DISEASE PREDICTION BY MACHINE LEARNING OVER BIG DATA FROM HEALTHCARE COMMUNI...Nexgen Technology
GET IEEE BIG DATA,JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
following topics are discussed inside the PPT:
Introduction
Objective
Motivation
Literature Survey
Some Key Features of Disease
Plan of Action
Methodology Adopted
Data Collection
Steps to be Performed
Functional Architecture
We are predicting Heart Disease by Taking 14 Medical Parameters as an inputs through 2 data Minning Techniques(Decision Tree(Faster) And KNN neighbour Algorithms(Slower)).
And Visualizing The dataset.If the output 1 then it means Higher Chances of getting Heart Attack ,if 0 then it means Less chances of Heart Attack.
DISEASE PREDICTION BY MACHINE LEARNING OVER BIG DATA FROM HEALTHCARE COMMUNI...Nexgen Technology
GET IEEE BIG DATA,JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
following topics are discussed inside the PPT:
Introduction
Objective
Motivation
Literature Survey
Some Key Features of Disease
Plan of Action
Methodology Adopted
Data Collection
Steps to be Performed
Functional Architecture
We are predicting Heart Disease by Taking 14 Medical Parameters as an inputs through 2 data Minning Techniques(Decision Tree(Faster) And KNN neighbour Algorithms(Slower)).
And Visualizing The dataset.If the output 1 then it means Higher Chances of getting Heart Attack ,if 0 then it means Less chances of Heart Attack.
Machine Learning Project - Default credit card clients Vatsal N Shah
- The model we built here will use all possible factors to predict data on customers to find who are defaulters and non‐defaulters next month.
- The goal is to find the whether the clients are able to pay their next month credit amount.
- Identify some potential customers for the bank who can settle their credit balance.
- To determine if their customers could make the credit card payments on‐time.
- Default is the failure to pay interest or principal on a loan or credit card payment.
Fraud detection is a popular application of Machine Learning. But is not that obvious and not that common as it seems. I'll tell how QuantUp implemented it for WARTA insurance company (a subsidiary of Talanx International AG).
The models developed gave between 10% and 30% of reduction of losses. The project was not a simple one because of the complex process of handling claims and using really rich dataset. The tools applied were R (modeling) and DataWalk (data peparation). You will learn what is important in development of such solutions in general, what was difficult in this particular project, and how to overcome possible difficulties in similar projects.
Machine Learning for Disease PredictionMustafa Oğuz
A great application field of machine learning is predicting diseases. This presentation introduces what is preventable diseases and deaths. Then examines three diverse papers to explain what has been done in the field and how the technology works. Finishes with future possibilities and enablers of the disease prediction technology.
Large amounts of heterogeneous medical data have become available in various healthcare organizations (payers, providers, pharmaceuticals). Those data could be an enabling resource for deriving insights for improving care delivery and reducing waste. The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment. More details are available here http://dmkd.cs.wayne.edu/TUTORIAL/Healthcare/
This project aims to determine the housing prices of California properties for new sellers and also for buyers to estimate the profitability of the deal using various regression models.
Below are the details of the models implemented and their performance score:
Linear Regression: RMSE- 68321.7051304
Decision Tree Regressor: RMSE- 70269.5738668
Random Forest Regressor: RMSE- 52909.1080535
Support Vector Regressor: RMSE- 110914.791356
Fine Tuning the Hyperparameters for Random Forest Regressor: RMSE- 49261.2835608
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
Data mining techniques are used for a variety of applications. In healthcare industry, datamining plays an important
role in predicting diseases. For detecting a disease number of tests should be required from the patient. But using data
mining technique the number of tests can be reduced. This reduced test plays an important role in time and performance.
This report analyses data mining techniques which can be used for predicting different types of diseases. This report reviewed
the research papers which mainly concentrate on predicting various disease
Presentation on Predictive modeling in Health-care at San Jose, Ca 2015. This presentation talks about healthcare industry in US, provides stats and forecasts. It then discusses a few use cases in health care and goes into detail on a kaggle example.
A Heart Disease Prediction Model using Logistic Regression By Cleveland DataBaseijtsrd
The early prognosis of cardiovascular diseases can aid in making decisions to lifestyle changes in high risk patients and in turn reduce their complications. Research has attempted to pinpoint the most influential factors of heart disease as well as accurately predict the overall risk using homogenous data mining techniques. Recent research has delved into amalgamating these techniques using approaches such as hybrid data mining algorithms. This paper proposes a rule based model to compare the accuracies of applying rules to the individual results of logistic regression on the Cleveland Heart Disease Database in order to present an accurate model of predicting heart disease. K. Sandhya Rani | M. Sai Chaitanya | G. Sai Kiran"A Heart Disease Prediction Model using Logistic Regression By Cleveland DataBase" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11402.pdf http://www.ijtsrd.com/computer-science/data-miining/11402/a-heart-disease-prediction-model-using-logistic-regression-by-cleveland-database/k-sandhya-rani
TeraCrunch: Transforming Organizations with Machien Learning and Gen-AI Solut...KC Digital Drive
These slides were presented at the February 2024 meeting of the KC Digital Drive Health Innovation Team.
This presentation was given by Tera Crunch. As they describe things: Let's face it – navigating the AI world can be like walking through a maze. You’ve got IT firms moonlighting as AI gurus, in-house teams juggling too much, and bespoke solution shops charging an arm and a leg. That’s where we come in. TeraCrunch is not just another AI & Gen-AI company. We’re the experienced friend you call when you need results without the runaround. We've been around the block for 11 years, building over 150 solutions with a track record of 5-40x ROI.
What makes us different? We cut through the complexities and unnecessary costs with our secret sauce – a blend of proprietary methods and pre-developed tech-stack we've honed for over a decade – getting you the results you need, fast. Plus, our data scientists with roots in places like Harvard and NASA, roll up their sleeves and get to work with your crew. With TeraCrunch, you’re choosing a partner that makes the complex simple and the uncertain sure.
Our presenter will be CEO Tapan Bhatt. With over 19 years of experience in developing cutting-edge technology products and fueling growth in start-up ventures, Tapan's unrivaled insights have been instrumental in driving success for numerous venture capital-backed high-tech startups across both coasts. Prior to establishing TeraCrunch, he held key leadership positions in a series of technology startups that achieved remarkable success. As Head of Business Development at ROAM (acquired by Ingenico), Head of Solution Sales & Sales Engineering at AisleBuyer(acquired by Intuit), Executive Director at Motricity (IPO in 2010, NASDAQ:MOTR), and Executive Director at Amobee Media Systems (acquired by SingTel).
Tapan has an MBA in Marketing from Avila University and a foundation in Electrical Engineering from K.K.Wagh College of Engineering. As part of his many external activities and roles, he is an Innovation Board Member of St. Luke's Health System.
Machine Learning Project - Default credit card clients Vatsal N Shah
- The model we built here will use all possible factors to predict data on customers to find who are defaulters and non‐defaulters next month.
- The goal is to find the whether the clients are able to pay their next month credit amount.
- Identify some potential customers for the bank who can settle their credit balance.
- To determine if their customers could make the credit card payments on‐time.
- Default is the failure to pay interest or principal on a loan or credit card payment.
Fraud detection is a popular application of Machine Learning. But is not that obvious and not that common as it seems. I'll tell how QuantUp implemented it for WARTA insurance company (a subsidiary of Talanx International AG).
The models developed gave between 10% and 30% of reduction of losses. The project was not a simple one because of the complex process of handling claims and using really rich dataset. The tools applied were R (modeling) and DataWalk (data peparation). You will learn what is important in development of such solutions in general, what was difficult in this particular project, and how to overcome possible difficulties in similar projects.
Machine Learning for Disease PredictionMustafa Oğuz
A great application field of machine learning is predicting diseases. This presentation introduces what is preventable diseases and deaths. Then examines three diverse papers to explain what has been done in the field and how the technology works. Finishes with future possibilities and enablers of the disease prediction technology.
Large amounts of heterogeneous medical data have become available in various healthcare organizations (payers, providers, pharmaceuticals). Those data could be an enabling resource for deriving insights for improving care delivery and reducing waste. The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment. More details are available here http://dmkd.cs.wayne.edu/TUTORIAL/Healthcare/
This project aims to determine the housing prices of California properties for new sellers and also for buyers to estimate the profitability of the deal using various regression models.
Below are the details of the models implemented and their performance score:
Linear Regression: RMSE- 68321.7051304
Decision Tree Regressor: RMSE- 70269.5738668
Random Forest Regressor: RMSE- 52909.1080535
Support Vector Regressor: RMSE- 110914.791356
Fine Tuning the Hyperparameters for Random Forest Regressor: RMSE- 49261.2835608
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
Data mining techniques are used for a variety of applications. In healthcare industry, datamining plays an important
role in predicting diseases. For detecting a disease number of tests should be required from the patient. But using data
mining technique the number of tests can be reduced. This reduced test plays an important role in time and performance.
This report analyses data mining techniques which can be used for predicting different types of diseases. This report reviewed
the research papers which mainly concentrate on predicting various disease
Presentation on Predictive modeling in Health-care at San Jose, Ca 2015. This presentation talks about healthcare industry in US, provides stats and forecasts. It then discusses a few use cases in health care and goes into detail on a kaggle example.
A Heart Disease Prediction Model using Logistic Regression By Cleveland DataBaseijtsrd
The early prognosis of cardiovascular diseases can aid in making decisions to lifestyle changes in high risk patients and in turn reduce their complications. Research has attempted to pinpoint the most influential factors of heart disease as well as accurately predict the overall risk using homogenous data mining techniques. Recent research has delved into amalgamating these techniques using approaches such as hybrid data mining algorithms. This paper proposes a rule based model to compare the accuracies of applying rules to the individual results of logistic regression on the Cleveland Heart Disease Database in order to present an accurate model of predicting heart disease. K. Sandhya Rani | M. Sai Chaitanya | G. Sai Kiran"A Heart Disease Prediction Model using Logistic Regression By Cleveland DataBase" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11402.pdf http://www.ijtsrd.com/computer-science/data-miining/11402/a-heart-disease-prediction-model-using-logistic-regression-by-cleveland-database/k-sandhya-rani
TeraCrunch: Transforming Organizations with Machien Learning and Gen-AI Solut...KC Digital Drive
These slides were presented at the February 2024 meeting of the KC Digital Drive Health Innovation Team.
This presentation was given by Tera Crunch. As they describe things: Let's face it – navigating the AI world can be like walking through a maze. You’ve got IT firms moonlighting as AI gurus, in-house teams juggling too much, and bespoke solution shops charging an arm and a leg. That’s where we come in. TeraCrunch is not just another AI & Gen-AI company. We’re the experienced friend you call when you need results without the runaround. We've been around the block for 11 years, building over 150 solutions with a track record of 5-40x ROI.
What makes us different? We cut through the complexities and unnecessary costs with our secret sauce – a blend of proprietary methods and pre-developed tech-stack we've honed for over a decade – getting you the results you need, fast. Plus, our data scientists with roots in places like Harvard and NASA, roll up their sleeves and get to work with your crew. With TeraCrunch, you’re choosing a partner that makes the complex simple and the uncertain sure.
Our presenter will be CEO Tapan Bhatt. With over 19 years of experience in developing cutting-edge technology products and fueling growth in start-up ventures, Tapan's unrivaled insights have been instrumental in driving success for numerous venture capital-backed high-tech startups across both coasts. Prior to establishing TeraCrunch, he held key leadership positions in a series of technology startups that achieved remarkable success. As Head of Business Development at ROAM (acquired by Ingenico), Head of Solution Sales & Sales Engineering at AisleBuyer(acquired by Intuit), Executive Director at Motricity (IPO in 2010, NASDAQ:MOTR), and Executive Director at Amobee Media Systems (acquired by SingTel).
Tapan has an MBA in Marketing from Avila University and a foundation in Electrical Engineering from K.K.Wagh College of Engineering. As part of his many external activities and roles, he is an Innovation Board Member of St. Luke's Health System.
A Multi-Pronged Approach to Data Mining Post-Acute Care EpisodesDamian R. Mingle, MBA
This study evaluates the opportunities available to Post-Acute Care providers who want to participate in redesigning their segment of the care continuum, specific to the Bundled Payments for Care Improvement Initiative (BPCI). We clarify how the BPCI Model 3 episodes of care are defined, the financial risk assumed by applicants, and the partnerships needed to mitigate risk by care coordination and redesign of clinical strategy. Furthermore, using data mining techniques, applied statistics, and applied contextual science, we present findings through visualizations enabling data discovery and accountability.
Data Mining in Healthcare: How Health Systems Can Improve Quality and Reduce...Health Catalyst
This is the complete 4-part series demonstrating real-world examples of the power of data mining in healthcare. Effective data mining requires a three-system approach: the analytics system (including an EDW), the content system (and systematically applying evidence-based best practices to care delivery), and the deployment system (driving change management throughout the organization and implementing a dedicated team structure). Here, we also show organizations with successful data-mining-application in critical areas such as: tracking fee-for-service and value-based payer contracts, population health management initiatives involving primary care reporting, and reducing hospital readmissions. Having the data and tools to use data mining and predict trends is giving these health systems a big advantage.
As per the Market Data Forecast report, the global healthcare prescriptive analytics market is likely to grow at a CAGR of 17.4% from 2022-2027. Organizations use prescriptive analytics to predict outcomes and to identify the logical course of action.
Prescriptive Analytics of user-generated data in the healthcare domain indicates what is likely to occur and suggests the best actions to avoid and mitigate risks. To know more about how healthcare is optimizing its operations with prescriptive analytics
Business Analytics in healthcare industry.pptxGauravMalve2
Hey there!
Exciting news – we're diving into the fascinating world of Business Analytics in the Healthcare sector, and I've just uploaded a killer PowerPoint presentation on SlideShare that you won't want to miss!
🏥 Title: Unveiling the Power of Business Analytics in Healthcare
🚀 Description:
Hey, fellow data enthusiasts! 👋 Get ready to embark on a journey through the dynamic realm where business analytics meets healthcare. Our latest presentation explores the impactful synergy between data-driven insights and the healthcare sector's ever-evolving landscape.
👉 Key Highlights:
Uncovering the role of analytics in optimizing healthcare operations.
Real-world examples showcasing improved patient outcomes through data analysis.
Navigating the challenges and opportunities in healthcare analytics.
Future trends that promise to reshape the healthcare analytics landscape.
🌐 SlideShare Link: Business Analytics in Healthcare
👀 Why You Should Check it Out:
Whether you're a healthcare professional, data enthusiast, or just someone intrigued by the magic that happens when numbers meet healthcare, this presentation is tailor-made for you! Gain insights, spark discussions, and stay ahead of the curve in understanding how analytics is revolutionizing the healthcare game.
Ready to elevate your understanding of business analytics in healthcare? Click the link above and let the learning begin! 🚀
Feel free to share with your network and dive into the discussion. Let's amplify the conversation around data-driven healthcare together!
Cheers !!!
The convergence of separate health systems has led to
a great increase in data, which some organisations are
struggling to get to grips with. Harnessing analytic tools
and sharing knowledge is the best way forward
In this article, Jim Hoffman, COO of BESLER Consulting, discusses current uses of predictive analytics in healthcare. It was featured in the September 2014 edition of Managing Health Today, a publication of the Hudson Valley Chapter of HFMA.
A hybrid approach to data management is emerging in healthcare as organizations recognize the value of an enterprise data warehouse in combination with a data lake.
In this SlideShare, we discuss data lakes in healthcare and we:
Provide an overview of a Hadoop-based data lake architecture and integration platform, and its application in machine learning, predictive modeling, and data discovery
Discuss several key use cases driving the adoption of data lakes for both providers and health plans
Discuss available data storage forms and the required tools for a data lake environment
Detail best practices for conducting data lake assessments and review key implementation considerations for healthcare
Predictive Analytics: It's The Intervention That MattersHealth Catalyst
In this two-part webinar, get the detailed knowledge you need to make informed decisions about adopting predictive analytics in healthcare so you can separate today's hype from reality. In part 1, you'll learn key learnings from Dale Sanders including 1) our fixation on predictive analytics in readmissions, 2) the common trap of predictions without interventions, 3) the common misconceptions of correlations verses causation, 4) examples of predictions without algorithms, and 5) the importance of putting the basics first.
In part 2, you'll hear from industry expert David Crockett, PhD in a "graduate level" crash course cover key concepts such as machine learning, algorithms, feature selection, classification, tools and more.
Providers know that successful care coordination is key to enhancing patient outcomes and better personalizing their experience. At its root, care coordination starts with effective communication, and healthcare organizations are increasingly turning to innovative technology solutions to solve their needs. To improve their care teams’ communication, coordination, and data capture capabilities, two of New York City’s leading healthcare organizations worked with two cutting edge tech solutions providers to design and implement innovative pilots as a part of the New York Digital Health Accelerator program. Utilizing real-life case studies, the panelists will discuss the design and implementation of the pilots, and lessons learned from their participation in the program.
• Anuj Desai - Vice President of Market Development, New York eHealth Collaborative
• Joseph Mayer, MD - Founder & CEO, Cureatr Inc.
• Patricia Meisner, MS, MBA - CEO & Co-Founder, ActualMeds
• Ken Ong, MD, MPH - Chief Medical Informatics Officer, New York Hospital Queens
• Victoria Tiase, MSN, RN - Director, Informatics Strategy, NewYork-Presbyterian Hospital
New York eHealth Collaborative Digital Health Conference
November 17, 2014
Unit VI Case StudyAnimal use in toxicity testing has long been .docxdickonsondorris
Unit VI: Case Study
Animal use in toxicity testing has long been a controversial issue; however, there can be benefits. Read “The Use of Animals in Research,” which is an article that can be retrieved from http://www.toxicology.org/pubs/docs/air/AIR_Final.pdf.
Evaluate the current policies outlined in the Position Statement on page 5 of the article. Use the SOT Guiding Principles in the Use of Animals in Toxicology to guide you in your analysis. Feel free to use additional information and avenues of information, including the textbook, to critically analyze this policy.
In addition, answer the following questions:
How do toxicologists determine which exposures may cause adverse health effects?
How does the information apply to what you are learning in the course?
What were the objectives of this toxicity testing?
What were the endpoints of this toxicity testing?
Finally, include whether or not you agree with the Society of Toxicology's position on animal testing.
Your Case Study assignment should be three to four pages in length. Use APA style guidelines in writing this assignment, following APA rules for formatting, quoting, paraphrasing, citing, and referencing.
Adventure Works Marketing Plan
Centralizing Medical Information To Improve Patient Care
(
Centralizing Medical Information To Improve patient Care
)
Contents
Centralizing Medical Information To Improve patient Care0
Contents1
History2
Executive Summary2
High-Level Functional Requirements:4
Project Charter4
Business Problem Statement5
Project Scope5
Budget and Schedule6
Strategy6
SWOT ANALYSIS6
Technology Constraints7
Project Documentation and Communication9
Project Organization and Staffing Approach9
Project Value Statement9
History
The Affordable Care Act law was passed to improve healthcare for its citizens in the United States by increasing the people that have health insurance and by decreasing healthcare cost. A benefactor to this law is the Medicare/Medicaid program which provides medical coverage to the poor, elderly and disabled individuals which is funded by the federal government. The Federal government covers funding for Medicare programs while it provides reimbursement funds for Medicaid programs provided by the states. (The National Federation Of Independent Business V Sebellius, Secretary Of Health And Human Services, 2012). The primary benefits of the Affordable Care Act Law are covering more consumers with improved quality of services while reducing healthcare cost, access to healthcare, and consumer protection. (ASPA, 2014) Centers For Medicare and Medicaid Services (CMS) manages both of these programs and by modernizing and strengthening the current system they will be lowering cost and providing quality care. Executive Summary
The Center for Medicare and Medicaid (CMS) is the federal office to organized the integration of Medicaid and Medicare services across multiple agencies nationwide. Its purpose is to improve access to services, ...
How the Business Model is becoming disruptive and aligning to it how technology is being disruptive. Defining the process and model for this Disruptive technologies
Sharing my thoughts with the rapid expansion of technology how the architects and software engineering community with great concepts which helps in managing complexity and help to develop in shorter period of time with quality
Based on experience as Architect. Tried to provide some thoughts on what are the important qualities Architect has to develop for emerging Technology complex environment
How the IT system can match with meeting demands of fast growing needs to business and some essential parameters which could help them in achieving it.
This explains how the different Business, Mobile, Data Analytics has impact on the IT of the organization. In subsequent slides would provide how this could be managed
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
2. PREDICTIVE ANALYTICS ?
• Predictive analytics is the practice of extracting information from existing
data sets in order to determine patterns and predict future outcomes and
trends. It does not tell you what will happen in the future but forecasts what
might happen in the future with an acceptable level of reliability, and
includes what-if scenarios and risk assessment.
3. PREDICTIVE ANALYTICS & BIG DATA
• Predictive analytics is an enabler of big data: Businesses collect vast amounts
of real-time customer data and predictive analytics uses this historical data,
combined with customer insight, to predict future events. Predictive analytics
enable organizations to use big data (both stored and real-time) to move
from a historical view to a forward-looking perspective of the customer.
4. HEALTHCARE ANALYTICS MARKET
• Real time analytics is carried out on the spot and helps in quick decision
making, for instance, clinical decision support software with active
knowledge systems use two or more items of patient data to generate case-
specific advice.
• Batch Analytics retrospectively evaluates past data such as patient records
and claims data from an insured population, which helps in predictive
modeling and cost control measures.
6. REQUIREMENT :
• Predictive analytics service providers generally start by studying the
characteristics of people who have already purchased a product from an
insurer,
• and then develop a profile — or model — of the kind of person who buys
that specific insurance product.
7. VARIABLES IN PREDICTIVE ANALYTICS
ALGORITHM
• Predicting which policy holders (or potential policy holders) will make a
claim
• And how long it will be until they make the claim.
• The more data available on the history of claims
• And ‘extraneous’ information about the policy holder
9. CASE STUDY
In this case analysis of hospital data was done to
optimize and balance human resources, medication
and time spent on each patient to improve clinical
outcomes. Fig.1 performs spectral partitioning of
the graph that was built using the data from the
health-care agency. Understanding the structure of
the data and capturing hidden interrelationships
helped to improve the existing resource allocation
schema. As a result created a model of resource
harness that stopped overspending and improved
the quality of patient's care.
10. Contd……….
In this case analysis of patient’s symptoms was taken to predict
the development of the disease. Fig.2 demonstrates principal
component analysis and support vector machine classifier.
Healthcare data analytics allows us to find patterns that help to
recognize early stages of the disease and predict its
development. This predictive model provides the hospital with an
opportunity to control the occurrence of epidemics as well as be
more accurate in early diagnosis of the disease.
11. MODEL EVALUATION
• Entries will be judged by comparing
• The predicted number of days a member will spend in the hospital with the actual number
of days a member spent in the hospital in DaysInHospital_Y4 (not given to competitors)
• Prediction accuracy will be evaluated based on the following metric
• The objective function for the model to minimize
where
1. i is a member;
2. n is the total number of members;
3. pred is the predicted number of days spent in hospital for member i in the test period;
4. act is the actual number of days spent in hospital for member i in the test period.
12. IMPROVING HEALTH CARE DELIVERY
• To identify when patients are likely to have a hospital stay
• And to direct health care providers to take preventative actions to avoid the
hospital stay.
• Prediction of product demand,
• Options prices,
• Turnover likelihood of sales leads.
13. APPLICATION
Model drug development collaborations that maximize IP and drug
discovery.
Simulate PRO (Patient Reported Outcomes) for care quality
improvement and outcomes.
Accelerate time to market for new therapies with strategic portfolio
modeling
Predict market access and optimize resource allocation for new
therapies
Predict high risk patients for ACO (accountable care organization)
and hospitals.
Leverage advanced analytics to reduce hospital readmissions
Simulate connected health consumer and recommend technology
interventions that drive healthy behavior change.
Simulate the financial risks and incentives of emerging
reimbursement models for ACO.