Predictive Analytics In Healthcare Research Articles

Piloting a model-to-data approach to enable predictive analytics in health care through patient mortality prediction.

ObjectiveThe development of predictive models for clinical application requires the availability of electronic health record (EHR) data, which is complicated by patient privacy concerns. We showcase the “Model to Data” (MTD) approach as a new mechanism to make private clinical data available for the development of predictive models. Under this framework, we eliminate researchers’ direct interaction with patient data by delivering containerized models to the EHR data.Materials and MethodsWe operationalize the MTD framework using the Synapse collaboration platform and an on-premises secure computing environment at the University of Washington hosting EHR data. Containerized mortality prediction models developed by a model developer, were delivered to the University of Washington via Synapse, where the models were trained and evaluated. Model performance metrics were returned to the model developer.ResultsThe model developer was able to develop 3 mortality prediction models under the MTD framework using simple demographic features (area under the receiver-operating characteristic curve [AUROC], 0.693), demographics and 5 common chronic diseases (AUROC, 0.861), and the 1000 most common features from the EHR’s condition/procedure/drug domains (AUROC, 0.921).DiscussionWe demonstrate the feasibility of the MTD framework to facilitate the development of predictive models on private EHR data, enabled by common data models and containerization software. We identify challenges that both the model developer and the health system information technology group encountered and propose future efforts to improve implementation.ConclusionsThe MTD framework lowers the barrier of access to EHR data and can accelerate the development and evaluation of clinical prediction models.

Healthcare predictive analytics for disease progression: a longitudinal data fusion approach

Healthcare predictive analytics using electronic health records (EHR) offers a promising direction to address the challenging tasks of health assessment. It is highly important to precisely predict the potential disease progression based on the knowledge in the EHR data for chronic disease care. In this paper, we utilize a novel longitudinal data fusion approach to model the disease progression for chronic disease care. Different from the conventional method using only initial or static clinical data to model the disease progression for current time prediction, we design a temporal regularization term to maintain the temporal successivity of data from different time points and simultaneously analyze data from data source level and feature level based on a sparse regularization regression approach. We examine our approach through extensive experiments on the medical data provided by the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The results show that the proposed approach is more useful to simulate and predict the disease progression compared with the existing methods.

AI for Social Good: Addressing Global Challenges and Empowering Communities

Artificial intelligence (AI) has emerged as a transformative force capable of addressing pressing global challenges and driving positive change in industries. This abstract examines the role of AI in driving social welfare, highlighting its potential to address social issues and empower communities around the world.
 AI technologies including machine learning, natural language processing, and computer vision have provided innovative solutions in health, education, environmental sustainability, poverty reduction, and moreover AI-enabled diagnosis and predictive analytics in healthcare have improved patient outcomes by improving diagnosis and treatment plans. Filling in the blanks is a learning experience it is satisfied by needs.
 Furthermore, AI-driven industries help create environmental protection efforts through efficient resource management, ecosystem monitoring, and prediction of natural disasters in terms of social equity narratively, AI tools help reduce poverty, improve economic inclusion, and reduce bias in the decision-making process.
 However, the ethical implications of implementing AI require careful consideration. Issues of data privacy, algorithmic biases, and the ethical use of AI raise concerns that need to be addressed to ensure responsible and appropriate adoption of AI by government, organizations and communities intervening to establish regulatory frameworks and ethical guidelines to prioritize fairness, transparency and accountability in AI applications Business efforts are important.

Integrated Prediction System for Chronic Disease Diagnosis to Ensure Better Healthcare

Technology innovation has made life easy for human beings. Technology is being used everywhere. This also extends to the healthcare sector. The healthcare sector produces a large amount of data each minute. Because of privacy issues, much of the data generated is not used and is not publicly accessible. Healthcare data comes from diverse sources hence it will be always varied in nature. Keeping track of such data has become much easier these days. Predictive analysis in healthcare is an emerging technology that identifies the person with poor health where the risks of developing chronic conditions are more likely and provide better solutions in the field of healthcare. Statistical methods and algorithms can be used to predict the disease before the actual symptoms are revealed in humans. By using data analytics algorithms one can easily predict chronic diseases such as obesity, high/low Blood Pressure, diabetes, asthma, cardiopulmonary disorders. Because of an unhealthy diet, lack of proper exercise, stress, consumption of tobacco, alcohol, etc. chronic diseases are most common these days. If the symptoms of chronic diseases are detected in the early stages, there will be less risk of hospitalization by cost-effectively maintaining better health. Big data analysis and health care can be mixed to produce accurate results. The application of predictive analytics in healthcare is highlighted in this paper. It provides a broader analysis in the prevention of different chronic diseases by using predictive analytics. The paper also includes various issues that arise when handling health care data. For each chronic disease, diverse models, techniques, and algorithms are used for predicting and analyzing. The paper comprises a conceptual model that integrates the prediction of most common chronic diseases

A Case Study for a Big Data and Machine Learning Platform to Improve Medical Decision Support in Population Health Management

Big data and artificial intelligence are currently two of the most important and trending pieces for innovation and predictive analytics in healthcare, leading the digital healthcare transformation. Keralty organization is already working on developing an intelligent big data analytic platform based on machine learning and data integration principles. We discuss how this platform is the new pillar for the organization to improve population health management, value-based care, and new upcoming challenges in healthcare. The benefits of using this new data platform for community and population health include better healthcare outcomes, improvement of clinical operations, reducing costs of care, and generation of accurate medical information. Several machine learning algorithms implemented by the authors can use the large standardized datasets integrated into the platform to improve the effectiveness of public health interventions, improving diagnosis, and clinical decision support. The data integrated into the platform come from Electronic Health Records (EHR), Hospital Information Systems (HIS), Radiology Information Systems (RIS), and Laboratory Information Systems (LIS), as well as data generated by public health platforms, mobile data, social media, and clinical web portals. This massive volume of data is integrated using big data techniques for storage, retrieval, processing, and transformation. This paper presents the design of a digital health platform in a healthcare organization in Colombia to integrate operational, clinical, and business data repositories with advanced analytics to improve the decision-making process for population health management.

Integrated Prediction System for Chronic Disease Diagnosis to Ensure Better Healthcare

Technology innovation has made life easy for human beings. Technology is being used everywhere. This also extends to the healthcare sector. The healthcare sector produces a large amount of data each minute. Because of privacy issues, much of the data generated is not used and is not publicly accessible. Healthcare data comes from diverse sources hence it will be always varied in nature. Keeping track of such data has become much easier these days. Predictive analysis in healthcare is an emerging technology that identifies the person with poor health where the risks of developing chronic conditions are more likely and provide better solutions in the field of healthcare. Statistical methods and algorithms can be used to predict the disease before the actual symptoms are revealed in humans. By using data analytics algorithms one can easily predict chronic diseases such as obesity, high/low Blood Pressure, diabetes, asthma, cardiopulmonary disorders. Because of an unhealthy diet, lack of proper exercise, stress, consumption of tobacco, alcohol, etc. chronic diseases are most common these days. If the symptoms of chronic diseases are detected in the early stages, there will be less risk of hospitalization by cost-effectively maintaining better health. Big data analysis and health care can be mixed to produce accurate results. The application of predictive analytics in healthcare is highlighted in this paper. It provides a broader analysis in the prevention of different chronic diseases by using predictive analytics. The paper also includes various issues that arise when handling health care data. For each chronic disease, diverse models, techniques, and algorithms are used for predicting and analyzing. The paper comprises a conceptual model that integrates the prediction of the most common chronic diseases.

Acceptability of electronic healthcare predictive analytics for HIV prevention: a qualitative study with men who have sex with men in New York City.

Large data sets, also known as "big data", shared in health information exchanges (HIEs), can be used in novel ways to advance health, including among communities at risk for HIV infection. We examined values and opinions about the acceptability of using electronic healthcare predictive analytics (eHPA) to promote HIV prevention in men who have sex with men (MSM). Our aims were twofold: (I) to evaluate the perspectives of MSM with diverse race/ethnicity and age on the acceptability of predictive analytics to determine individual HIV risk and (II) to determine acceptability of having targeted prevention messaging based upon those risk estimates sent directly to the consumer.Method: Two of the authors facilitated 12 focus groups (n=57) with adult MSM without HIV, living in NYC. Groups were divided by ethnicity (Black, Latino, and White) and age (under 35 and 35 and over). Participants were recruited through HIV prevention sites, community-based organizations, social media, and Internet sites that serve these communities. Grounded theory methods were used to analyze the data with Dedoose. We identified six main themes related to acceptability: (I) reach, relevance, and potential uptake of using predictive analytics to establish HIV risk and deliver targeted prevention messaging; (II) patient-provider communication; (III) public health and individual rights; (IV) perceptions of intervention effectiveness; (V) electronic health data security; and (VI) stigma. Within each thematic domain, MSM discussed concerns, benefits, and provided recommendations for implementation. MSM in this study were supportive of the use of "big data" and technology to reach marginalized populations and improve public health, yet expressed concerns about the relevance, effectiveness, and security eHPA. Efforts to advance eHPA for HIV prevention should address these concerns, especially among the most-at-risk communities of color. Development of eHPA for HIV prevention should involve targeted messaging that addresses specific concerns regarding eHPA security, accuracy, and relevance.

Applying Analytics from the Edge to the Cloud in an Unified Scalable Healthcare Systems

Healthcare research is becoming increasingly large-scale and international. Cloud computing enables the comprehensive integration of research and clinical data, and the global sharing and collaborative processing of these data within a flexibly scalable infrastructure. Clouds offer novel research opportunities in research, as they facilitate cohort studies to be carried out at unprecedented scale, and they enable computer processing with superior pace and throughput, allowing researchers to address questions that could not be addressed by studies using limited cohorts. A well-developed example of such research is the arrhythmias Analysis of heart disease (Coronary artery disease), which involves the analysis of petabyte-scale electronic health records datasets from research centers in different locations or countries and different jurisdictions. Predictive analytics in healthcare plays a ma or role in finding the presence of disease and its severity using observations and symptoms associated with patients Health records. Clustering is a process of discovering relationships among data in datasets. It is also used for predicting relationships in the results discovered. Machine Learning is widely used in various applications such as business organizations, e-commerce, and health care industry, scientific and engineering for predicting and discovering relationships among data. In the health care industry, the predictive analytics in Machine Learning is mainly used for Disease Prediction. The ob ective of work is to predict the heart disease with reduced number of features from Electro Cardio Images. Early detection of disease and by mapping the drug side effects with patient histories is the needed approach. The real time applications of knowledge acquisition of health care data research require unified deep learning healthcare systems. This paper presents deep learning service share model architecture for the generalizations of knowledge processing which is available in form of cloud and by using various parameters which enhances the assistive intelligence.

Application of Data Analytics Principles in Healthcare

Information technology has transformed the healthcare field worldwide. In many areas of the healthcare industry, implementations of data analytics tools are commonly used recently. Applying data analytics principles in medical sciences appropriately transforms the mere storage of medical records in to discovery of drugs. Data science and analytics are essential tools because they can help make better decisions when it comes to spending and reducing inefficiencies in healthcare. The proposed model of healthcare data analytics provides a framework to accelerate the adoption and implementation of predictive analytics in healthcare. Healthcare data analytics can be applied to prove formulated hypotheses, test those using standard analytics models and predict patient health conditions. It can be used to classify patients at risk of developing diseases such as diabetes, asthma, and other life-long illnesses. In spite of the challenges faced while applying data science predictive analytics in the healthcare environment, there is an enormous opportunity for its usage in providing quality healthcare for patients

The importance of ensuring artificial intelligence and machine learning can be understood at the human level

Abstract With rapid developments in big data technology and the prevalence of large-scale datasets from diverse sources, the healthcare predictive analytics (HPA) field is witnessing a dramatic surge in interest. In healthcare, it is not only important to provide accurate predictions, but also critical to provide reliable explanations to the underlying black-box models making the predictions. Such explanations can play a crucial role in not only supporting clinical decision-making but also facilitating user engagement and patient safety. If users and decision makers do not have faith in the HPA model, it is highly likely that they will reject its use. Furthermore, it is extremely risky to blindly accept and apply the results derived from black-box models, which might lead to undesirable consequences or life-threatening outcomes in domains with high stakes such as healthcare. As machine learning and artificial intelligence systems are becoming more capable and ubiquitous, explainable artificial intelligence and machine learning interpretability are garnering significant attention among practitioners and researchers. The introduction of policies such as the General Data Protection Regulation (GDPR), has amplified the need for ensuring human interpretability of prediction models. In this talk I will discuss methods and applications for developing local as well as global explanations from machine learning and the value they can provide for healthcare prediction.

Cloud Enabled Predictive Big data Analytics Framework for Healthcare

Big data is one of the recently emerged domain in today's digital age where everything is being digitized. Very large size of data is produced from various organizations all through the globe .This very large size of data is termed as big data..Conventional databases are not able to handle the challenges with enormous data. Detailed examination of large amounts of data requires a lot of efforts at various levels with the aim of finding knowledge , hidden patterns for decision making. Big data analytics plays an essential character in order to attain predictive analytics in healthcare. Cloud systems can be employed for the storage of big data so that users can be able to access it from anywhere . The objective of this paper is to review the concept of big data , healthcare in the context of big data and cloud computing. It also proposes architecture framework model for predictive big data analytics in healthcare area. It also presents technology for big data analytics. This paper also addresses the use of big data analytics along with specifying healthcare applications.

Predictive analytics in health care: how can we know it works?

There is increasing awareness that the methodology and findings of research should be transparent. This includes studies using artificial intelligence to develop predictive algorithms that make individualized diagnostic or prognostic risk predictions. We argue that it is paramount to make the algorithm behind any prediction publicly available. This allows independent external validation, assessment of performance heterogeneity across settings and over time, and algorithm refinement or updating. Online calculators and apps may aid uptake if accompanied with sufficient information. For algorithms based on “black box” machine learning methods, software for algorithm implementation is a must. Hiding algorithms for commercial exploitation is unethical, because there is no possibility to assess whether algorithms work as advertised or to monitor when and how algorithms are updated. Journals and funders should demand maximal transparency for publications on predictive algorithms, and clinical guidelines should only recommend publicly available algorithms.

Transformation of the Doctor-Patient Relationship: Big Data, Accountable Care, and Predictive Health Analytics.

The medical profession is steeped in traditions that guide its practice. These traditions were developed to preserve the well-being of patients. Transformations in science, technology, and society, while maintaining a self-governance structure that drives the goal of care provision, have remained hallmarks of the profession. The purpose of this paper is to examine ethical challenges in health care as it relates to Big Data, Accountable Care Organizations, and Health Care Predictive Analytics using the principles of biomedical ethics laid out by Beauchamp and Childress (autonomy, beneficence, non-maleficence, and justice). Among these are the use of Electronic Health Records within stipulations of the Health Insurance Portability and Accountability Act. Clinicians are well-positioned to impact health policy development to address ethical issues associated with the use of Big Data, Accountable Care, and Health Care Predictive Analytics as we work to transform the doctor-patient relationship towards improving population health outcomes and creating a healthier society.

The number needed to benefit: estimating the value of predictive analytics in healthcare.

Predictive analytics in health care has generated increasing enthusiasm recently, as reflected in a rapidly growing body of predictive models reported in literature and in real-time embedded models using electronic health record data. However, estimating the benefit of applying any single model to a specific clinical problem remains challenging today. Developing a shared framework for estimating model value is therefore critical to facilitate the effective, safe, and sustainable use of predictive tools into the future. We highlight key concepts within the prediction-action dyad that together are expected to impact model benefit. These include factors relevant to model prediction (including the number needed to screen) as well as those relevant to the subsequent action (number needed to treat). In the simplest terms, a number needed to benefit contextualizes the numbers needed to screen and treat, offering an opportunity to estimate the value of a clinical predictive model in action.

Future Trends of the Healthcare Data Predictive Analytics using Soft Computing Techniques in Data Science

Predictive Analytics, Soft Computing (SC) and Optimization, Data Mining and Data Science are rapidly becoming some of the most-discussed, perhaps utmost glorified topics in healthcare business. Artificial Intelligence, Machine Learning, Artificial Neural Networks, Fuzzy Logic, Expert Systems, etc., is well-studied disciplines with a long history of success in many industries. Healthcare can acquire treasured sessions from this prior achievement to startup the efficacy of predictive analytics for refining patient care, chronic disease management, hospital administration and supply chain efficiencies. The prospects that presently occurs for healthcare systems is to state what “predictive analytics” stands for to them and how can it be cast off furthermost excellently to cause further enhancements. In all industries including healthcare, prediction plays a best worthwhile role when that data is passed on as accomplishments. The inclinations to mediate the vital data is in harnessing the power of historical and real-time data with visions from forecasting those data based on the times ahead. Importantly, to best gauge efficacy and value, both the predictor and the intervention must be integrated within the same system and workflow where the trend occurs. A valuable report of the organized publicity and expectation of predictive analytics in healthcare through a blend of psychology, digital technology, and entrepreneurship is available for real-time implementation for the good of the public. Review and evaluation on these disciplines pave ways to open up new arenas envisaging the future trends of Predictive analytics, Data Mining and Science and Soft Computing (SC) in healthcare, stepping strongly into pervasive computing, ambient intelligence, ubiquitous computing and many more automated technical concepts and computing’s ahead.

Genetic Algorithm based Optimized Image Registration using Fast Walsh Hadamard Transform

Numerous medical means are mandatory with accuracy and totality of data about a patient for a doctor for healthcare predictive analytics and so as to treat and take care of their patients’ remedy. Medical image registration practices bestow more affluent diagnosis and medication information to the doctors in stipulating an all-embracing allusion resource for the beneficiaries entangled in image registration as an optimization crisis. In this research work, optimization of theGenetic Algorithm based image registration technique using Fast Walsh Hadamard Transform was implemented and their evaluation of results proved to be good.

Life Model: A novel representation of life-long temporal sequences in health predictive analytics

Predictive analytics in healthcare can prevent patients’ emergency health conditions, and reduce costs in the long term. Moreover, accurate and timely anomaly predictions by focusing on recent events can save lives. In real-time IoT predictive analytics, modeling historical temporal health records with missing values in diagnosis prediction is a major challenge. Recent studies have started using deep learning and data abstraction techniques to model health data. However, it is difficult to train a model to predict anomalies based on temporal sparse data, especially to classify all disease diagnosis classes. Modeling a lifetime of an individual’s medical history in a short, concise sequence is a challenge. Moreover, the model should be robust and preserve the concept of time for variety of examples despite the missing values; especially in an IoT system, in which real-time prediction depends on both recent data and historical records.The proposed solution in this research for modelingtemporal pattern sequences is called as Life Model (LM). LM provides a concise sequence to represent the history or future, using the novel intensity temporal sequence (ITS) tensors. LM algorithms and properties enable ITS tensors to train long short-term memory (LSTM) recurrent neural networks (RNN) efficiently in order to predict anomalies and diagnosis in real-time, even in the absence of some values.LM is used to predict mortality of 10,000 patients from MIMIC III dataset based on their diagnosis and procedures codes. The results show improvement in the model trained by LM-mapped data compared to fixed-sized intervals which achieved an accuracy of 99.6% with AUROC and brier score of 99.5% and of 0.00 respectively. In addition, the LM model can predict the approximate time of activities, with different granularity of seconds and up to years; tested on an activity dataset.Furthermore, a new LM-powered predictive health analytics and real-time monitoring schema (PHARMS) is proposed to enable design and implementation of predictive health analytic systems. PHARMS uses deep learning for real-time minimally-invasive intelligent activity monitoring and predictive analysis in a medical IoT scheme.

Healthcare predictive analytics: An overview with a focus on Saudi Arabia.

Despite a newfound wealth of data and information, the healthcare sector is lacking in actionable knowledge. This is largely because healthcare data, though plentiful, tends to be inherently complex and fragmented. Health data analytics, with an emphasis on predictive analytics, is emerging as a transformative tool that can enable more proactive and preventative treatment options. This review considers the ways in which predictive analytics has been applied in the for-profit business sector to generate well-timed and accurate predictions of key outcomes, with a focus on key features that may be applicable to healthcare-specific applications. Published medical research presenting assessments of predictive analytics technology in medical applications are reviewed, with particular emphasis on how hospitals have integrated predictive analytics into their day-to-day healthcare services to improve quality of care. This review also highlights the numerous challenges of implementing predictive analytics in healthcare settings and concludes with a discussion of current efforts to implement healthcare data analytics in the developing country, Saudi Arabia.

Using predictive analytics and big data to optimize pharmaceutical outcomes.

The steps involved, the resources needed, and the challenges associated with applying predictive analytics in healthcare are described, with a review of successful applications of predictive analytics in implementing population health management interventions that target medication-related patient outcomes. In healthcare, the term big data typically refers to large quantities of electronic health record, administrative claims, and clinical trial data as well as data collected from smartphone applications, wearable devices, social media, and personal genomics services; predictive analytics refers to innovative methods of analysis developed to overcome challenges associated with big data, including a variety of statistical techniques ranging from predictive modeling to machine learning to data mining. Predictive analytics using big data have been applied successfully in several areas of medication management, such as in the identification of complex patients or those at highest risk for medication noncompliance or adverse effects. Because predictive analytics can be used in predicting different outcomes, they can provide pharmacists with a better understanding of the risks for specific medication-related problems that each patient faces. This information will enable pharmacists to deliver interventions tailored to patients' needs. In order to take full advantage of these benefits, however, clinicians will have to understand the basics of big data and predictive analytics. Predictive analytics that leverage big data will become an indispensable tool for clinicians in mapping interventions and improving patient outcomes.

Healthcare Predictive Analytics for Risk Profiling in Chronic Care: A Bayesian Multitask Learning Approach

Clinical intelligence about a patient's risk of future adverse health events can support clinical decision making in personalized and preventive care. Healthcare predictive analytics using electronic health records offers a promising direction to address the challenging tasks of risk profiling. Patients with chronic diseases often face risks of not just one, but an array of adverse health events. However, existing risk models typically focus on one specific event and do not predict multiple outcomes. To attain enhanced risk profiling, we adopt the design science paradigm and propose a principled approach called Bayesian multitask learning (BMTL). Considering the model development for an event as a single task, our BMTL approach is to coordinate a set of baseline models—one for each event—and communicate training information across the models. The BMTL approach allows healthcare providers to achieve multifaceted risk profiling and model an arbitrary number of events simultaneously. Our experimental evaluations demonstrate that the BMTL approach attains an improved predictive performance when compared with the alternatives that model multiple events separately. We also find that, in most cases, the BMTL approach significantly outperforms existing multitask learning techniques. More importantly, our analysis shows that the BMTL approach can create significant potential impacts on clinical practice in reducing the failures and delays in preventive interventions. We discuss several implications of this study for health IT, big data and predictive analytics, and design science research.