PMED: APPM Workshop: Personalized Patient Vital Sign Monitoring in Home, Hospital & Clinical Settings - Christopher McCann, March 14, 2019
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This talk was presented at the Advances in Precision and Personalized Medicine during Session 3: Medical Device Analytics
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Connected Health & Me - Matic Meglic - Nov 24th 2014
This document discusses how data sharing is changing healthcare by empowering patients. It outlines a shift from a traditional care model, where patients are passive recipients of care, to one where patients are engaged and empowered through access to their own health data and contextual knowledge. Key drivers of this change include affordable technology, the quantified self-movement, big data, and empowered patients. The document discusses how patient registries and personalized medicine can utilize data to better understand treatment efficacy for similar patients and provide personalized care plans. It also notes challenges around data privacy and the need for guidelines. Overall, the document advocates for empowering patients through access to their own health data while using data and technology to coordinate and improve healthcare.
Overview of Patient Reported Outcomes in SAFTINet
This document discusses patient-reported outcomes (PROs) in the SAFTINet and PEC studies. It defines a PRO as a questionnaire collected directly from patients in clinical trials or settings. PROs can measure disease control and be used for screening, monitoring, feedback, decision-making, communication, and evaluating quality. The document outlines upcoming agenda items for meetings discussing how partners currently collect and use PROs, barriers to implementation, and potential use cases for an asthma PRO measure.
How to Use Data to Improve Patient Safety: A Two-Part Discussion
As healthcare organizations continue to experience expenses growing faster than revenues, value based care, and consumer transparency of costs and quality, patient safety will be an important determinant of success. This session will describe the sociotechnical attributes of a safe system, the challenges, the barriers and opportunities, and how to use data and your culture of safety as a powerful tool to drive down adverse events.
Attendees will learn:
Why patient safety and quality are important.
How data can help improve patient safety.
The history of patient safety and where we are today.
What components make up a safety analytics culture.
How the internal safety culture directly impacts patient safety metrics.
To describe basic guidelines for improving a safety culture with analytics.
World Drug Safety berlin sept 2017
Attached is a view on the importance of Automation & appropriate technology in the collection of Adverse Event data, and how this data can be used to the benefit of the patient population.
Using Digital Innovation to Establish Authentic Reporter Dialogue
Digital solutions that put patients at forefront of safety processes
Capture relevant, essential and complete data at first interaction
Maximise the value of initial contact and reduce low value follow up
Solutions that ensure REMS and RMP commitments are met and are future proofed
Big Data, Small Data
The document discusses the potential for mobile health (mHealth) technologies and small data sensing. It describes how sensors in smartphones can be used to monitor health metrics and manage chronic diseases. Some examples discussed include using phone sensors and microphones to monitor lung function for conditions like asthma. Challenges around compliance, cost, and data reliability are also covered. The future of mHealth is predicted to involve integrating more sensors directly into phones and greater involvement of regulatory agencies like the FDA.
What's Next In Connected Health
Is it self-tracking? We are only beginning to understand the power of self-tracking be it due to the quantified self movement or because of the increasing number of connected medical devices. A real opportunity is in understanding how mobile devices will play a key role in the future of our personal health. Medical Devices, sensors, big data, cloud computing are and will continue to enable continuous monitoring of people and patients.
Analytics in healthcare bhuvaneashwar 11th_march
Presentation on Analytics applications in Healthcare, delivered at IIM Bangalore as part of a masterclass on healthcare analytics
Pegwin pitch deck
The Pegwin Insights software platform is used by caregivers at the point of care to measure and monitor a patient’s risk profile and receive early insight into a patient’s deterioration so that timely intervention and preventive action can be taken to avoid post-operative complications. Pegwin Insights is the only evidence-based Machine Learning and AI solution to assist caregivers with better-than-human accuracy in real-time.
hitppd-110109065920.pdf
Information technology refers to hardware and software used to store, process, transmit, and securely retrieve information. Computers are reshaping healthcare by connecting different departments and enabling storage and retrieval of clinical and statistical data. Some key applications of computers in nursing include computerized hospital information systems, clinical decision support, electronic medical records, and medication administration records.
Big Data and Analytic Strategy for Clinical Research
This document discusses how big data and analytics can help simplify clinical research and make trials more cost-effective. It begins by providing context on how Henry Ford revolutionized car manufacturing using specialized machinery and standardized processes. Similarly, big data is creating a radical shift in how research is conducted by enabling the analysis of large and complex datasets. The rest of the document outlines opportunities in areas like personalized medicine, challenges like dealing with diverse and fast-changing data, and how innovation in clinical research design can help address these challenges to develop more targeted treatments.
Apport du Big Data pour la médecine personnalisée
This document discusses how integrated computing and data can lead to improved healthcare outcomes through precision medicine. It provides examples of how large healthcare data sets from various sources can be analyzed using machine learning to better predict and treat conditions like heart failure. Penn Medicine is highlighted as successfully using patients' electronic medical records, medications, and other data to improve predictive models for re-hospitalization risk. The document also introduces the Trusted Analytics Platform and Intel's Collaborative Cancer Cloud initiative for enabling genomic research through distributed analytics. Finally, it describes how natural language processing of clinical records could help identify cancer patients for clinical trials more quickly.
Intel telecom paris tech 20160616
This document discusses how integrated computing and data can lead to improved healthcare outcomes through precision medicine. It provides examples of how large healthcare systems like Penn Medicine are using machine learning on patient data to better predict and treat conditions like heart failure. The document also introduces the Trusted Analytics Platform and Intel's Collaborative Cancer Cloud which aim to accelerate big data analytics for medical research. Finally, it discusses how natural language processing of clinical records through the ConSoRe project could help oncologists more quickly identify patient cohorts for clinical trials and research.
Pharma challenges - Patient Centricity and Digital Capabilities
Today pharma's business model is being challenged. The industry needs to rethink how it creates value. In particular, it needs to connect to patients and caregivers in a meaningful way. It many cases this connection can be guaranteed through digital tools and strategies. This presentation focuses on these challenges and showcases some best practices that are already available in the marketplace.
IT in Private Cardiology Practice, 2011
This is a presentation from 2011 highlighting the possibilities of IT in private cardiology practice. It is of historical value but touches on early fundamental concepts of digitalization of a private practice in the field of cardiology.
Theranos Original Pitchdeck 2006
Theranos presented to investors with the goal of becoming the standard for improving drug therapies. Its initial market is phase IV clinical trials, using a product platform of cartridges, readers, and informatics services. Founded in 2003 with 50 employees, Theranos aims to generate $120-300M in revenue over 1.5 years through deals with pharmaceutical companies. The company's system allows for simultaneous quantitative measurement of drugs and biomarkers from small blood samples, returning results comparable to gold standards within 30 minutes. Theranos' value proposition is improving drug labels and safety by enabling real-time pharmacokinetic and pharmacodynamic monitoring during trials and treatment.
Ontology-Driven Clinical Intelligence: A Path from the Biobank to Cross-Disea...
The discovery of clinical insights through effective management and reuse of data requires several conditions to be optimized: Data need to be digital, data need to be structured, and data need to be standardized in terms of metadata and ontology. This presentation describes a bioinformatics system that combines a next-generation biobank management model mapped to applicable international standards and guidelines with a master ontology that controls all input and output and is able to add unique properties to meet the specialized needs of clinicians for cross-disease research.
Similar to PMED: APPM Workshop: Personalized Patient Vital Sign Monitoring in Home, Hospital & Clinical Settings - Christopher McCann, March 14, 2019 (20)
Medical Device Integration for Patient Centered Health Care
Medical Device Integration for Patient Centered Health Care
Connected Health & Me - Matic Meglic - Nov 24th 2014
Connected Health & Me - Matic Meglic - Nov 24th 2014
How to Use Data to Improve Patient Safety: A Two-Part Discussion
How to Use Data to Improve Patient Safety: A Two-Part Discussion
Using Digital Innovation to Establish Authentic Reporter Dialogue
Using Digital Innovation to Establish Authentic Reporter Dialogue
Big Data and Analytic Strategy for Clinical Research
Big Data and Analytic Strategy for Clinical Research
Pharma challenges - Patient Centricity and Digital Capabilities
Pharma challenges - Patient Centricity and Digital Capabilities
Ontology-Driven Clinical Intelligence: A Path from the Biobank to Cross-Disea...
Ontology-Driven Clinical Intelligence: A Path from the Biobank to Cross-Disea...
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We discuss a general roadmap for generating causal inference based on observational studies used to general real world evidence. We review targeted minimum loss estimation (TMLE), which provides a general template for the construction of asymptotically efficient plug-in estimators of a target estimand for realistic (i.e, infinite dimensional) statistical models. TMLE is a two stage procedure that first involves using ensemble machine learning termed super-learning to estimate the relevant stochastic relations between the treatment, censoring, covariates and outcome of interest. The super-learner allows one to fully utilize all the advances in machine learning (in addition to more conventional parametric model based estimators) to build a single most powerful ensemble machine learning algorithm. We present Highly Adaptive Lasso as an important machine learning algorithm to include.
In the second step, the TMLE involves maximizing a parametric likelihood along a so-called least favorable parametric model through the super-learner fit of the relevant stochastic relations in the observed data. This second step bridges the state of the art in machine learning to estimators of target estimands for which statistical inference is available (i.e, confidence intervals, p-values etc). We also review recent advances in collaborative TMLE in which the fit of the treatment and censoring mechanism is tailored w.r.t. performance of TMLE. We also discuss asymptotically valid bootstrap based inference. Simulations and data analyses are provided as demonstrations.Causal Inference Opening Workshop - Bayesian Nonparametric Models for Treatme...
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HYPERTENSION - SLIDE SHARE PRESENTATION.
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Chapter wise All Notes of First year Basic Civil Engineering.pptx
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Mule event processing models | MuleSoft Mysore Meetup #47
Mule event processing models | MuleSoft Mysore Meetup #47
Event Link:- https://meetups.mulesoft.com/events/details/mulesoft-mysore-presents-mule-event-processing-models/
Agenda
● What is event processing in MuleSoft?
● Types of event processing models in Mule 4
● Distinction between the reactive, parallel, blocking & non-blocking processing
For Upcoming Meetups Join Mysore Meetup Group - https://meetups.mulesoft.com/mysore/YouTube:- youtube.com/@mulesoftmysore
Mysore WhatsApp group:- https://chat.whatsapp.com/EhqtHtCC75vCAX7gaO842N
Speaker:-
Shivani Yasaswi - https://www.linkedin.com/in/shivaniyasaswi/
Organizers:-
Shubham Chaurasia - https://www.linkedin.com/in/shubhamchaurasia1/
Giridhar Meka - https://www.linkedin.com/in/giridharmeka
Priya Shaw - https://www.linkedin.com/in/priya-shaw
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Making of a Nation.
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Social Laboratory, New Zealand,
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ملزمة تشريح الجهاز الهيكلي (نظري 3)
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1- مُترجمة ترجمة تُناسب جميع المستويات
2- تحتوي على 78 رسم توضيحي لكل كلمة موجودة بالملزمة (لكل كلمة !!!!)
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3- دقة الكتابة والصور عالية جداً جداً جداً
4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً
5- الملزمة تشرح نفسها ب نفسها بس تكلك تعال اقراني
6- تحتوي الملزمة في اول سلايد على خارطة تتضمن جميع تفرُعات معلومات الجهاز الهيكلي المذكورة في هذهِ الملزمة
واخيراً هذهِ الملزمة حلالٌ عليكم وإتمنى منكم إن تدعولي بالخير والصحة والعافية فقط
كل التوفيق زملائي وزميلاتي ، زميلكم محمد الذهبي 💊💊
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PMED: APPM Workshop: Personalized Patient Vital Sign Monitoring in Home, Hospital & Clinical Settings - Christopher McCann, March 14, 2019
- 1. Personalized Patient Vital Sign Monitoring in Home, Hospital, and Clinical Trial Settings Christopher McCann CEO Current Health Contact Information www.currenthealth.com Twitter: @heycurrent LinkedIn: https://www.linkedin.com/company/currenthealth Email: hello@currenthealth.com Facebook: www.facebook.com/heycurrenthealth
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- 7. Extended Use Cases Patient reported outcomes Dialysis ?
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- 17. Advantages in Monitoring Data Quality: Internal Trial Monitoring
- 18. Concluding Remarks • Working at the confluence of software engineers and data scientists. • Many of these methods are technically simple. • Imperative to acquire high- quality personalized data • Strong building blocks for more sophisticated data analysis
- 19. Software Data Science Thank you! Hardware
- 20. Contact Information www.currenthealth.com Twitter: @heycurrent LinkedIn: https://www.linkedin.com/company/currenthealth Email: hello@currenthealth.com Facebook: www.facebook.com/heycurrenthealth