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Reducing hospital readmissions using data science and a social twist
 
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Wexner Medical Center is running a clinical study to find reliable evidence about whether one can improve patients' behavior by drawing on their social and family circles. The study asks friends and family of cardiac patients to send text messages encouraging them to continue their rehabilitation classes. But there are implications for data mining in health care, the use of social media, and long-term approaches to chronic illness.
Views: 413 O'Reilly
Reducing Hospital Readmissions with Big Data Predictive Analytics
 
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Michael Covert, guest speaker and Big Data expert, examines how Healthcare Providers are finding ways to use Big Data analytics to reduce readmission rates and improve operational efficiency while complying with regulatory mandates. He covers: - Creating a deep learning architecture for sophisticated predictive analytics - Implementation considerations for a working predictive analytics solution: MedPredict™ - Best practices for building, monitoring and managing your Big Data analytics applications
Views: 1056 Cascading
Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission
 
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Authors: Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, Noemie Elhadad Abstract: In machine learning often a tradeoff must be made between accuracy and intelligibility. More accurate models such as boosted trees, random forests, and neural nets usually are not intelligible, but more intelligible models such as logistic regression, naive-Bayes, and single decision trees often have significantly worse accuracy. This tradeoff sometimes limits the accuracy of models that can be applied in mission-critical applications such as healthcare where being able to understand, validate, edit, and trust a learned model is important. We present two case studies where high-performance generalized additive models with pairwise interactions (GA2Ms) are applied to real healthcare problems yielding intelligible models with state-of-the-art accuracy. In the pneumonia risk prediction case study, the intelligible model uncovers surprising patterns in the data that previously had prevented complex learned models from being fielded in this domain, but because it is intelligible and modular allows these patterns to be recognized and removed. In the 30-day hospital readmission case study, we show that the same methods scale to large datasets containing hundreds of thousands of patients and thousands of attributes while remaining intelligible and providing accuracy comparable to the best (unintelligible) machine learning methods. ACM DL: http://dl.acm.org/citation.cfm?id=2788613 DOI: http://dx.doi.org/10.1145/2783258.2788613
Hospital Readmission Analysis Demo
 
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See how you can import and integrate analytical models from open-source R platform, to predict the probability of hospital readmission.
Views: 2654 MicroStrategy
Using Data Mining to Predict Hospital Admissions From the Emergency Department
 
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Using Data Mining to Predict Hospital Admissions From the Emergency Department -- The World Health Organization estimates that by 2030 there will be approximately 350 million young people (below 30 to 40 years) with various diseases associated with renal complications, stroke and peripheral vascular disease. Our aim is to analyze the risk factors and system conditions to detect disease early with prediction strategies. By using the effective methods to identify and extract key information that describes aspects of developing a prediction model, sample size and number of events, risk predictor selection. Crowding within emergency departments (EDs) can have significant negative consequences for patients. EDs therefore need to explore the use of innovative methods to improve patient flow and prevent overcrowding. One potential method is the use of data mining using machine learning techniques to predict ED admissions. This system highlights the potential utility of three common machine learning algorithms in predicting patient admissions. In this proposed approach, we considered a heart disease as a main concern and we start prediction over that disease. Because in India a strategic survey on 2015-6016 resulting that every year half-a million of people suffer from various heart diseases. Practical implementation of the models developed in this paper in decision support tools would provide a snapshot of predicted admissions from the ED at a given time, allowing for advance resource planning and the avoidance bottlenecks in patient flow, as well as comparison of predicted and actual admission rates. When interpretability is a key consideration, EDs should consider adopting logistic regression models, although GBM's will be useful where accuracy is paramount. Using the strategic algorithm such as Logistic Regression, Decision Trees and Gradient Boosted Machine, we can easily identify the disease with various attributes and risk factor specifications. Based on these parameters, the analysis of high risk factors of developing disease is identified using mining principles. Use of data mining algorithms will result in quick prediction of disease with high accuracy. Data mining, emergency department, hospitals, machine learning, predictive models -- For More Details, Contact Us -- Arihant Techno Solutions www.arihants.com E-Mail-ID: [email protected] Mobile: +91-75984 92789
|| Part 1 || Prediction Of Hospital Readmissions using Diabetes Dataset || Quick View ||
 
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This video contains the short precap of How To Use Machine Learning To Predict Hospital Readmissions using Diabetes Dataset achieving an accuracy of 94%. I'll be describing each of these steps in the next videos. So, stay tunned.
Views: 803 Deep insight of AI
Cost-sensitive Deep Learning for Early Readmission Prediction at A Major Hospital
 
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Author: Haishuai Wang, Department of Computer Science and Engineering, Washington University in St. Louis Abstract: With increased use of electronic medical records (EMRs), data mining on medical data has great potential to improve the quality of hospital treatment and increase the survival rate of patients. Early readmission prediction enables early intervention, which is essential to preventing serious or life-threatening events, and act as a substantial contributor to reducing healthcare costs. Existing works on predicting readmission often focus on certain vital signs and diseases by extracting statistical features. They also fail to consider skewness of class labels in medical data and different costs of misclassification errors. In this paper, we recur to the merits of convolutional neural networks (CNN) to automatically learn features from time series of vital sign, and categorical feature embedding to effectively extend feature vectors with heterogeneous clinical features, such as demographics, hospitalization history, vital signs and laboratory tests. Then, both learnt features via CNN and statistical features via feature embedding are fed into a multilayer perceptron (MLP) for prediction. We use a cost-sensitive formulation to train MLP during prediction to tackle the imbalance and skewness challenge. We validate the proposed approach on two real medical datasets from Barnes-Jewish Hospital, and all data is taken from historical EMR databases and reflects the kinds of data that would realistically be available at the clinical prediction system in hospitals. We find that early prediction of readmission is possible and when compared with state-of-the-art existing methods used by hospitals, our methods perform significantly better. Based on these results, a system is being deployed in hospital settings with the proposed forecasting algorithms to support treatment. More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 45 KDD2017 video
Predicting Hospital Surgical Site Infections, Part I – Machine Learning Workflows
 
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Practical predictive analytics for medicine. This video highlights a great medical text and covers a hands-on demonstration of building predictive analytic workflows for surgical site infections. If you are interested in machine learning and want to build models that improve healthcare you should watch this video.
Views: 208 Scott Burk
|| Part 5 || Prediction Of Hospital Readmissions using Diabetes Dataset || Data Modelling 2 ||
 
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This is the final part of the tutorial "How To Use Machine Learning To Predict Hospital Readmissions". In this video, i have applied some of the Machine learning models such as Random Forest, Logistic Regression and Decision tree classifier to get good accuracy.
Views: 31 Deep insight of AI
Reducing the Costs of Hospital Readmissions with Telemedicine
 
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With more than 2,600 hospitals receiving lower reimbursements from Medicare for FY 2014-2015 for having high rates of readmissions within 30 days of discharge, hospital staffs are looking for ways to remain engaged with patients In this way, they hope to spot trouble signs early and correct problems before patients once again become ill and need to be readmitted. During this program, a panel discusses the potential for telemedicine as one useful method of staying in touch with patients. Dr. Philip Johnson is the Chair of the Emergency Department at Summit Healthcare in Show Low, Arizona. Tom Chelston is a registered nurse who is the Director of Healthcare for IVCi, an audio/visual integrator and videoconferencing company. And Neal Schoenbach is the Vice-President of Business Development for GlobalMed, which designs and manufactures healthcare delivery systems for telemedicine. Within the program is a simulated home visit by a lower-level licensee to check on the health of a recently hospitalized heart patient. But this program covers many other aspects of the readmission problem - not just the telemedicine component. The program was streamed live over the Internet on February 25, 2015.
Sandro Radovanović   Decision Support System for Hospital Readmission
 
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RapidMiner Wisdom 2015 Presentation Author: Sandro Radovanović Organization: Faculty of Organizational Sciences, University of Belgrade Presentation Title: Decision Support System for Hospital Readmission Prediction Based on Meta-Heuristic Feature Selection and Stacking
Views: 111 RapidMiner, Inc.
ProjectTemplate and R Workflow
 
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Slides and materials are available at: https://osf.io/jcg2t/ 00:00 Introductions 03:25 Signs of bad workflow 06:40 Aims of talk 07:40 Initial demo of complete project 14:30 ProjectTemplate overview 16:50 Creating Projects with Standard ProjectTemplate 18:25 config directory 19:40 lib directory 20:22 data directory 21:40 munge directory 22:14 running ProjectTemplate 23:00 Customising ProjectTemplate 28:20 Demo of customised ProjectTemplate 37:00 Conclusion 38:40 Questions A talk on ProjectTemplate and R Workflow presented at Melbourne R Users (4th July 2017).
Views: 774 Jeromy Anglim
ML #12 - Deep Dive into Heart Failure Readmissions with Joe Smith
 
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Thank you for all of the positive feedback on our real world example on length of stay (episode #6)! This week we'll feature Joe Smith, Health Catalyst Data Architect and model builder. Joe will walk us through the use case, creation, and deployment of his model designed to predict heart failure readmissions. Join Levi, Mike, and Joe for another exciting week of healthcare.ai!
Views: 8363 Healthcare AI
Big Data and Machine Learning in Healthcare: How, Why, and When
 
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Harvard Medical School Assistant Professor and CEO of Cyft, Dr. Leonard D'Avolio's keynote address to the HIMSS Big Data and Analytics Conference on October 25th, 2016 in Boston. In it, he explains why data is finally poised to transform healthcare, demystifies big data and machine learning, and lays out specific problems that these methods can address using real world examples. www.cyft.com scholar.harvard.edu/len @ldavolio
Views: 13738 Leonard D'Avolio
ML #16 - Data Science at an Academic Medical Center with Risa Myers
 
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Data science is new to healthcare providers. While many academic studies exist on how machine learning can help predict a patient's risk of infection, readmission, or extended length of stay, few hospital systems have data science groups to produce such clinical decision support for specific health system priorities and custom clinical workflows. Houston Methodist is one such hospital system that is investing in data science solutions to pressing business priorities. Risa Myers is one of the leading data scientists based at Houston Methodist Hospital, which was recently recognized as the top hospital in Texas by US News & World Report. Join us in this interview with Risa as she discusses the following: - The prioritization process of data science projects. - How to find collaborators & projects. - Data integration challenges and silos.
Views: 11278 Healthcare AI
Prediction of Hospital Readmission
 
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Prediction of Hospital Readmission --- CORNELL Data Challenge spring 2017
Views: 91 Lutz Finger
Reduce Healthcare Readmissions with Predictive Analytics and Patient Forecasts
 
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Demonstration of IBM business analytics to help a hospital or healthcare provider understand the readmission rates of their patient population. We analyze patient data about patients that onboard into a local hospital with various levels of acute or non-acute pneumonia symptoms. We improve our ability to do a readmission study. Patient data in the form of notes are used to figure out from a discharge consultation perspective whether to retain the patient, and asses their risk level for readmission.
Views: 1618 Perficient, Inc.
MicroStrategy 2 min #18 - Hospital Readmissions - Saving Millions in 10 cliks
 
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Avoiding patient readmissions is a current, important, and relevant topic for both providers and payors alike. Leveraging common and readily available data with MicroStrategy and advanced analytics, using R, we are able to materialize deep and inuitive insights that directly translates to dollars savings and better patient outcomes.
Views: 1318 HF Chadeisson
Understanding the critical role hospital data plays in lowering costs and improving patient care
 
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Learn the critical role that clinical analytics and health care data benchmarks plays in helping hospitals find ways to lower costs and improve patient care. Steve Meurer, executive principal, Data Science & Member Insights for Vizient also discusses how to use data to find areas for performance improvement and successfully reduce clinical variation. Visit www.vizientinc.com.
Views: 162 Vizient, Inc.
How to build an effective health care data analytics program in hospitals
 
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Learn how to build an effective clinical analytics program that identifies areas for performance improvement to help deliver quality patient care, lower hospital costs and inform supply chain decisions. Learn more: VizientInc.com/Our-solutions/Clinical-Solutions/Clinical-Data-Base. In this recorded webinar, our clinical, supply chain and implementation experts examine industry challenges and demonstrate how data transparency is the most powerful driver in performance improvement. Vizient speakers include: Steve Meurer, executive principal, Data Science & Member Insights; Amanda Hooper, associate vice president for Clinical/Supply Solutions; Jill Cotchen, sales executive, Clinical and Operational Solutions. This webinar recording originally took place on May 31, 2018.
Views: 983 Vizient, Inc.
Leveraging Predictive Models to Reduce Readmissions
 
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Table of Contents Introduction 1:02 Our Readmission Story 2:33 Tools & Technologies 20:10 Additional Opportunities 49:47 Q&A 54:54 Far too often analytics efforts have fallen short of making a tangible impact on outcomes because they haven’t been successfully implemented in real workflows. Predictive models remain at risk of becoming isolated in their use along the continuum of care where their integration may provide benefits larger than the sum of each silo. To combat this, UnityPoint Health (UPH) focused on integrating analytical models within the same readmission reduction strategy and coaching the care team to facilitate their adoption. Using this approach, one of UPH hospital’s risk-adjusted readmission indexes improved 40 percent over three years, surpassing internal system targets in performance and becoming the top performer in the health system. Learning Objectives: - Describe applicable predictive models useful in reducing 30-day readmissions. - Learn the elements of a successful readmissions reduction strategy in an integrated health system. - Understand common obstacles faced in the adoption of analytical tools and how to overcome them. View this webinar to gain knowledge of the analytics tools and methods UPH used, including innovative individualized risk heat-maps generated for each patient, strategies for analytics adoption, and lessons learned along the way.
Views: 112 Health Catalyst
MachineLearning to Analyze Hospital Data
 
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Healthcare cost is one of the significant problem in our country. This Presentation talks about analysis of Hospital data (COPD) using supervise and un-supervise technique of machine learning. We tried to find co-relation between various factor.
Views: 60 Prat Shah
Hospital to Home: Strategies to Prevent Readmissions to the Emergency Room Webinar
 
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BHH Oct. 13, 2015 Monthly Webinar Hospital to Home: Strategies to Prevent Readmissions to the Emergency Room. In this month's Behavioral Health Learning Collaborative monthly webinar, Hospital to Home: Strategies to Prevent Readmissions to the Emergency Room, Julie Shackley, RN, MSN, President and CEO of Androscoggin Home Care and Hospice shared highlights from their Hospital to Home pilot with CMMC (Central Maine Medical Center), including some pilot results, readmission rates and needs identified during hospital to home visits. Dr. Tom Sneed, Medical Director from Tri-County Mental Health Services also joined in to highlight how his behavioral health team is looking to partner with Androscoggin Home Health (a local Community Care Team-CCT) to support their clients needs. This information will also help Behavioral Health Home Organizations identify strategies to support their clients in appropriately use of the Emergency Room.
Views: 163 Quality Counts
Machine Learning Prediction of patient readmission rate | +91-8146105825 For query
 
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https://www.youtube.com/watch?v=r6aoF9sR52Y :: for full implementation in brief https://www.youtube.com/watch?v=Uy3fjozu5Jo :: part one of data preprocessing https://www.youtube.com/watch?v=Ok3pp8EDTzs :: part two of data preprocessing https://www.youtube.com/watch?v=UHzTNGclAu0&t=2s :: part three of data preprocessing https://www.youtube.com/watch?v=Ok3pp8EDTzs :: part three of data classification
Views: 113 Fly High with AI
Dynamic Hierarchical Classification for Patient Risk-of-Readmission
 
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Authors: Senjuti Basu Roy, Ankur Teredesai, Kiyana Zolfaghar, Rui Liu, David Hazel, Stacey Newman, Albert Marinez Abstract: Congestive Heart Failure (CHF) is a serious chronic condition often leading to 50% mortality within 5 years. Improper treatment and post-discharge care of CHF patients leads to repeat frequent hospitalizations (i.e., readmissions). Accurately predicting patient's risk-of-readmission enables care-providers to plan resources, perform factor analysis, and improve patient quality of life. In this paper, we describe a supervised learning framework, Dynamic Hierarchical Classification (DHC) for patient's risk-of-readmission prediction. Learning the hierarchy of classifiers is often the most challenging component of such classification schemes. The novelty of our approach is to algorithmically generate various layers and combine them to predict overall 30-day risk-of-readmission. While the components of DHC are generic, in this work, we focus on congestive heart failure (CHF), a pressing chronic condition. Since healthcare data is diverse and rich and each source and feature-subset provides different insights into a complex problem, our DHC based prediction approach intelligently leverages each source and feature-subset to optimize different objectives (such as, Recall or AUC) for CHF risk-of-readmission. DHC's algorithmic layering capability is trained and tested over two real world datasets and is currently integrated into the clinical decision support tools at MultiCare Health System (MHS), a major provider of healthcare services in the northwestern US. It is integrated into a QlikView App (with EMR integration planned for Q2) and currently scores patients everyday, helping to mitigate readmissions and improve quality of care, leading to healthier outcomes and cost savings. ACM DL: http://dl.acm.org/citation.cfm?id=2788585 DOI: http://dx.doi.org/10.1145/2783258.2788585
Data-Driven Decision Making in Healthcare Systems
 
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Mohsen Bayati of Stanford University explains how machine learning could and is being examined and used to determine ways to make health care more cost-effective. One example is patient readmission rates, with the most common occurrences in from past studies being from elderly and Medicare patients. A variety of reasons persist, but the surprising fact is many of these readmissions could have been avoided with a small amount of preventive care in the first place. Medication mismanagement is among the top reasons, and heart failure also is listed. A patientΓÇÖs lack of access to care outside of the hospital is also a major factor for readmissions.
Views: 982 Microsoft Research
Readmissions Insight™
 
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Watch and learn more about how Predixion Software's Readmissions Insight™ solutions helps harness the power of Big Data to tackle big issues in healthcare.
Views: 812 Predixion Software
Leveraging Electronic Health Record Data to Predict Patient Re-Admission -- Texas Health Resources
 
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Texas Health Resources and the Parkland Center for Clinical Innovation are collaborating on a research project to leverage EHR data to predict the risk of patients being readmitted following hospital discharge. Staff members at Texas Health and the Parkland center worked together to provide this video for the Robert Wood Johnson Foundation's "Care About Your Care Transition to Better Care Video Contest," documenting their collaboration in this area. Texas Health Resources 1-877-THR-WELL www.TexasHealth.org
Rethinking Readmission as a Hospital Quality Metric
 
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Rethinking Readmission as a Hospital Quality Metric: Insurance Coverage and Latent Health Status Affect Patients’ Rate of Readmission In this research, we focus on the drivers of readmission rates of CHF patients and observe that hospital readmission rates are determined not only by the quality of care, but also by non-clinical factors such as patient unobserved health status and patient insurance coverage. Our quasi-experimental results indicate that privately insured and self-pay patients experience higher likelihood of readmission after enrolling in Medicare. We also develop and estimate a hidden Markov model (HMM) to study the effect of unobserved health status, with which we reveal its significant impact on patient readmission risk. Poster Number: 7
Views: 230 INFORMS
Using Predictive Analytics to Identify At-Risk Patients, Reduce Variance, and Improve Outcomes
 
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In Wisconsin, what could be bigger than the Wisconsin Badgers or the Green Bay Packers? Aurora Health Care. Aurora Health Care is the largest employer in the state of Wisconsin with over five million patient visits, 30,000 caregivers, and numerous hospitals and clinics. So how does a company with such a massive scope achieve a fully integrated, multidisciplinary population health model with superior outcomes? Andy Roesgen visits Milwaukee to find out.
Views: 1255 amgatv
Applying big data to little patients | Srinivasan Suresh | TEDxYouth@Shadyside
 
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Dr. Srinivasan Suresh talks about predictive analytics in healthcare. He explains how technology has given doctors the ability to see trends from the past and present, so that they may prepare for the future. Dr. Srinivasan Suresh is a pediatric emergency physician and an informaticist. He currently serves as the Chief Information Officer at Children’s Hospital of Pittsburgh of UPMC. At Children’s, he oversees operations, budgeting and strategy as they relate to the information technology functions of the hospital. Dr. Suresh focuses on areas of interests in the application of business intelligence tools and advanced data analytics to improve child health, patient and provider satisfaction, and mobile health. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Views: 5921 TEDx Talks
Healthcare Analytics
 
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2013 INFORMS Annual Meeting Minneapolis Plenaries and Keynotes Presented by Dimitris Bertsimas, Operations Research Center, MIT In this talk Bertsimas presents an analytics approach to personalized diabetes management and the design of clinical trials for cancer. In the first part of the talk, he presents a system to make personalized lifestyle and health decisions for diabetes management, as well as for general health and diet management. (Joint work with Allison O' Hair.) In the second part of the talk, he proposes an analytics approach for the analysis and design of clinical trials that provides insights into what is the best currently available drug combination to treat a particular form of cancer and how to design new clinical trials that can discover improved drug combinations. The team develops semi-automated extraction techniques to build a comprehensive database of data from clinical trials. They use this database to develop statistical models from earlier trials that are capable of predicting the survival and toxicity of the combination of the drugs used, when the drugs used have been seen in earlier trials, but in different combinations. Then, using these statistical models, they develop optimization models that select novel treatment regimens that could be tested in clinical trials, based on the totality of data available on existing combinations. Ultimately, their approach offers promise for improving life expectancy and quality of life for cancer patients at low cost. (Joint work with Allison O' Hair, Stephen Relyea and John Silberholz).
Views: 5874 INFORMS
William Paiva: Transforming health care and medical education through clinical Big Data analytics
 
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Health care is undergoing significant transformation, and digital health data is at the center of this change. According to the Centers for Disease Control, nearly 80 percent of the nation’s health care institutions have converted to an electronic medical record (EMR) system from the old paper-based system. New technologies like smartphone applications are also creating new stockpiles of digital data. Genetic data is growing as well; scientists can sequence a person’s entire DNA within 24 hours and for less than $1,000. Collectively, the amount of digital health data is expected to grow from 500,000 to 25 million terabytes over the next five years. Why do we care that our health information is now in a digital format? How does it benefit all of us? People who work in health care—and every industry for that matter—are smart, well trained, and do their best to stay up-to-date with the latest research, methodologies and trends. However, it is not rational to assume individuals have the depth of knowledge or data access to deal with every situation they encounter. Furthermore, the health care field is already understaffed, and this issue will only get worse as the looming mass retirement of baby boomers from the health care workforce creates an unprecedented supply-and-demand crisis. Digitized health data has the potential to help mitigate this troubling situation. Predictive medicine uses computing power and statistical methods to analyze EMR and other health-related data to predict clinical outcomes for individual patients. Beyond health outcome forecasting, predictive medicine also can uncover surprising and often unanticipated clinical associations. Oklahoma State University’s Center for Health Systems Innovation (CHSI), through its Institute for Predictive Medicine (IPM), is a leader in the exploding field of predictive medicine thanks to the unprecedented donation by Cerner Corporation of its HIPAA-compliant clinical health database, one of the largest available in the United States. Specifically, this dataset represents clinical information from over 63 million patients and includes admission, discharge, clinical events, pharmacy, and laboratory data spanning more than 16 years. Over 20 full-time CHSI employees and nearly two dozen graduate students are working to execute the CHSI mission to transform rural and Native American health through data analytics. Further, CHSI has a number of ongoing partnerships with academia, health systems and corporations to extract value from digitized health data. One example of CHSI’s numerous predictive medicine projects is an effort to help physicians determine whether the performance of particular cardiovascular drugs varies by gender or race, or both. Conversely, this study will help indicate which drugs perform poorly or even cause complications in these populations. Other CHSI studies are designed to give physicians insight into whether patients with a particular disease are likely to develop or already have an associated disease, which will aid in co-managing these conditions and lead to better health care. Another project is designed to help hospitals use data on patient demographic characteristics, comorbidities, discharge setting, and other medical information contained in comprehensive EMR systems to determine if patients are at high risk for being readmitted for disease-associated complications. If patients are considered high risk, they can get the care and support necessary to prevent frequent cycling through the health care system. Predictive medicine can also lead to the creation and implementation of tools for managing larger patient loads, which can aid health care providers in dealing with supply-and-demand problems. For instance, CHSI has developed a clinical decision support system that can detect diabetic retinopathy with a high degree of accuracy using lab and comorbidity data available through primary care visits. This algorithm addresses the very real challenge of low patient compliance, particularly among rural and underserved populations, with annual ophthalmic eye exams, which are the gold standard for retinopathy detection and preventing vision impairment or total vision loss. CHSI is extending this work to other common diabetes-related microvascular complications with the goal of developing a comprehensive suite of tools that can help increase prevention and management of these complications among the nation’s growing diabetic population.
Views: 635 Stanford Medicine X
ML #8 - Open Healthcare Datasets
 
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Many people want healthcare data to play with, but don't know where to find it. In this chat we'll provide you the data resources you need to start doing machine learning.
Views: 15171 Healthcare AI
Real-time analytics for healthcare
 
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http://ibm.co/SQz7dA - Big data is driving massive changes in healthcare. Hear from Hear from Founder and Director of Emory Center for Clinical Care Dr. Tim Buckman and Executive VP and CFO of the largest health care delivery system in Georgia, Matt Muhart. Both deliver better safer care at lower costs with big data and analytics.
Views: 10720 IBM Analytics
Transform Healthcare with Advanced Analytics Solutions (KenSci)
 
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In this session, learn how you can transform key healthcare scenarios such as hospital length of stay prediction and patient care analytics with intelligent solutions from Microsoft and our partners. Take a closer look and test drive the KenSci healthcare solution https://aka.ms/kensci-clinicalanalytics.
Views: 532 Microsoft Cloud
Predictive Population Analytics for Hospital Revenue Growth
 
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We offer hospitals a disciplined, systematic approach to both micro and macro risk analysis. We do predictive modeling in our micro-risk analysis, where we focus on the population characteristics down to the diagnostic profiles. Our analysis takes diagnostic profiles and interpolates them with treatment cost profile data to estimate the prospective revenue streams varying individual diagnostic groups and then aggregates those overall prospective revenues for the total population. Our macro analysis takes the component annual predictive modeling results and projects them forward to forecast possible scenarios for prospective frequency, severity and total cost projections. We do repeated multiple sets of prospective analyses, to examine possible trends which may be revealed under one or another set of assumptions. In this way, the exposure and risk profiles are more thoroughly examined and available to support a better informed business model for managing these risks. The bottom line is that our team will help you increase revenue, reduce leakage, and have a better handle on your responding to changes in the healthcare landscape.
Views: 174 Lou Polur
Predictive Analytics in Healthcare
 
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Business Intelligence & Analytics solutions enable healthcare service providers to build sustainable competitive advantage with the help of insights derived from their existing operations and patient data. HIMSS describes healthcare analytics as the “systematic use of data and related clinical and business (C&B) insights developed through applied analytical disciplines such as statistical, contextual, quantitative, predictive, and cognitive spectrums to drive fact-based decision making for planning, management, measurement and learning Objectives: ► Healthcare providers are improving the clinical outcomes of patients via treatments and protocols ► Promotion of wellness and disease management The Predictors – Predictive Analytics: In order to predict the re-admission, following data fields/predictors were considered. ► Demographics – Age, Sex ► Lab data – Includes lab tests ► Vitals – Includes BP, Sugar, Weight, etc. ► Visit types – Emergency, In-patient, and Outpatient ► Diagnosis – Diseases/ailments – Heart, Pneumonia ► Previous hospital visit ► Length of stay ► The data was received as a set of .csv files which gave the complete details of Demographics, Admission, vitals, lab tests of selected sample of patients over a period of time. ► The processing of the data included the following activities: ► Removing commas, uploading .csv files to HDFS (Horton works) ► The required DDL scripts were written in Hive ► The necessary joins were written ► The result was refined datasets ► The refined datasets are passed on to Data Analysis team for analysis The best model is arrived at by testing the data under different classifiers and precision, recall and F1 score metrics calculated for each classifier. ► Gradient Boosting ► Random Forest ► Support Vector Machines ► Logistic Regression ► K-Nearest Neighbor ► Ridge _________________________________________________________________ Like the Video follow us for more: Facebook: https://www.facebook.com/altencalsoftlabs Twitter: https://twitter.com/altencalsoftlab LinkedIn: https://www.linkedin.com/company/calsoft-labs-india-p-ltd-an-alten-group-company Google+: https://plus.google.com/+Altencalsoftlabs/ _________________________________________________________________ Looking for similar IT Services? Write to us [email protected] (OR) Visit Us @ http://www.altencalsoftlabs.com/
Views: 2748 ALTEN Calsoft Labs
Improving Healthcare With Machine Learning And Advanced Analytics
 
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This session will focus on real use cases of how Advanced Analytic solutions from Microsoft are being applied today to: predict the impact of weather patterns on health service utilization in Norway (Azure ML) and improve the health of people with Chronic Disease conditions in the United States by aggregating Electronic Medical Records (EMR) data with open data sources to spot previously unrecognized factors contributing to poor health (SQL, Power Query, Power Map, Power BI). Session participants will come away with general understanding of opportunities in health and be able to cite specific examples of how Advanced Analytics is already impacting the quality and effectiveness of healthcare.
Views: 3379 Microsoft Power BI
Data Mining and Prediction Modelling in the Dairy Industry Using Time Series and Sliding Windows
 
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"WHY - As a major livestock producer, the European Union is directly affected by the global need for more sustainable food production. Climate change will undoubtedly impact on farm animal production but the health and welfare of livestock is also of increasing public concern. Due to rapid development of precision livestock farming technologies and availability of high-throughput from milk sensors, large-scale massive data has become available on research farms. The preferred matrix to measure the biomarkers is milk, as it is more accessible than blood and allows low-cost, automated repeat sampling using ‘in-line’ sampling and analytical technologies. WHAT - Certain biomarkers in milk such as N-glycan structures (BM-1), metabolites (BM-2) or mid-infra-red spectra (BM-3) can serve as biomarkers to predict production efficiency and disease. Data mining and machine learning can unlock insights around such biomarkers. As more of the aforementioned types of datasets become available over the near future, scalable data mining and prediction pipelines applied to animals science are needed. TAKEAWAYS -In this session you will learn: The methodology for ranking multiple biomarkers according to their predictive power; Data processing and statistical modelling performed using Spark v2.1.1 with scala API; Infrastructure, configuration, and implementation of the data pipeline using sliding windows with Apache Spark’s MLlib Visualization of of datasets via ElasticSearch-Kibana. Talk by Miel Hostens Session hashtag: #EUds14"
Views: 471 Databricks
What is Healthcare Informatics?
 
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Incorporating information technologies and information management, this work describes evolving areas of efficiency in the healthcare industry due to healthcare informatics enhancements. Beginning with an overview of how information management can enhance organizational efficiency the book delves into how informatics can impact productivity for healthcare providers and reduce costs. It stresses the incorporation of available information technologies along with appropriate management tactics to ensure the most effective informatics outcomes that can drive efficiencies. Areas that are addressed include project management in healthcare, knowledge management, decision support systems, business intelligence, Six Sigma, and advanced analytics such as data mining.
Views: 150805 YouTube NJIT
Care Team Coordination: How People, Process, and Technology Impact Patient Transitions
 
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With mounting regulatory requirements focused on readmission prevention and the growing complexity of care delivery, ACOs, hospitals, and community-based organizations are under pressure to effectively and efficiently manage patient transitions. In this Webinar we will explore the ways in which people, process, and technology influence patient care and how organizations can optimize these areas to enhance communication, increase operational efficiency, and improve care coordination across the continuum. This presentation is designed for CIOs, CQOs, and the clinical and technology leaders at ACOs, hospitals and health systems, and post-acute care providers who are interested in improving care coordination. Learning Objectives Attendees of this webinar will learn: 1) The role people, processes, and technology each have in improving care team coordination. 2) Ways to leverage the expanding role of care managers. 3) How to incorporate best practices and complex care management models into a care transition process. 4) How an organization reduced 30-day readmission rates by 22%, decreased LOS by 0.5 days, and increased referral of high-risk patients to post-discharge care management by 40%.
Views: 883 HIStalk Webinars
Visualizing Performance Gaps in Healthcare Data Webinar
 
45:36
Self-evaluation is not only a valuable tool for ensuring that your practice is running at peak efficiency, but it is a critical process for remaining competitive in today's changing healthcare environment. Often it is difficult for leaders and providers to determine where the focus should be placed. Many successful organizations are now visualizing gaps and lags in access, service and billing to identify processes that are hindering financial performance, slowing cash flow and, most importantly, negatively impacting the patient experience. From our webinar on August 23, 2017.
Role of Big Data, Predictive Analytics and Cognitive support in healthcare
 
38:10
Health Technology is a B2B Enterprise news portal focusing on the latest and most cutting-edge info in healthcare ICTs and medical technologies. It offers a platforms for delivering rich, relevant and up-to-date information on e- health, m- health, IT implementation in Government organisations and hospitals and providing a platform for technology solution providers, Vendors and CIO’s and IT decision makers of leading Hospital’s spanning across the entire healthcare industry.
Views: 56 Health Technology
SORMAS Project: Containing Infectious Disease Outbreaks with Big Data Analysis
 
05:15
The technology researched at Hasso Plattner Institute for lightning-fast and flexible processing and analysis of large amounts of data (In-Memory Data Management) ensures the creation of interactive, current status analysis. In this way trends are recognized early on and potential developments can be simulated. The detection of suspicious cases helps local stakeholders and specialists on various governmental levels efficiently organize the necessary countermeasures to stop the spread of disease. Weitere Informationen: http://hpi.de/plattner/projects/sormas
Can Google AI Accurately Predict Risk of Death for Hospital Patients?
 
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Google has designed an artificial intelligence that can reportedly predict whether patients will die 24 hours of being admitted to the hospital.
Views: 244 Veuer
AN ALGORITHM FOR PREDICTIVE DATA MINING APPROACH IN MEDICAL DIAGNOSIS
 
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AN ALGORITHM FOR PREDICTIVE DATA MINING APPROACH IN MEDICAL DIAGNOSIS Shakuntala Jatav1 and Vivek Sharma2 1M.Tech Scholar, Department of CSE, TIT College, Bhopal 2Professor, Department of CSE, TIT College, Bhopal ABSTRACT The Healthcare industry contains big and complex data that may be required in order to discover fascinating pattern of diseases & makes effective decisions with the help of different machine learning techniques. Advanced data mining techniques are used to discover knowledge in database and for medical research. This paper has analyzed prediction systems for Diabetes, Kidney and Liver disease using more number of input attributes. The data mining classification techniques, namely Support Vector Machine(SVM) and Random Forest (RF) are analyzed on Diabetes, Kidney and Liver disease database. The performance of these techniques is compared, based on precision, recall, accuracy, f_measure as well as time. As a result of study the proposed algorithm is designed using SVM and RF algorithm and the experimental result shows the accuracy of 99.35%, 99.37 and 99.14 on diabetes, kidney and liver disease respectively. KEYWORDS Data Mining, Clinical Decision Support System, Disease Prediction, Classification, SVM, RF. For other details Please Visit : http://aircconline.com/ijcsit/V10N1/10118ijcsit02.pdf
Find the Value of Patient Risk Acuity Analytics within Your Practice
 
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Join PDS as we discuss how to use Hierarchical Condition Coding (HCC) diagnosis scoring to analyze and improve the profitability of shared savings and value-based contracts. Commercial and Medicare contracts rely on HCC Risk Scoring to determine incentive payments and fee schedule increases which makes HCC management as essential as managing claims payment.
Predicting and Analyzing Claims Fraud Using IBM Watson Analytics
 
13:45
In this demonstration, you're going to take the role of a claims investigator, analyst or manager interested in analyzing claims history and ways to reduce claim fraud. By using IBM Watson Analytics, all those mentioned will be able to quickly and easily gain historic and predictive insights without coding, modeling and most importantly without the assistance of anyone else but themselves.
Views: 4638 Brian Snyder