1. MyHarmony is an application developed by the Malaysian Ministry of Health and MIMOS Bhd to generate statistics and clinical quality indicators from free-text clinical documents through natural language processing and coding with SNOMED CT terms.
2. In a case study, MyHarmony was able to accurately generate national cardiovascular disease registry statistics and key performance indicators from over 16,000 anonymized hospital discharge summaries through mapping terms to SNOMED CT codes.
3. The study demonstrated MyHarmony's ability to aggregate related terms through the SNOMED CT hierarchy to provide a more comprehensive analysis compared to simple string matching. This allows automated generation of timely statistics to support health planning and quality improvement.
Improving Credit Card Fraud Detection: Using Machine Learning to Profile and ...Melissa Moody
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Researchers Navin Kasa, Andrew Dahbura, and Charishma Ravoori undertook a capstone projectâpart of the UVA Data Science Institute Master of Science in Data Science programâthat addresses credit card fraud detection through a semi-supervised approach, in which clusters of account profiles are created and used for modeling classifiers.
Improving Credit Card Fraud Detection: Using Machine Learning to Profile and ...Melissa Moody
Â
Researchers Navin Kasa, Andrew Dahbura, and Charishma Ravoori undertook a capstone projectâpart of the UVA Data Science Institute Master of Science in Data Science programâthat addresses credit card fraud detection through a semi-supervised approach, in which clusters of account profiles are created and used for modeling classifiers.
Implementation of blood donation application using android smartphoneIJARIIT
Â
Blood is an important constituent of the human body. Timely availability of quality blood is a crucial requirement for
sustaining the healthcare services. In the hospital, in most of the cases, when blood is required, could not be provided on time
causing unpleasant things. Though donor is available in the hospital, the patient is unaware of it, and so is a donor. To resolve
this, a communication between hospital, blood bank, donor, and the receptor is important. The system listed following forecasting
on price variations and stock handling, increase in number of blood type, increase in human accident Infrastructure, blood on a
various category to be managed. So we solve the problem using the android application. The system will make sure that in case
of need, the blood will be made available to the patient. There will be web portal as well as an android app to make this
communication faster. It aims to create an e-Information about the donor and organization that are related to donating the
blood. The Methodology used to build this system uses GPS. The Proposed system will be used in Blood banks, Hospitals, for
Donors and Requesters whoever registers to the system.
DE-IDENTIFICATION OF PROTECTED HEALTH INFORMATION PHI FROM FREE TEXT IN MEDIC...ijsptm
Â
Medical health records often contain clinical investigations results and critical information regarding patient health conditions. In these medical records, along with patient health information, patient Protected Health Information (PHI) such as names, locations and date information can co-exist. As per Health Insurance Portability and Accountability Act (HIPAA), before sharing the medical records with researchers and others, all types of PHI information needs to be de-identified. Manual de-identification through human annotators is laborious and error prone, hence, a reliable automated de-identification system is need of the hour.
In this work, various state of the art techniques for de-identification of patient notes in electronic health records were analyzed for their performance, based on the performance quoted in the literature, NeuroNER was selected to de-identify Indian Radiology reports. NeuroNER is a named-entity recognition text de-identification tool developed by Massachusetts Institute of Technology (MIT). This tool is based on the Artificial Neural Networks written in Python and uses Tensorflow machine-learning framework and it comes with five pre-trained models.
To test the NeuroNER models on Indian context data such as name of the person and place, 3300 medical records were simulated. Medical records were simulated by extracting clinical findings, remarks from MIMIC-III data set. For collection of all the relevant Indian data, various websites were scraped to include Indian names, Indian locations (all towns and cities), and Indian Hospital and unit names. During the testing of NeuroNER system, we observed that some of the Indian data such as name, location, etc. were not de-identified satisfactorily. To improve the performance of NeuroNER on Indian context data, along with the existing NeuroNER pre-trained model, a new pre-trained model was added to handle Indian medical reports. Medical dictionary lookup was used to reduce number of misclassifications. Results from all four pre-trained models and the model trained on Indian simulated data were concatenated and final PHI token list was generated to anonymize the medical records to obtain de-identified records. Using this approach, we improved the applicability of the NeuroNER system to Indian data and improved its efficiency and reliability. 2000 simulated reports were used for transfer learning as training set, 1000 reports were used for test set and 300 reports were used for validation (unseen) set.
Authenticated Medical Documents Releasing with Privacy Protection and Release...JAYAPRAKASH JPINFOTECH
Â
Authenticated Medical Documents Releasing with Privacy Protection and Release Control
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
A Study on the Applications and Impact of Artificial Intelligence in E Commer...ijtsrd
Â
Trends in computer science show that various aspects of Artificial Intelligence are emerging, and other trends show that these advances are being applied to create intelligent in formation systems. In recent days artificial intelligence is changing the ways in which computers are usable as problem solving tools. The talent of humans is thus smartly creating and operating tools are indeed a feature of human based brainpower. This technology is now adapted by various E Commerce websites in order to identify the customer preference, pervious purchases, frequent checks etc. Google and Microsoft are also investing in artificial intelligence through various forms in order to enhance better customer service. The main aim of the study is to analysis and explores the various applications and impact of artificial intelligence in E Commerce industry. This study analyses and concludes that by replacement of human expert with artificial intelligence systems in E Commerce industry can significantly speedup and cheapens the production or service process. Prof. Lakshmi Narayan. N | Naveena. N "A Study on the Applications and Impact of Artificial Intelligence in E-Commerce Industry" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26374.pdfPaper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/26374/a-study-on-the-applications-and-impact-of-artificial-intelligence-in-e-commerce-industry/prof-lakshmi-narayan-n
Understanding the Need of Data Integration in E Healthcareijtsrd
Â
This paper discusses the current scenario of e healthcare and different dimensions of Big data in healthcare and the importance of data integration in e health care and the challenges associated with data integration and associated uses of data integration with respect to different use cases which might be helpful to physician's decision making because the data driven decision making involves combination of heterogeneous data which includes Electronic Health Record containing different types of data and connected healthcare organization in order to provide value based connected healthcare which would be useful to primary healthcare center located at different location because patients suddenly expect their healthcare experiences to be as exceptional and as transparent as those of retail or banking, and physician's have to scramble to adjust to these new expectations due to lack of data integrity. Mrs. Shashi Rekha. H. | Dr. Chethana Prakash. M | Dr. K. Thippeswamy "Understanding the Need of Data Integration in E- Healthcare" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31007.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/31007/understanding-the-need-of-data-integration-in-e-healthcare/mrs-shashi-rekha-h
Making isp business profitable using data miningijitcs
Â
This Data mining is a new powerful technology with great potential to extract hidden predictive
information from large databases. Tools of data mining scour databases for hidden patterns, predict future
trends and behaviours which allow businesses to make proactive, knowledge driven decision. This paper
analyzes ISPâs (Internet Service Provider) data to generate association rules for frequent patterns and
apply the criteria support and confidence to identify the most important relationships. Here, Apriori
algorithm is used to mine association rules. Furthermore, the conclusion point out business challenges and
provides the proposals of making ISP business profitable.
Presentation for UP Health Informatics HI201 under Dr. Iris Tan and Dr. Mike Muin. The topic for discussion Interoperability & Standards, a healthcare scenario was given regarding two disparate information systems, one found in a clinic, another with a hospital information system. #MSHI #HI201
Medical Assistant Design during this Pandemic Like Covid-19AI Publications
Â
In the current world scenario, individuals square measure additional involved regarding their health. However, it's terribly troublesome to get consultation with the doctor just in case of any health problems. Since the invention of the Coronavirus (nCOV-19), it's become a world pandemic. At an equivalent time, it's been a good challenge to hospitals or health care employees to manage the flow of the high variety of cases. particularly in remote areas, it's becoming tougher to consult a doctor once the immediate hit of the epidemic has occurred. So, to steer an honest life, care is incredibly vital. The planned plan is to form a medical chatbot victimization Machine Learning algorithm which will diagnose the illness and supply basic details regarding the illness before consulting a doctor. Several studies will solve this downside with some reasonably chatbot or health assistant. This project report proposes a colloquial care larva that's designed to order, counsel and provides data on generic medicines for diseases to the patients. During this paper, we would like to explore and deepen additional information regarding chatbots that would facilitate individuals to urge an equivalent and correct treatment as a doctor would do. In addition, presenting a virtual assistant may live with the infection severity and connect with registered doctors once symptoms become serious.
Clinical Data-Mining (CDM) involves the conceptualization, extraction, analysis, and interpretation of available clinical data for practice knowledge-building, clinical decision-making and practitioner reflection.
Implementation of blood donation application using android smartphoneIJARIIT
Â
Blood is an important constituent of the human body. Timely availability of quality blood is a crucial requirement for
sustaining the healthcare services. In the hospital, in most of the cases, when blood is required, could not be provided on time
causing unpleasant things. Though donor is available in the hospital, the patient is unaware of it, and so is a donor. To resolve
this, a communication between hospital, blood bank, donor, and the receptor is important. The system listed following forecasting
on price variations and stock handling, increase in number of blood type, increase in human accident Infrastructure, blood on a
various category to be managed. So we solve the problem using the android application. The system will make sure that in case
of need, the blood will be made available to the patient. There will be web portal as well as an android app to make this
communication faster. It aims to create an e-Information about the donor and organization that are related to donating the
blood. The Methodology used to build this system uses GPS. The Proposed system will be used in Blood banks, Hospitals, for
Donors and Requesters whoever registers to the system.
DE-IDENTIFICATION OF PROTECTED HEALTH INFORMATION PHI FROM FREE TEXT IN MEDIC...ijsptm
Â
Medical health records often contain clinical investigations results and critical information regarding patient health conditions. In these medical records, along with patient health information, patient Protected Health Information (PHI) such as names, locations and date information can co-exist. As per Health Insurance Portability and Accountability Act (HIPAA), before sharing the medical records with researchers and others, all types of PHI information needs to be de-identified. Manual de-identification through human annotators is laborious and error prone, hence, a reliable automated de-identification system is need of the hour.
In this work, various state of the art techniques for de-identification of patient notes in electronic health records were analyzed for their performance, based on the performance quoted in the literature, NeuroNER was selected to de-identify Indian Radiology reports. NeuroNER is a named-entity recognition text de-identification tool developed by Massachusetts Institute of Technology (MIT). This tool is based on the Artificial Neural Networks written in Python and uses Tensorflow machine-learning framework and it comes with five pre-trained models.
To test the NeuroNER models on Indian context data such as name of the person and place, 3300 medical records were simulated. Medical records were simulated by extracting clinical findings, remarks from MIMIC-III data set. For collection of all the relevant Indian data, various websites were scraped to include Indian names, Indian locations (all towns and cities), and Indian Hospital and unit names. During the testing of NeuroNER system, we observed that some of the Indian data such as name, location, etc. were not de-identified satisfactorily. To improve the performance of NeuroNER on Indian context data, along with the existing NeuroNER pre-trained model, a new pre-trained model was added to handle Indian medical reports. Medical dictionary lookup was used to reduce number of misclassifications. Results from all four pre-trained models and the model trained on Indian simulated data were concatenated and final PHI token list was generated to anonymize the medical records to obtain de-identified records. Using this approach, we improved the applicability of the NeuroNER system to Indian data and improved its efficiency and reliability. 2000 simulated reports were used for transfer learning as training set, 1000 reports were used for test set and 300 reports were used for validation (unseen) set.
Authenticated Medical Documents Releasing with Privacy Protection and Release...JAYAPRAKASH JPINFOTECH
Â
Authenticated Medical Documents Releasing with Privacy Protection and Release Control
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
A Study on the Applications and Impact of Artificial Intelligence in E Commer...ijtsrd
Â
Trends in computer science show that various aspects of Artificial Intelligence are emerging, and other trends show that these advances are being applied to create intelligent in formation systems. In recent days artificial intelligence is changing the ways in which computers are usable as problem solving tools. The talent of humans is thus smartly creating and operating tools are indeed a feature of human based brainpower. This technology is now adapted by various E Commerce websites in order to identify the customer preference, pervious purchases, frequent checks etc. Google and Microsoft are also investing in artificial intelligence through various forms in order to enhance better customer service. The main aim of the study is to analysis and explores the various applications and impact of artificial intelligence in E Commerce industry. This study analyses and concludes that by replacement of human expert with artificial intelligence systems in E Commerce industry can significantly speedup and cheapens the production or service process. Prof. Lakshmi Narayan. N | Naveena. N "A Study on the Applications and Impact of Artificial Intelligence in E-Commerce Industry" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26374.pdfPaper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/26374/a-study-on-the-applications-and-impact-of-artificial-intelligence-in-e-commerce-industry/prof-lakshmi-narayan-n
Understanding the Need of Data Integration in E Healthcareijtsrd
Â
This paper discusses the current scenario of e healthcare and different dimensions of Big data in healthcare and the importance of data integration in e health care and the challenges associated with data integration and associated uses of data integration with respect to different use cases which might be helpful to physician's decision making because the data driven decision making involves combination of heterogeneous data which includes Electronic Health Record containing different types of data and connected healthcare organization in order to provide value based connected healthcare which would be useful to primary healthcare center located at different location because patients suddenly expect their healthcare experiences to be as exceptional and as transparent as those of retail or banking, and physician's have to scramble to adjust to these new expectations due to lack of data integrity. Mrs. Shashi Rekha. H. | Dr. Chethana Prakash. M | Dr. K. Thippeswamy "Understanding the Need of Data Integration in E- Healthcare" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31007.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/31007/understanding-the-need-of-data-integration-in-e-healthcare/mrs-shashi-rekha-h
Making isp business profitable using data miningijitcs
Â
This Data mining is a new powerful technology with great potential to extract hidden predictive
information from large databases. Tools of data mining scour databases for hidden patterns, predict future
trends and behaviours which allow businesses to make proactive, knowledge driven decision. This paper
analyzes ISPâs (Internet Service Provider) data to generate association rules for frequent patterns and
apply the criteria support and confidence to identify the most important relationships. Here, Apriori
algorithm is used to mine association rules. Furthermore, the conclusion point out business challenges and
provides the proposals of making ISP business profitable.
Presentation for UP Health Informatics HI201 under Dr. Iris Tan and Dr. Mike Muin. The topic for discussion Interoperability & Standards, a healthcare scenario was given regarding two disparate information systems, one found in a clinic, another with a hospital information system. #MSHI #HI201
Medical Assistant Design during this Pandemic Like Covid-19AI Publications
Â
In the current world scenario, individuals square measure additional involved regarding their health. However, it's terribly troublesome to get consultation with the doctor just in case of any health problems. Since the invention of the Coronavirus (nCOV-19), it's become a world pandemic. At an equivalent time, it's been a good challenge to hospitals or health care employees to manage the flow of the high variety of cases. particularly in remote areas, it's becoming tougher to consult a doctor once the immediate hit of the epidemic has occurred. So, to steer an honest life, care is incredibly vital. The planned plan is to form a medical chatbot victimization Machine Learning algorithm which will diagnose the illness and supply basic details regarding the illness before consulting a doctor. Several studies will solve this downside with some reasonably chatbot or health assistant. This project report proposes a colloquial care larva that's designed to order, counsel and provides data on generic medicines for diseases to the patients. During this paper, we would like to explore and deepen additional information regarding chatbots that would facilitate individuals to urge an equivalent and correct treatment as a doctor would do. In addition, presenting a virtual assistant may live with the infection severity and connect with registered doctors once symptoms become serious.
Clinical Data-Mining (CDM) involves the conceptualization, extraction, analysis, and interpretation of available clinical data for practice knowledge-building, clinical decision-making and practitioner reflection.
Complete and accurate clinical documentation in the medical record has a direct impact on the assignment of codes, more accurate levels of reimbursement, and is critical to the higher quality of patient care. This paper describes the development of a system which can automatically flag the cases if there is an opportunity of improvement in patient clinical doc- uments. Automated Clinical Documentation Improvement (CDI) leverages the natural language processing (NLP) and contextual understanding of health record structure with additional business rules logic, helping CDI specialists identify critical documentation information that may be missing from the medical record. This results in more specific coding opportunity and better under- standing of the clinical complexity for accurate reimbursement. This system helped increase CDI specialistsâ productivity by efficiently filtering cases which need more attention from them.
Systematic review of quality standards for medical devices and practice measu...Pubrica
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A systematic literature search performed in databases (Medline, Cochrane Library, Scopus, Embase, CRD York), selected journals and websites identified articles describing either a general MDR structure or the development process of specific registries.
Learn More : https://pubrica.com/services/research-services/systematic-review/
Reference: https://bit.ly/3MCXLOK
Why Pubrica:
When you order our services, we promise you the following â Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Bio statistical experts | High-quality Subject Matter Experts.
âŻ
Contact us: ⯠âŻâŻ
Web:âŻhttps://pubrica.com/âŻ
Blog:âŻhttps://pubrica.com/academy/âŻ
Email:âŻsales@pubrica.comâŻ
WhatsApp : +91 9884350006âŻ
United Kingdom: +44-1618186353
Systematic review of quality standards for medical devices and practice measu...Pubrica
Â
A systematic literature search performed in databases (Medline, Cochrane Library, Scopus, Embase, CRD York), selected journals and websites identified articles describing either a general MDR structure or the development process of specific registries.
Learn More : https://pubrica.com/services/research-services/systematic-review/
Reference: https://bit.ly/3MCXLOK
Why Pubrica:
When you order our services, we promise you the following â Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Bio statistical experts | High-quality Subject Matter Experts.
âŻ
Contact us: ⯠âŻâŻ
Web:âŻhttps://pubrica.com/âŻ
Blog:âŻhttps://pubrica.com/academy/âŻ
Email:âŻsales@pubrica.comâŻ
WhatsApp : +91 9884350006âŻ
United Kingdom: +44-1618186353
Our work as consultants primarily involve implementing CRM systems to consolidate clinical and administrative data from EHRs and health plans for patient care coordination, medical tourism, transitional care, aftercare and case management. In the case of a hospital setting, they are using Mckesson Paragon EHR using ICD 10, CPT and LOINC to capture data associated to problem lists, medical history, procedures, medical orders, and test results. In the case of medications, they are using RxNorm. The system can handle SNOMED but they are only using ICD. In the case of the health plan, the data we gather is based on ICD, CPT, and NDC only. In another project, we are working to establish a centralized system to capture all test results of Puerto Rico for abnormalities identification, patient and provider notification. In addition, this data will be used to analyze health population the data we are receiving include terminology type, LOINC or CPT. Depending on the laboratory information system vendor we get the CPT or LOINC code.
Health Care Data Sets and their purpose
UHDDS, UACDS, MDS, OASIS, DEEDS and EMDS.
Explain the standardization data collection efforts.
Explain the five type of standards that need to be in place to implement the Nationwide Health Information Network (NHIN).
Standard Development Organizations
Evolving and Emerging Health Information Standards
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
Â
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
Â
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more âmechanicalâ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Â
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Â
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as âpredictable inferenceâ.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Â
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But thereâs more:
In a second workflow supporting the same use case, youâll see:
Your campaign sent to target colleagues for approval
If the âApproveâ button is clicked, a Jira/Zendesk ticket is created for the marketing design team
Butâif the âRejectâ button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
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Are you looking to streamline your workflows and boost your projectsâ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, youâre in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part âEssentials of Automationâ series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Hereâs what youâll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
Weâll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Donât miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Â
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
DevOps and Testing slides at DASA ConnectKari Kakkonen
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My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
My harmony generating statistics from clinical text for monitoring clinical quality indicators
1. MyHarmony: Generating Statistics from Clinical Text for Monitoring Clinical Quality Indicators
Md. Khadzir Sheikh Ahmad1
; Mohd Syazrin Mohd Sakri1
; âIsmat Mohd Sulaiman1
; Syirahaniza Mohd
Salleh1
; Dickson Lukose2
; Omar Ismail1
; Abd Aziz Latip3
; Muhammad Aiman Mazlan3
.
1
Health Informatics Centre, Planning Division, Ministry of Health, Malaysia
2
GCS Agile Pty. Ltd.
3
MIMOS Bhd.
Abstract:
The Ministry of Health developed MyHarmony with MIMOS Bhd. as part of the Malaysian Health Data
Warehouse (MyHDW) initiative. MyHDW aims to be a trusted source of truth of comprehensive health
data structured for analysis. MyHarmony fulfills that criteria by enabling data and information from
unstructured form to be mined, such as texts, images, and sound. MyHarmony's first deliverable is the
ability to mine clinical texts using Natural Language Processing (NLP) with SNOMED CT as its
knowledge-base of clinical terms. MyHarmony is the engine the retrieves data and information into
computer processable form by assigning SNOMED CT codes, which can then be further analysed
statistically. MyHarmony is able to recognise and harmonise different terms that means the same. It also
understands context for a more accurate coding; such as negations (no, not known, unknown) and
conditionals (past history, symptoms of, previous). Using SNOMED CT, MyHarmony's ability is further
advanced by using subsumption technique for a more comprehensive statistical results. This study will
present a use case where clinical text from anonymized hospital discharge summaries can generate
clinical indicators using MyHarmony for health managers. An added benefit to the operational (hospital)
staff is the ability to produce such indicators is an efficient and timely manner by reducing workload for
data collection and submission. MyHarmony could be the new and improved way to provide important
statistical measures for evidence-based health planning, leading to improved healthcare services and
health as a whole.
Keywords:
MyHarmony; MyHDW; Text mining; Quality Indicators; SNOMED CT
1. Introduction:
The Ministry of Health developed MyHarmony with MIMOS Bhd. as part of the Malaysian Health Data
Warehouse (MyHDW) initiative. MyHDW aims to be a trusted source of truth of comprehensive health
data structured for analysis. MyHarmony is an application in the Malaysian Health Data Warehouse
(MyHDW) that aims to analyse semi-structured and unstructured data. The unstructured data can be in the
form of free-text, visual, audio and machine generated data. Unstructured data does not have
predetermined values and not stored in an organized manner to be analysed by a conventional data
warehouse. Therefore, other techniques need to be applied. MyHarmony aims to address this and be
included as part of MyHDW.
There were three (3) major deliverables in the conceptual stage. The first part refers to the development
and implementation of health terminology standards, namely SNOMED CT, which will be the knowledge
bases for MyHarmony. The second part was harmonization of the medical terminology to SNOMED CT
2. terms by way of mapping. The last part was about the development and implementation of MyHarmony
to show that the application can codify relevant terms in free-text using Natural Language Processing
(NLP) technique. The SNOMED CT codified data can then be analysed for information generation.
2. Methodology:
The development was first started in 2014 with the development of Cardiology Refset. Cardiology Refset
was the terminology reference for the MyHarmony engine during the harmonisation/mapping and
codification process. Cardiology Refset (version 1.0) was completed and released in 2014. It is a simple
reference set [1] containing about 600 terms related to Cardiology speciality including signs and
symptoms, diagnoses, procedures, body structures, medical devices and medications. It was delivered in
time to be tested on MyHarmony standalone system to generate National Cardiovascular Disease
(NCVD) registries.
The draft Refset and method was presented during IHTSDO meetings and Expo in succession on
September 2013, October 2013, and April 2014 to gain feedback from experts in the international
community. The finalized method was presented during SNOMED CT Expo, October 2014 [2] .
The Cardiology Refset was then expanded to include all cardiology related terms and Cardiology Refset
v1.1 was completed in July 2016 containing more than 6000 concepts. First, more than 300,000
SNOMED CT concepts (Fully Specified Names) were extracted and reviewed by PIK using eyeballing
technique. About 12,000 concepts that were believed to be related to Cardiology specialty was given to
the clinicians for review. The clinicians reduced the number of concepts to about 6,000. Additionally, the
Refset included local terms and common abbreviations which were mapped to existing concepts.
Next, the team utilise MyHarmony to generate the analysis. There were 4 main functions in MyHarmony:
1. Terminology Managementâ to allow user to upload the SNOMED CT International Release
content into MyHarmony, and upload SNOMED CT Refset in reference to the SNOMED CT
International Release.
2. Data Managementâ to allow user to upload the data that will be harmonized and codified. This
function also allows user to view the content of the data.
3. Codification Managementâ to allow user to codify the dataset according to the selected
SNOMED CT Refset and view the codified data for validation purpose.
4. Query Managementâ to allow user to explore the data by generating queries using Structured
Query Language (SQL).
The functions were arranged according to the work process. First, the SNOMED CT International
Release, SNOMED CT Cardiology Refset, and the dataset needs to be uploaded. Then, the dataset is
codified and saved. Using the Query Management, the codified data can then be explored via data
profiling and query generation.
For initial analysis, SNOMED CT International release version 20160731 was used as the Cardiology
Reference Set was developed using this version of SNOMED CT.
3. The team received a set of database from a hospital with cardiology service which consists of 16224
discharge summaries from year 2017. The database was then uploaded into MyHarmony. The personally
identified information (patient names, ID, and street address) were anonymised prior to codification and
analysis. The output is a codified dataset, which enable information processing and analysis by machines.
Using the Query Management, the codified data was then be explored via data profiling and query
generation.
3. Result:
The team conducted several data profiling queries to ensure that MyHarmony were able to capture the
data correctly. For example, the number of records by month between Raw data (MyHarmony without
SNOMED CT) and Harmonized data (MyHarmony with SNOMED CT) should return the same result.
Other examples of data profiling queries are the number of records by gender, by specialty, and by
ethnicity.
Next, the team developed queries required by the National Cardiovascular Disease (NCVD) registries and
compare the results with published registry reports. For example, querying the number of Ischaemic Heart
Disease (IHD) by gender shows 1:4 female to male ratio, which is a similar ratio in the registry reports.
Furthermore, the query also shows that Harmonized data captures more result compare to Raw data due to
SNOMED CT relationship structure, thus capturing all the subtypes of IHD and its synonyms or ways of
writing. The registry, however, only captures three (3) diagnosis due to its structured format, which are
ST Elevation Myocardial Infarction, Non-ST Elevation Myocardial Infarction, and Unstable Angina. This
trend and pattern comparison allow validation by the Clinicians and gains their buy-in in using
MyHarmony.
The team also tried to generate more queries required by the NCVD registry. However, it was limited by
the documentation in the discharge summary. Registry queries requires more detail information that may
often not documented in a discharge summary, such as information on smoking status and complications
of procedures.
After that, the team was tasked to generate National Cardiology Key Performance Indicators (KPIs).
MyHarmony are able to generate 7 out of the 8 KPIs (KPI 2 to 8). The first KPI was excluded because the
data are available at the clinic and not documented in inpatient discharge summaries. The Health
Information Framework (HIF) was developed for the 7 KPIs, which detailed out the inclusion and
exclusion criteria, the target, the formula, the terms used by MyHarmony, and query, and lastly a section
for additional notes.
Preliminary manual validation on the completeness and accuracy of codified data shows 90% precision
and 70% recall. The content of those records is complex as it does not follow grammar rules, and contains
a large number of short forms, abbreviations, acronyms and analogous terms (e.g., synd, ACS, CCS IV,
NYHA 2). One example of record is â2VD with RCA culprit lesion - Ad hoc PCI DES to RCA and LADâ
which is challenging to codify using approaches based on strict grammar. The revised version of
MyHarmony uses a different approach based on shallow parsing and the consideration of multiple
4. suitable combinations of words in a sentence. With further iterations and improvement in the mapping,
these challenges were overcome[3].
From the SNOMED CT codified database, the system was able to show a more accurate result during
analysis . This is because MyHarmony capitalises on the existing SNOMED CT relationships structure
between concepts. In this case, when querying âNumber of Ischaemic Heart Disease cases per yearâ,
MyHarmony search the code and term for âIschaemic heart diseaseâ, its synonyms and accepted
abbreviations, and all the subtypes of Ischaemic heart disease such as all subtypes of âMyocardial
infarctionâ and âAnginaâ. MyHarmony aggregates these records resulting in a more accurate analysis.
Usually, the result where MyHarmony utilise SNOMED CTâs relationship structure would show more
records. This is because Mi-Harmony was able to aggregate data not just through String match, but also
utilize the IS-A hierarchy structure in SNOMED CT. For example, querying âIschemic heart diseaseâ will
gather clinical records with synonymous terms like âIschaemic heart diseaseâ and âIHDâ; as well as
clinical records containing all the subtypes of âIschemic heart diseaseâ such as âMyocardial Infarctionâ
and âUnstable anginaâ.
Context awareness such as negation and pasts events were also applied. For example, the term âNo chest
painâ, âNo known history of diabetes mellitusâ, and âSymptoms of heart failureâ will not be coded as the
presenting condition. Additionally, terms like âPrevious history ofâ, âPrevious admission ofâ, and
âFamily history ofâ within the same sentence as a clinical condition will not be coded as the current
condition for the record.
4. Discussion and Conclusion:
When showcasing these abilities to the clinicians, the team agreed that MyHarmony was able to:
(i) Generate more information from free-text utilising the SNOMED CT structure, thus,
reducing the effort needed to collect data in a structured manner such as in a registry
and indicator reports;
(ii) Able to generate new information by retrospectively running new queries on old
discharge summary records; thus, reducing the effort and time to collect data in a
prospective manner when new questions arise, such as for indicator reports that often
change on a yearly basis;
(iii) Able to deliver information in a timelier fashion; thus clinicians and health managers
are able to plan and take action without waiting for a 1 to 3 yearly report;
(iv) Improve documentation of clinicians when they are aware of MyHarmonyâs ability
during roadshows.
Generating indicators for monitoring and evaluation can be a burden even for healthcare facilities
equipped with EHR. Conventionally, collecting data for indicators requires multiple data entries in
aggregated manner, with manual submission to central agencies, where the results are only published on a
yearly basis. Introducing MyHarmony may reduce these burdens. Capturing data from the source in an
automated way, i.e. free text documented by doctors, would reduce duplication of work and the amount of
resources to capture the data into manual form. Having the data in granular form would allow a more
5. dynamic analysis and prevents dishonesty. Information required, whether old or new information, can be
formulated and disseminated back to the clinicians and health managers in a timelier fashion.
MyHarmony has the potential to expand further in its implementation and technology. However, there are
still challenges to be addressed. Currently, MyHarmony has been developed to mine free-text for
Cardiology via a back-end approach. It uses a single version of SNOMED CT International. The team is
still researching the best approach to manage SNOMED CT versions and its codified data, which may
impact the resulting analysis in an inconsistent way. The team are also seeking international experience
for this matter.
Other challenges include researching a more efficient and effective method to develop SNOMED CT
Refsets. The initial method by referencing terms required by registries or indicators has been established.
However, expanding the SNOMED CT Refsets to include relevant terms for a specific clinical specialty
or domain needs to be refined further. Eye-balling technique to search the entire SNOMED CT content
have its strength and weaknesses. Even though it is a very thorough method, there are possibilities of
missed terms and very time consuming. Despite these challenges, the journey in developing MyHarmony
and the lessons learnt has allowed the team to refine the methods and processes to expand the use of
MyHarmony to other clinical specialties.
Analysis from unstructured data would hope to complement analysis from structured data (like census and
registries), with the additional benefit workload reduction to provide timelier, trusted, and dynamic
information.
References:
1. SNOMED CT Simple Reference Set:
https://confluence.ihtsdotools.org/display/DOCRFSPG/5.1.+Simple+Reference+Set
2. Mohd Sulaiman I, Sheikh Ahmad MK. SNOMED CT Cardiology Reference Set Development,
Malaysia. Proceedings of the SNOMED CT Implementation Showcase [Internet]. Amsterdam: IHTSDO;
2014. Retrieved from:
http://ihtsdo.org/fileadmin/user_upload/doc/showcase/show14/SnomedCtShowcase2014_Abstract_14058
.pdf
3. Abdul Manaf NA, Mohamed K, Lukose D. Harmonizing EHR Databases with SNOMED CT. Proceedings of the
SNOMED CT Implementation Showcase 2014 [Internet]. Amsterdam: IHTSDO; 2014.
https://confluence.ihtsdotools.org/display/FT/SNOMED+CT+Implementation+Showcase+2014