techniques in order to identify patients.
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29. (4) Accommodate (and potentially combine) all definitions types ii
1 case assignment rx t2dm med-
abnormal lab
A case is identified in the presence of
an abnormal lab value (defined as one
of three different exacerbations in blood
sugar level) AND if medication for this
type of diabetes has been prescribed.
boolean
step
Input Output
dm cohort-
abnormal lab
Potential t2dm
cases, with ab-
normal lab results
identified
dm cases-
case assignment 1
t2dm cases, as iden-
tified by first case as-
signment rule
csv
rx t2dm med-abnormal lab.knwf knime -File= Computational
Implementation
Units
rx t2dm med-abnormal lab.py python -
...
i f row [ ” t1dm dx cnt ” ] == ”0”
and row [ ” t2dm dx cnt ” ] == ”0”
and ” t2dm rx dts ” in c s v r e a d e r . fieldnames
and ” abnormal lab ” in c s v r e a d e r . fieldnames
and row [ ” abnormal lab ” ] == ”1” :
row [ ”t2dm” ] = ”CASE” ;
...
Abstract
Functional
Figure 3: Individual step of T2DM logic-based Phenoflow definition and implementation units.
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31. Phenoflow web architecture
The Phenoflow model is complemented by a web architecture that accentuates its benefits.
Chapman, Martin, et al. “Phenoflow: A microservice architecture for portable workflow-based
phenotype definitions.” AMIA, 2021. 22
32. Phenoflow web architecture
The Phenoflow model is complemented by a web architecture that accentuates its benefits.
Web
Portal
Generator
Visualiser
Implementation
Units
Author(s)
User
customise
author,
expand
data
workflow
visualisation
workflow
workflow,
visualisation,
implementation units
Chapman, Martin, et al. “Phenoflow: A microservice architecture for portable workflow-based
phenotype definitions.” AMIA, 2021. 22
33. Aside: Microservice architectures
We separate any system into individual services
to ensure scalability (service replication), re-
silience (service indepedence), technology het-
erogeneity (allowing different people to use
their favourite languages), composability (en-
abling reuse) and ease of deployment.
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34. Authoring i
1. Author a new definition under the model.
1.1 Represent an existing definition in a standard way.
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35. Authoring ii
2. Upload implementation units for each step in the model.
2.1 The now modular nature of the definition provides a template for development.
2.2 Alternatively, allows existing implementations developed by users to be reused in a standard
context.
3. Users can upload one or more implementations for each step in their own definitions, or
the definitions created by others.
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37. Execution ii
1.1 Can be edited by technical users who, if there are multiple uploads for each step, can
configure how each step is implemented prior to download, in order to provide them with
familiar languages with which to work.
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38. Execution iii
1.2 Can simply be executed out of the box by non-technical users2
2Currently requires the command line, a GUI executor is forthcoming!
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39. Execution iv
2. Pick a connector to be the first step in the workflow, depending on the format of the
dataset you are targeting.
2.1 Credentials for data stores are entered locally.
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42. Initial evaluation
First showed portability improvements in terms of clinical knowledge requirements and
programming expertise using the Knowledge conversion, clause Interpretation, and
Programming (KIP) phenotype portability scoring system (Shang et al., JBI, 2019.)
Knowledge Clause Programming Total
Traditional Code 0 2 2 4
Phenoflow Code 0 0 0 0
Traditional Logic 1 1 2 4
Phenoflow Logic 0 1 0 1
Table 2: KIP scores indicating the portability of traditional code-based (COVID-19) and logic-based
(Type 2 Diabetes) phenotype definitions and their Phenoflow counterparts.
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43. Clinical trials i
Recruitment in the REST clinical trial (AOMd) was handled using the TRANSFoRm e-source
trial platform.
In the original trial, archetype-based criteria were translated to concrete implementations
(e.g. XPath queries) by TRANSFoRm in order to determine a patient’s eligibility from their
EHR.
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44. Clinical trials ii
We developed a new service (PhEM) that instead enables the execution of a computable
phenotype against an EHR in order to identify eligible patients at the point-of-care.
EHR
integration
Archetype
execution
Data Node Connector
GP
Patient
record
Study system
Archetype
translation
Phenotype
execution
microservice
(PhEM)
The use of PhEM was shown to increase recruitment accuracy.
Chapman, Martin, et al. “Using Computable Phenotypes in Point-of-Care Clinical Trial
Recruitment”. MIE, 2021.
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45. Provenance
A reverse application: connected Phenoflow with the Data Provenance Template server, a
piece of software that holds structured fragments of provenance.
These fragments record the evolution
of the data (definitions) within Phe-
noflow, as they are edited by users,
improving validity, intelligibility and
reproducibility:
used used wasAssociatedWith
wasGeneratedBy
var:updated
prov:end vvar:time
prov:type phenoflow#Updated
zone:id update
var:author
prov:type phenoflow#Author
var:phenotypeAfter
phenoflow:description vvar:description
phenoflow:name vvar:name
prov:type phenoflow#Phenotype
var:phenotypeBefore
prov:type phenoflow#Phenotype
var:step
phenoflow:coding vvar:coding
phenoflow:doc vvar:doc
phenoflow:position vvar:position
phenoflow:stepName vvar:stepName
phenoflow:type vvar:type
prov:type phenoflow#Step
zone:id update
Fairweather, Elliot, et al. “A delayed instantiation approach to template-driven provenance for
electronic health record phenotyping”. IPAW, 2020.
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47. Authoring vs. parsing
“A model is only useful if it’s used”.
A challenging task to expect the adoption of a single model.
Alternative approach: parse the definitions developed by others (e.g. those represented in
other libraries), represent them within Phenflow, and then provide them for use.
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48. Parsing
Grab data relating to a definition (codelist, drug list, keywords, more complex logic).
Determine where key information is within this
data (e.g. a ‘conceptid’ column in a codelist CSV).
Populate the information required for the Phenoflow model au-
tomatically (e.g. step structure – easier for something like a
codelist; may require some intervention with more complex logic).
Automatically generate implementation units for each step.
Add these to the Phenoflow library ready for download.
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49. Parsing example – HDR UK national phenomics resource i
Have imported, standardised and provided implementations for ∼300 existing definitions as
a part of the HDR phenomics resource:
Phenoflow
Concept Library
(Web Interface +
API access)
CALIBER
Gateway
User
Bulk import API access
Workflow link Workflow link
Dataset link
Phenotype link
Dataset link
Phenotype Link
(API access)
Library
Platform
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50. Parsing example – HDR UK national phenomics resource ii
These are now directly linked to from CALIBER, and from the soon to be release HDR UK
Phenotype Portal:
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51. Parsing example - KCLHI NLP Phenotypes
The Health Informatics group at King’s have derived a set of inclusion (and exclusion – yet to
be modelled) keywords for a range of conditions.
Steps of model, and other required information, generated based on this data.
Example implementation provided and used as part of parsing process to generate
implementation units.
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52. Other future work i
• Always interested in parsing definitions from new sources.
• Publish more implementations for complex disease-specific phenotypes, e.g. long covid
(LOCOMOTION; phenotypes from NW London GP records) and stroke (KCL NIHR;
phenotypes from SLSR).
• Increase the library of workflow modules (e.g. types of dataset connectors) ready for
download and use.
• Automatic data conversion to enable use of different implementation techniques on same
dataset, e.g. conversion from CSV to DB to allow use of SQL scripts.
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53. Thank you!
Welcome to visit Phenoflow itself: http://kclhi.org/phenoflow.
View the architecture on Github: https://github.com/kclhi/phenoflow.
Publications mentioned: https://martinchapman.co.uk/publications/pheno.
Contact: @martin chap man.
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