Computer-aided Big Healthcare Data (BHD) Analytics
30 Sep 2020-
TL;DR: This work plans to exhibit a survey of the writing of big data arrangements in the medicinal services part, the potential changes, challenges, and accessible stages and philosophies to execute enormous information investigation in the healthcare sector.
Abstract: Tremendous measure of data lakes with the exponential mounting rate is produced by the present healthcare sector. The information from differing sources like electronic wellbeing record, clinical information, streaming information from sensors, biomedical image data, biomedical signal information, lab data, and so on brand it substantial as well as mind-boggling as far as changing information positions, which have stressed the abilities of prevailing regular database frameworks in terms of scalability, storage of unstructured data, concurrency, and cost. Big data solutions step in the picture by harnessing these colossal, assorted, and multipart data indexes to accomplish progressively important and learned patterns. The reconciliation of multimodal information seeking after removing the relationship among the unstructured information types is a hotly debated issue these days. Big data energizes in triumphing the bits of knowledge from these immense expanses of information. Big data is a term which is required to take care of the issues of volume, velocity, and variety generally seated in the medicinal services data. This work plans to exhibit a survey of the writing of big data arrangements in the medicinal services part, the potential changes, challenges, and accessible stages and philosophies to execute enormous information investigation in the healthcare sector. The work categories the big healthcare data (BHD) applications in five broad categories, followed by a prolific review of each sphere, and also offers some practical available real-life applications of BHD solutions.
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01 Jan 2013
TL;DR: How Big Data is becoming a growing force in the changing healthcare landscape is explored and how to generate, capture, analyze and make use of the streams of new kinds of data that are about to flood healthcare is explored.
Abstract: The potential of Big Data allows us to hope to slow the ever-increasing costs of care, help providers practice more effective medicine, empower patients and caregivers, support fitness and preventive self-care, and to dream about more personalized medicine. Yet, as with the Internet, social media, and cloud computing, early enthusiasts are creating hyperbolic expectations about how and how quickly Big Data will transform healthcare. A number of issues challenge the adoption and success of healthcare Big Data, including privacy and security, who owns the data, and the regulatory labyrinth. Furthermore, real advances depend on better ways to exploit the disconnected puddles and lakes of existing data as well as better ways to generate, capture, analyze and make use of the streams of new kinds of data that are about to flood healthcare. This paper will introduce to Big Data and explore how it is becoming a growing force in the changing healthcare landscape.
95 citations
01 Apr 2010
TL;DR: In this paper, the authors evaluated the diagnostic utility of three different diagnostic and therapeutic approaches for hyponatremia: inexperienced doctor, intensive care senior physicians acting as Senior Physician, and senior endocrinologist serving as Reference Standard.
Abstract: BACKGROUND
The differential diagnosis of hyponatremia is often challenging because of its association with multiple underlying pathophysiological mechanisms, diseases, and treatment options. Several algorithms are available to guide the diagnostic approach to hyponatremia, but their diagnostic and clinical utility has never been evaluated. We aimed to assess in detail the diagnostic utility as well as the limitations of the existing approaches to hyponatremia.
METHODS
Each of the 121 consecutive subjects presenting with hyponatremia (serum sodium <130 mmoL/L) underwent 3 different and independent diagnostic and therapeutic approaches: inexperienced doctor applying an established Algorithm, intensive care senior physicians acting as Senior Physician, and senior endocrinologist serving as Reference Standard.
RESULTS
The overall diagnostic agreement between Algorithm and Reference Standard was 71% (respective Cohen's kappa and delta values were 0.64 and 0.70), the overall diagnostic agreement between Senior Physician and Reference Standard was 32% (0.20 and 0.19, respectively). Regarding the therapeutic consequences, the diagnostic accuracy of the Algorithm was 86% (0.70 and 0.72, respectively) and of the Senior Physician was 48% (0.01 and 0.04, respectively). In retrospect, by disregarding the patient's extracellular fluid volume and assessing the effective arterial blood volume by determination of the fractional urate excretion, the Algorithm improved its diagnostic accuracy to 95%.
CONCLUSION
Although the Algorithm performed reasonably well, several shortcomings became apparent, rendering it difficult to apply the Algorithm without reservation. Whether some modifications may enhance its diagnostic accuracy and simplify the management of hyponatremia needs to be determined.
92 citations
TL;DR: In this paper , the authors use AI explainability as a way to help build trustworthiness in the medical domain and take a look at the recent developments in the area of explainable AI which encourages creativity, and at times are necessities in practice to raise awareness.
Abstract: Artificial Intelligence (AI) is creating a revolution in the healthcare industry with its recent developments in organized and amorphous data and quick progress in analytic techniques. The usefulness of AI in healthcare is being recognised at the same time as people begin to be concerned with the possible lack of explainability and bias in the models created. This explains the concept of explainable artificial intelligence (XAI), which increases the faith held in a system, thus leading to more widespread use of AI in healthcare. In this chapter, we offer diverse ways of viewing the XAI concepts, understandability and interpretability of explainable AI systems, mainly focussing on the healthcare domain. The intention is to educate healthcare providers on the understandability and interpretability of explainable AI systems. The medical model is the root cause of life, and we should be assured adequate to treat the patient according to its rules. This chapter uses AI explainability as a way to help build trustworthiness in the medical domain and takes a look at the recent developments in the area of explainable AI which encourages creativity, and at times are necessities in practice to raise awareness.
References
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13 May 2011
TL;DR: The amount of data in the authors' world has been exploding, and analyzing large data sets will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, according to research by MGI and McKinsey.
Abstract: The amount of data in our world has been exploding, and analyzing large data sets—so-called big data— will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, according to research by MGI and McKinsey's Business Technology Office. Leaders in every sector will have to grapple with the implications of big data, not just a few data-oriented managers. The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data for the foreseeable future.
4,700 citations
TL;DR: The application of big data to health care is discussed, using an economic framework to highlight the opportunities it will offer and the roadblocks to implementation, and suggests that leveraging the collection of patient and practitioner data could be an important way to improve quality and efficiency of health care delivery.
Abstract: THE AMOUNT OF DATA BEING DIGITALLY COLLECTED AND stored is vast and expanding rapidly. As a result, the science of data management and analysis is also advancing to enable organizations to convert this vast resource into information and knowledge that helps them achieve their objectives. Computer scientists have invented the term big data to describe this evolving technology. Big data has been successfully used in astronomy (eg, the Sloan Digital Sky Survey of telescopic information), retail sales (eg, Walmart’s expansive number of transactions), search engines (eg, Google’s customization of individual searches based on previous web data), and politics (eg, a campaign’s focus of political advertisements on people most likely to support their candidate based on web searches). In this Viewpoint, we discuss the application of big data to health care, using an economic framework to highlight the opportunities it will offer and the roadblocks to implementation. We suggest that leveraging the collection of patient and practitioner data could be an important way to improve quality and efficiency of health care delivery. Widespread uptake of electronic health records (EHRs) has generated massive data sets. A survey by the American Hospital Association showed that adoption of EHRs has doubled from 2009 to 2011, partly a result of funding provided by the Health Information Technology for Economic and Clinical Health Act of 2009. Most EHRs now contain quantitative data (eg, laboratory values), qualitative data (eg, text-based documents and demographics), and transactional data (eg, a record of medication delivery). However, much of this rich data set is currently perceived as a byproduct of health care delivery, rather than a central asset to improve its efficiency. The transition of data from refuse to riches has been key in the big data revolution of other industries. Advances in analytic techniques in the computer sciences, especially in machine learning, have been a major catalyst for dealing with these large information sets. These analytic techniques are in contrast to traditional statistical methods (derived from the social and physical sciences), which are largely not useful for analysis of unstructured data such as text-based documents that do not fit into relational tables. One estimate suggests that 80% of business-related data exist in an unstructured format. The same could probably be said for health care data, a large proportion of which is text-based. In contrast to most consumer service industries, medicine adopted a practice of generating evidence from experimental (randomized trials) and quasi-experimental studies to inform patients and clinicians. The evidence-based movement is founded on the belief that scientific inquiry is superior to expert opinion and testimonials. In this way, medicine was ahead of many other industries in terms of recognizing the value of data and information guiding rational decision making. However, health care has lagged in uptake of newer techniques to leverage the rich information contained in EHRs. There are 4 ways big data may advance the economic mission of health care delivery by improving quality and efficiency. First, big data may greatly expand the capacity to generate new knowledge. The cost of answering many clinical questions prospectively, and even retrospectively, by collecting structured data is prohibitive. Analyzing the unstructured data contained within EHRs using computational techniques (eg, natural language processing to extract medical concepts from free-text documents) permits finer data acquisition in an automated fashion. For instance, automated identification within EHRs using natural language processing was superior in detecting postoperative complications compared with patient safety indicators based on discharge coding. Big data offers the potential to create an observational evidence base for clinical questions that would otherwise not be possible and may be especially helpful with issues of generalizability. The latter issue limits the application of conclusions derived from randomized trials performed on a narrow spectrum of participants to patients who exhibit very different characteristics. Second, big data may help with knowledge dissemination. Most physicians struggle to stay current with the latest evidence guiding clinical practice. The digitization of medical literature has greatly improved access; however, the sheer
1,396 citations
TL;DR: An overview of machine learning applications for the analysis of genome sequencing data sets, including the annotation of sequence elements and epigenetic, proteomic or metabolomic data is provided.
Abstract: The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets. Here, we provide an overview of machine learning applications for the analysis of genome sequencing data sets, including the annotation of sequence elements and epigenetic, proteomic or metabolomic data. We present considerations and recurrent challenges in the application of supervised, semi-supervised and unsupervised machine learning methods, as well as of generative and discriminative modelling approaches. We provide general guidelines to assist in the selection of these machine learning methods and their practical application for the analysis of genetic and genomic data sets.
1,317 citations
TL;DR: The use of information embedded in the Twitter stream is examined to (1) track rapidly-evolving public sentiment with respect to H1N1 or swine flu, and (2) track and measure actual disease activity.
Abstract: Twitter is a free social networking and micro-blogging service that enables its millions of users to send and read each other's “tweets,” or short, 140-character messages. The service has more than 190 million registered users and processes about 55 million tweets per day. Useful information about news and geopolitical events lies embedded in the Twitter stream, which embodies, in the aggregate, Twitter users' perspectives and reactions to current events. By virtue of sheer volume, content embedded in the Twitter stream may be useful for tracking or even forecasting behavior if it can be extracted in an efficient manner. In this study, we examine the use of information embedded in the Twitter stream to (1) track rapidly-evolving public sentiment with respect to H1N1 or swine flu, and (2) track and measure actual disease activity. We also show that Twitter can be used as a measure of public interest or concern about health-related events. Our results show that estimates of influenza-like illness derived from Twitter chatter accurately track reported disease levels.
1,195 citations
Columbia University Medical Center1, Regenstrief Institute2, Stanford University3, AstraZeneca4, University of Hong Kong5, University of California, Los Angeles6, Ajou University7, Erasmus University Medical Center8, University of South Australia9, Uppsala Monitoring Centre10, Taipei Medical University11, Janssen Pharmaceutica12, Columbia University13
TL;DR: Observational Health Data Sciences and Informatics has built on learnings from the Observational Medical Outcomes Partnership to turn methods research and insights into a suite of applications and exploration tools that move the field closer to the ultimate goal of generating evidence about all aspects of healthcare.
Abstract: The vision of creating accessible, reliable clinical evidence by accessing the clincial experience of hundreds of millions of patients across the globe is a reality. Observational Health Data Sciences and Informatics (OHDSI) has built on learnings from the Observational Medical Outcomes Partnership to turn methods research and insights into a suite of applications and exploration tools that move the field closer to the ultimate goal of generating evidence about all aspects of healthcare to serve the needs of patients, clinicians and all other decision-makers around the world.
716 citations