Kraaij infrastructures for secure data analytics def brussel 2017Wessel Kraaij
How can we combine vertically partitioned data for data driven health in a secure way. Description of the Holland Health Data Cooperative and the Prana Data project www.pranadata.nl pilot on homomorphic encryption.
Contribution to the DigEnlight workshop https://digitalenlightenment.org/event/workshop-towards-european-ecosystem-health-care-data Oct 25 2017, Brussels.
From personal health data to a personalized adviceWessel Kraaij
Invited talk at the health track of ICT.OPEN 2018, 20-3-2018
1. Related Data science challenges to Digital Health trends
2. Designing an infrastructure to support secure learning from distributed health data repositories, for personalized health advice
3. Supporting patients with rare diseases with patient driven research and the generation of new hypotheses based on patient experiences.
Methodologies for Addressing Privacy and Social Issues in Health Data: A Case...Trilateral Research
Huge quantities of complex and diverse data are generated everyday in healthcare institutions, including clinical documentation (diagnostics, lab data, imaging data, etc.), administrative data, activities and cost data, and R&D data from clinical trials.
An Introduction to Clinical InformaticsCorinn Pope
Why should you care about clinical informatics? Because those who practice clinical informatics just may help our healthcare system get out of its funk and become an efficient, lean, and tech-savvy machine. Plus, the industry is growing and growing fast.
Presentation by Kelly Hart, ONDC in PM&C, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Legal and regulatory challenges to data sharing for clinical genetics and ge...Human Variome Project
There are many factors that impede genomic variant sharing in the UK, despite it becoming a necessary part of clinical care. These include the lack of a designated infrastructure or mechanism aggravated by the complexity of laws that apply, and fragmented and variable advice from local ‘Caldicott guardians’ who guide NHS trusts on their responsibilities concerning data protection and confidentiality. Since the legitimacy of data sharing in the UK is framed in terms of ‘personal data’ being shared for ‘direct care’ (subject to legal exceptions), the blurred boundaries between clinical care and research, and the spectrum of identifiability of data also lead to differing interpretations resulting in inconsistent practices.
In a multidisciplinary collaboration, the PHG Foundation and the UK’s Association for Clinical Genetic Science co-hosted a workshop to examine the clinical necessity for sharing variant data and associated phenotypic information, the technical feasibility and the legal and regulatory impediments to such sharing. Delegates included clinicians, laboratory scientists, and key policy makers, including the National Data Guardian for Health and Care and representatives from the 100,000 Genomes Project, a pioneering research project which promises to build a legacy for future genomics services in the UK. The key finding from our work was that current arrangements for sharing genomic variants within the NHS are unsatisfactory and inconsistent practices are compromising safety and quality. Our workshop report [1] highlights the urgent need for (i) national agreement to optimise sharing within the NHS and develop consensus on the legitimacy of data sharing, (ii) standardised operational processes, including a designated sustainable database or mechanism for sharing, and (iii) strong leadership by the multiple relevant health organisations to demonstrate the benefits and risks associated with sharing and not sharing data.
Since publication of the workshop report, the NHS Consortium (operating within the DECIPHER database) has reported a 120% increase in the number of cases shared, the 100,000 Genomes Project and associated data embassy have got underway and the EU Data Protection Regulation has been finalised. However research highlights continuing public reservations about some aspects of data sharing including commercial access and misgivings around secondary uses of data. Publication of the National Data Guardian’s long-awaited review of consent and security provisions to provide guidance on a new consent and opt-out model for sharing patient information in the NHS, has been delayed pending the results of the EU referendum being known. Against this backdrop, the imperative to develop robust, proportionate policies for genomic data sharing becomes increasingly acute.
Funding from the PHG Foundation and the Association for Clinical Genetic Science.
Kraaij infrastructures for secure data analytics def brussel 2017Wessel Kraaij
How can we combine vertically partitioned data for data driven health in a secure way. Description of the Holland Health Data Cooperative and the Prana Data project www.pranadata.nl pilot on homomorphic encryption.
Contribution to the DigEnlight workshop https://digitalenlightenment.org/event/workshop-towards-european-ecosystem-health-care-data Oct 25 2017, Brussels.
From personal health data to a personalized adviceWessel Kraaij
Invited talk at the health track of ICT.OPEN 2018, 20-3-2018
1. Related Data science challenges to Digital Health trends
2. Designing an infrastructure to support secure learning from distributed health data repositories, for personalized health advice
3. Supporting patients with rare diseases with patient driven research and the generation of new hypotheses based on patient experiences.
Methodologies for Addressing Privacy and Social Issues in Health Data: A Case...Trilateral Research
Huge quantities of complex and diverse data are generated everyday in healthcare institutions, including clinical documentation (diagnostics, lab data, imaging data, etc.), administrative data, activities and cost data, and R&D data from clinical trials.
An Introduction to Clinical InformaticsCorinn Pope
Why should you care about clinical informatics? Because those who practice clinical informatics just may help our healthcare system get out of its funk and become an efficient, lean, and tech-savvy machine. Plus, the industry is growing and growing fast.
Presentation by Kelly Hart, ONDC in PM&C, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Legal and regulatory challenges to data sharing for clinical genetics and ge...Human Variome Project
There are many factors that impede genomic variant sharing in the UK, despite it becoming a necessary part of clinical care. These include the lack of a designated infrastructure or mechanism aggravated by the complexity of laws that apply, and fragmented and variable advice from local ‘Caldicott guardians’ who guide NHS trusts on their responsibilities concerning data protection and confidentiality. Since the legitimacy of data sharing in the UK is framed in terms of ‘personal data’ being shared for ‘direct care’ (subject to legal exceptions), the blurred boundaries between clinical care and research, and the spectrum of identifiability of data also lead to differing interpretations resulting in inconsistent practices.
In a multidisciplinary collaboration, the PHG Foundation and the UK’s Association for Clinical Genetic Science co-hosted a workshop to examine the clinical necessity for sharing variant data and associated phenotypic information, the technical feasibility and the legal and regulatory impediments to such sharing. Delegates included clinicians, laboratory scientists, and key policy makers, including the National Data Guardian for Health and Care and representatives from the 100,000 Genomes Project, a pioneering research project which promises to build a legacy for future genomics services in the UK. The key finding from our work was that current arrangements for sharing genomic variants within the NHS are unsatisfactory and inconsistent practices are compromising safety and quality. Our workshop report [1] highlights the urgent need for (i) national agreement to optimise sharing within the NHS and develop consensus on the legitimacy of data sharing, (ii) standardised operational processes, including a designated sustainable database or mechanism for sharing, and (iii) strong leadership by the multiple relevant health organisations to demonstrate the benefits and risks associated with sharing and not sharing data.
Since publication of the workshop report, the NHS Consortium (operating within the DECIPHER database) has reported a 120% increase in the number of cases shared, the 100,000 Genomes Project and associated data embassy have got underway and the EU Data Protection Regulation has been finalised. However research highlights continuing public reservations about some aspects of data sharing including commercial access and misgivings around secondary uses of data. Publication of the National Data Guardian’s long-awaited review of consent and security provisions to provide guidance on a new consent and opt-out model for sharing patient information in the NHS, has been delayed pending the results of the EU referendum being known. Against this backdrop, the imperative to develop robust, proportionate policies for genomic data sharing becomes increasingly acute.
Funding from the PHG Foundation and the Association for Clinical Genetic Science.
International perspective for sharing publicly funded medical research dataARDC
Presentation by Olivier Salvado, CSIRO, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
It was an honor to be invited to present the Clinical Informatics keynote at the Health Informatics Society of Australia's #HIC16 conference on July 25, 2016.
Here is an outline of the topics that I spoke about in greater depth with audience of Clinicians & IT execs.
(In a separate presentation I spoke of the importance of engaging Patients in healthcare design, patient generated data, self-care, crowdsourcing, etc)
Presentation by Prof Lisa Askie, ANZCTR, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Presentation by Hugo Leroux and Liming Zhu, CSIRO, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Investigator-initiated clinical trials: a community perspectiveARDC
Presentation by Miranda Cumpston, ACTA, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
SmartHealth Ecosystem Event 12.6.2019, Tatu Laurila presentation on How to make best out of Finnish health data for future global innovation and precision medicine?
Closing the Loop in Healthcare Analytics - Correlating Clinical and Administrative Systems with Research Efforts to Deliver Clinical Efficiency in Real Time
Presentation by Dr Adrian Burton, ARDC, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Registries are a powerful informatics tool for research and public health. As both commercial payers and the Centers for Medicare and Medicaid Services work to shift incentives shift toward value based-purchasing, demand for reliable, accessible data on populations is growing. The purpose of this poster is to define accountable care organizations (ACOs), explain the importance of registries in managing data for ACOs, and discuss specific informatics requirements unique to accountable care registries.
Brisbane Health-y Data: What are health and sensitive data and why are they t...ARDC
Presentation given by Sarah Olesen at the 'Sharing Health-y Data Workshop: Challenges and Solutions' event co-hosted by ANDS and HISA. Held on Wednesday 16th March 2016 at the Translational Research Institute, Brisbane, Australia.
BioSHaRE: The DataSHIELD Legal Analysis Template - Susan Wallace - University...Lisette Giepmans
BioSHaRE conference July 28th, 2015, Milan - Latest tools and services for data sharing
Stream 2: ELSI approaches and services
An ethico-legal analysis was conducted at ULEIC that examined each step of the DataSHIELD process from the perspective of UK case law, regulations, and guidance. In order to facilitate a similar analysis for other countries/ jurisdictions, a ‘DataSHIELD Legal Analysis Template’ is being made. Contact: sew40@leicester.ac.uk
DataSHIELD was born of the requirement in the biomedical and social sciences to co-analyse individual patient data (micro data) from different sources, without disclosing identity or sensitive information. Under DataSHIELD, raw data never leave the data provider and no micro data or disclosive information can be seen by the researcher. The analysis is taken to the data – not the data to the analysis. It provides a flexible, modular, open-source solution ideally placed to serve a broad user and development community and to circumvent barriers related to ethical-legal restrictions, intellectual property and physical size of the data as a limiting factor.
Interoperability in health care information systemsAlexander Ask
A slide show from our bachelor thesis presentation. Its main focus is interoperability in health care and how interoperability issues can be addressed by open standardization.
Public Health Informatics, Consumer Health Informatics, mHealth & Personal He...Nawanan Theera-Ampornpunt
Presented at the Health Informatics and Health Information Technology Course, Doctor of Philosophy and Master of Science Programs in Data Science for Health Care (International Program), Faculty of Medicine Ramathibodi Hospital, Mahidol University on October 24, 2017
International perspective for sharing publicly funded medical research dataARDC
Presentation by Olivier Salvado, CSIRO, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
It was an honor to be invited to present the Clinical Informatics keynote at the Health Informatics Society of Australia's #HIC16 conference on July 25, 2016.
Here is an outline of the topics that I spoke about in greater depth with audience of Clinicians & IT execs.
(In a separate presentation I spoke of the importance of engaging Patients in healthcare design, patient generated data, self-care, crowdsourcing, etc)
Presentation by Prof Lisa Askie, ANZCTR, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Presentation by Hugo Leroux and Liming Zhu, CSIRO, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Investigator-initiated clinical trials: a community perspectiveARDC
Presentation by Miranda Cumpston, ACTA, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
SmartHealth Ecosystem Event 12.6.2019, Tatu Laurila presentation on How to make best out of Finnish health data for future global innovation and precision medicine?
Closing the Loop in Healthcare Analytics - Correlating Clinical and Administrative Systems with Research Efforts to Deliver Clinical Efficiency in Real Time
Presentation by Dr Adrian Burton, ARDC, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Registries are a powerful informatics tool for research and public health. As both commercial payers and the Centers for Medicare and Medicaid Services work to shift incentives shift toward value based-purchasing, demand for reliable, accessible data on populations is growing. The purpose of this poster is to define accountable care organizations (ACOs), explain the importance of registries in managing data for ACOs, and discuss specific informatics requirements unique to accountable care registries.
Brisbane Health-y Data: What are health and sensitive data and why are they t...ARDC
Presentation given by Sarah Olesen at the 'Sharing Health-y Data Workshop: Challenges and Solutions' event co-hosted by ANDS and HISA. Held on Wednesday 16th March 2016 at the Translational Research Institute, Brisbane, Australia.
BioSHaRE: The DataSHIELD Legal Analysis Template - Susan Wallace - University...Lisette Giepmans
BioSHaRE conference July 28th, 2015, Milan - Latest tools and services for data sharing
Stream 2: ELSI approaches and services
An ethico-legal analysis was conducted at ULEIC that examined each step of the DataSHIELD process from the perspective of UK case law, regulations, and guidance. In order to facilitate a similar analysis for other countries/ jurisdictions, a ‘DataSHIELD Legal Analysis Template’ is being made. Contact: sew40@leicester.ac.uk
DataSHIELD was born of the requirement in the biomedical and social sciences to co-analyse individual patient data (micro data) from different sources, without disclosing identity or sensitive information. Under DataSHIELD, raw data never leave the data provider and no micro data or disclosive information can be seen by the researcher. The analysis is taken to the data – not the data to the analysis. It provides a flexible, modular, open-source solution ideally placed to serve a broad user and development community and to circumvent barriers related to ethical-legal restrictions, intellectual property and physical size of the data as a limiting factor.
Interoperability in health care information systemsAlexander Ask
A slide show from our bachelor thesis presentation. Its main focus is interoperability in health care and how interoperability issues can be addressed by open standardization.
Public Health Informatics, Consumer Health Informatics, mHealth & Personal He...Nawanan Theera-Ampornpunt
Presented at the Health Informatics and Health Information Technology Course, Doctor of Philosophy and Master of Science Programs in Data Science for Health Care (International Program), Faculty of Medicine Ramathibodi Hospital, Mahidol University on October 24, 2017
Trusted! Quest for data-driven and fair health solutions Sitra / Hyvinvointi
An inspiring online event on 3 February 2021. We are discussing the future of data-driven health solutions that focus on fairness for all stakeholders: people, business and the public sector. We are asking questions such as: What is fairness in health? What role does trust play in data-driven health services? What needs to change and who needs to act? Most of all, we are launching “The Fair Health Data Challenge“.
Event speakers:
- Jaana Sinipuro, Project Director, IHAN – Human-driven data economy, Sitra
- Dipak Kalra, President, The European Institute for Innovation through Health Data (i~HD)
- Pekka Kahri, Technology Officer, HUS Helsinki University Hospital
- Markus Kalliola, Project Director, Health data 2030, Sitra
- Tiina Härkönen, Leading Specialist, Sitra
Presentation of the PICASO Project at WHINN Conference, October 2016PicasoProject
Presentation of PICASO in the session on integrating health and social care by Jesper Thestrup from partners In-JeT ApS
WHINN: Week of Health and INNovation, October 2016
Big Data Analytics using in Healthcare Management Systemijtsrd
Big data is the new technology for healthcare management system. Present day's big data analytics are using in everywhere because of its good data management and its large storage capacity. In hospital managements the patients and doctors record keeping safe is the important role in healthcare system. In worldwide the big data method is extended use in the area of medicine and healthcare system. In this sector so many problems are there in implementing big data in healthcare system especially in relation to securities, privacy matters, standard records, good governance, managing of data, data storing and maintenance, etc. It is critical that these challenges to overcome before big data can be implemented successfully in healthcare. The amount of data being digitally collected and stored safely in big data Hadoop clusters. This paper introduces healthcare data, big data in healthcare systems, applications, advantages, issues of Big Data analytics in healthcare sector. Gagana H. S | Bhavani B. T | Gouthami H. S "Big Data Analytics using in Healthcare Management System" 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/ijtsrd31014.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/31014/big-data-analytics-using-in-healthcare-management-system/gagana-h-s
Future of patient data global summary - 29 may 2018Future Agenda
We are witnessing a growing revolution around the provision of healthcare. Much is being driven by the proliferation of medical data and the technology that supports this. As the pressures on healthcare providers continue to escalate, the better collection, management and use of more patient-specific information provides a significant opportunity for innovation and change. The Future Agenda team made this, the Future of Patient Data, the focus of our major Open Foresight project for 2017/18 – 12 discussions across 11 countries, gathering views from over 300 experts.
This report shares the findings from the Future of Patient Data research project. It highlights several important emerging issues that are the source of major differences of opinion around the world. These include how to best accommodate rising data sovereignty concerns, the privatisation of health information and the growing value of health data. Some of the challenges and opportunities are technical in nature, but many are concerned with different ethical, philosophical and cultural approaches to health and how we treat the sick in society.
To access the full report please see https://www.futureofpatientdata.org
Zone model for data privacy and confidentiality in medical researchWolfgang Kuchinke
There exist several privacy frameworks for cancer research or biobanking (e.g. ACGT, GenoMatch, caBIG). But most existing privacy frameworks apply the most stringent approach to their data flow and interpret “anonymisation” in a restrictive way. A more flexible approach is needed to guarantee privacy of patient data, but at the same time enable unhindered research. We developed an easy model to display policies and rules for data privacy; it employs the novel concept of "privacy zones for research data flows". The zone model can be used for all important research scenarios.
The Privacy Zone Model is built upon the concept of three zones (Care Zone, Non-care Zone and Research Zone) habouring databases, data transformation operators, such
as data linkers and privacy filters. Using our model, a risk gradient for moving data from a zone of high risk for patient identification to a zone of low risk can be created for each data flow.
Augmented Personalized Health: using AI techniques on semantically integrated...Amit Sheth
Keynote @ 2018 AAAI Joint Workshop on Health Intelligence (W3PHIAI 2018), 2 February 2018, New Orleans, LA [Video: https://youtu.be/GujvoWRa0O8]
Related article: https://ieeexplore.ieee.org/document/8355891/
Abstract
Healthcare as we know it is in the process of going through a massive change - from episodic to continuous, from disease-focused to wellness and quality of life focused, from clinic centric to anywhere a patient is, from clinician controlled to patient empowered, and from being driven by limited data to 360-degree, multimodal personal-public-population physical-cyber-social big data-driven. While the ability to create and capture data is already here, the upcoming innovations will be in converting this big data into smart data through contextual and personalized processing such that patients and clinicians can make better decisions and take timely actions for augmented personalized health. In this talk, we will discuss how use of AI techniques on semantically integrated patient-generated health data (PGHD), environmental data, clinical data, and public social data is exploited to achieve a range of augmented health management strategies that include self-monitoring, self-appraisal, self-management, intervention, and Disease Progression Tracking and Prediction. We will review examples and outcomes from a number of applications, some involving patient evaluations, including asthma in children, bariatric surgery/obesity, mental health/depression, that are part of the Kno.e.sis kHealth personalized digital health initiative.
Background: Background: http://bit.ly/k-APH, http://bit.ly/kAsthma, http://j.mp/PARCtalk
Improving health care outcomes with responsible data scienceWessel Kraaij
Keynote presentation by Wessel Kraaij at the Dutch pattern recognition and impage processing society (NVPBV) 29/5/2018, Eindhoven.
This talk discusses
1. trends in health care and respondible data science and their intersection
2. Secure federated analytics on distributed data repositories
3. Generating clinically relevant hypotheses from patient forum discussions.
3.A Basic Overview of Health Information Exchange.pdfBelayet Hossain
What is health information exchange? A hie software enables healthcare providers to securely communicate clinical data in line with HIPAA regulations. In other words, it’s a system for securely moving a client’s health information from one county to another.
https://itphobia.com/a-basic-overview-of-health-information-exchange/
Digital Enlightment Forum: Towards a European ecosystem for health care data
Presentation of eStandards/Trillium II at the workshop of the Digital Enlightment Forum
Similar to Towards an ecosystem for privacy respecting analysis of distributed health data (20)
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
Anti ulcer drugs and their Advance pharmacology ||
Anti-ulcer drugs are medications used to prevent and treat ulcers in the stomach and upper part of the small intestine (duodenal ulcers). These ulcers are often caused by an imbalance between stomach acid and the mucosal lining, which protects the stomach lining.
||Scope: Overview of various classes of anti-ulcer drugs, their mechanisms of action, indications, side effects, and clinical considerations.
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
Pulmonary Thromboembolism - etilogy, types, medical- Surgical and nursing man...VarunMahajani
Disruption of blood supply to lung alveoli due to blockage of one or more pulmonary blood vessels is called as Pulmonary thromboembolism. In this presentation we will discuss its causes, types and its management in depth.
Factory Supply Best Quality Pmk Oil CAS 28578–16–7 PMK Powder in Stockrebeccabio
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The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
Towards an ecosystem for privacy respecting analysis of distributed health data
1. TOWARDS AN ECOSYSTEM FOR
PRIVACY RESPECTING ANALYSIS
OF DISTRIBUTED HEALTH DATA
Wessel Kraaij (TNO and Leiden University) and Marc van Lieshout (TNO)
2. OVERVIEW
Introduction: big data in health applications, privacy risks
The right to privacy
Personal data: interests of researchers vs. data subjects
FAIR & RESPECT4U
Project outlines
PIME – a privacy respecting data platform with transparency features
PRANA – privacy respecting data analytics in health care settings
07 June 20162 | Privacy respecting approach in health care applications
Respo
nsible
Empo
wering
Sec
ure
Proac
tive
Ethi
cal
Contr
olled
Transp
arent
3. BIG DATA IN HEALTH CARE
07 June 20163 | Privacy respecting approach in health care applications
http://www-03.ibm.com/press/us/en/photo/40728.wss
4. QUANTIFIED SELF
4
bron: MIT
Quantified Self
A DIY movement aiming for
improved self knowledge by
using tracking technology
(sensors and apps).
Gary Wolf (Wired): “Almost
everything we do generates
data”.
bron: RescueTime
5. FROM POPULATION AVERAGES
TOWARDS INDIVIDUAL TREATMENT
5
Van ‘big’ naar ik
Bron: cbw.ge en wikimedia.org
Contributing towards
Reference population
Interpretation of
QS data needs
contrasting peer
data.
6. 07 June 20166 | Privacy respecting approach in health care applications
BMC (October 2015)
66% of tested health apps (#79) which all were accredited
according to the UK NHS accreditation scheme did not use data
encryption
90% of apps tested transmit data to the cloud
20% of apps did not have a privacy policy
78% of those with a privacy policy did not adequately describe the
nature of personal information that was transmitted
Serious risk for unforeseen and unwanted dissemination of data to
third party services without clear notification to and consent by the
end user.
APPS FOR HEALTHY LIVING
7. SO WHAT?
Distrust in EHR systems is high.
Data protection regulation in EU has been strengthened.
It is more difficult to do studies that aggregate patients across different
hospitals or countries.
The development of precision medicine and personalized health meets a
serious technical and legal barrier.
A possibility for more efficient and more effective health care is delayed
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WE NEED INNOVATIONS IN DATA
MANAGEMENT AND GOVERNANCE
8. #1: FAIR DATA: FINDABLE, ACCESSIBLE,
INTEROPERABLE, REUSABLE
Solution to increase the impact of public research.
Data should be accessible, to reproduce results
How about patient data?
07 June 20168 | Privacy respecting approach in health care applications
9. BUT PRIVACY IS A FUNDAMENTAL RIGHT
07 June 20169 | Privacy respecting approach in health care applications
EU Charter of Fundamental Rights (2009)
Article 7: Respect for private and family life: Everyone has the right to
respect for his or her private and family life, home and
communications.
Article 8: Protection of personal data: Everyone has the right to the
protection of personal data concerning him or her.
The Dutch Constitution:
Safeguards in article 10 (private life), article 11 (the body), article
12 (the home), article 13 (communications)
11. MOVING TO PRACTICE: PIME AND PRANA
Two technology valorization programmes (EIT Digital and COMMIT/) funding
two separate streams of research
PIME (Personal Information Management Ecosystems)
Focus on patient self management
Dedicated middleware platform with several privacy and security features
Privacy and transparency dashboard to help patients keeping control over
their data
PRANA (Privacy Respecting ANAlysis of health data)
Focus on analysis of aggregated distributed health data
Looking for ways to enhance privacy respecting analysis of patient data
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12. 07 June 201612 | Privacy respecting approach in health care applications
PIME
13. PERSONAL DATA STORE WITH ACCESS
CONTROL POLICIES
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A set of permissions (permit or deny) or obligations based on
conditions
Conditions use comparisons on attributes and their specified values
Traditional AC applications are in the computer networks firewalls and
building security and are usually ROLE-based
New access control applications are in controlled credit cards,
controlled cell phones and access to structured documents
There is a shift underway to ABAC (attribute based access control)
With our PDS we’re talking about Cell-Based Access Control (CBAC)*
14. PROOF OF THE PUDDING
PIME pilot
Middleware platform with privacy dashboard for integrated birth control
Province of Noord Holland; small pilot (few tens of patients)
TNO/Synergetics for organising patient consent (and control!)
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ONATAL
15. PRANA DATA
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16. RESEARCH QUESTION
How to perform privacy respecting analysis on sensitive data
that is distributed and should not be disclosed to the parties that perform the analysis?
Data protection and processing by design
Informed consent based transparency
Privacy respecting analysis of distributed data repositories
Provide proof of principles in 2 use cases:
Research setting: MUMC and UMCG development of distributed learning technology
focused on lung cancer prediction models
Patient setting: relate individual health data to the best matching patient profiles, while
respecting data protection rules, informed consent settings and data location
Privacy respecting analyses on
patient data without revealing data
2 Proof of Principles:
Research Setting
Patient setting
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17. PERSONAL HEALTH TRAIN
If it is impossible to bring the data to the learner / model (a centralized
approach)
just bring the learner to the data ( a distributed approach)
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http://www.dtls.nl/fair-data/personal-health-train/
Andre Dekker, MUMC
Bram Peter ‘t Hoen,
LUMC
DTL
https://vimeo.com/138977162
18. CONCLUSIONS
Increasing need for sophisticated solutions that bring together:
Patients’ need for privacy respecting approaches
Patients’ need for transparency
Health care providers’ need for advanced data analytics
Working –with various stakeholders- on solutions that meet
FAIR principles (Findable – Accessible – Interoperable – Reusable)
RESPECT4U principles (Responsible – Empowering – Secure – Pro-active
– Ethical – Controlled – Transparent)
Experimentation with
Real patients – health care providers
Technology
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19. THANK YOU FOR YOUR ATTENTION
Wessel.kraaij@tno.nl marc.vanlieshout@tno.nl