Data mining algorithms are originally designed by assuming the data is available at one centralized site. These algorithms also assume that the whole data is fit into main memory while running the algorithm. But in today's scenario the data has to be handled is distributed even geographically. Bringing the data into a centralized site is a bottleneck in terms of the bandwidth when compared with the size of the data. In this paper for multiclass SVM we propose an algorithm which builds a global SVM model by merging the local SVMs using a distributed approach(DSVM). And the global SVM will be communicated to each site and made it available for further classification. The experimental analysis has shown promising results with better accuracy when compared with both the centralized and ensemble method. The time complexity is also reduced drastically because of the parallel construction of local SVMs. The experiments are conducted by considering the data sets of size 100s to hundred of 100s which also addresses the issue of scalability.
In 2005 discussed the association between the symptoms and the treatment of a patient @cite_12 . their discussion also includes the significance of handling the huge amount with the help of data mining.
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Data mining has been used intensively and extensively by many organizations. In healthcare, data mining is becoming increasingly popular, if not increasingly essential. Data mining applications can greatly benefit all parties involved in the healthcare industry. For example, data mining can help healthcare insurers detect fraud and abuse, healthcare organizations make customer relationship management decisions, physicians identify effective treatments and best practices, and patients receive better and more affordable healthcare services. The huge amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform these mounds of data into useful information for decision making. This article explores data mining applications in healthcare. In particular, it discusses data mining and its applications within healthcare in major areas such as the evaluation of treatment effectiveness, management of healthcare, customer relationship management, and the detection of fraud and abuse. It also gives an illustrative example of a healthcare data mining application involving the identification of risk factors associated with the onset of diabetes. Finally, the article highlights the limitations of data mining and discusses some future directions. As a new concept that emerged in the middle of 1990's, data mining can help researchers gain both novel and deep insights and can facilitate unprecedented understanding of large biomedical datasets. Data mining can uncover new biomedical and healthcare knowledge for clinical and administrative decision making as well as generate scientific hypotheses from large experimental data, clinical databases, and or biomedical literature. This review first introduces data mining in general (e.g., the background, definition, and process of data mining), discusses the major differences between statistics and data mining and then speaks to the uniqueness of data mining in the biomedical and healthcare fields. A brief summarization of various data mining algorithms used for classification, clustering, and association as well as their respective advantages and drawbacks is also presented. Suggested guidelines on how to use data mining algorithms in each area of classification, clustering, and association are offered along with three examples of how data mining has been used in the healthcare industry. Given the successful application of data mining by health related organizations that has helped to predict health insurance fraud and under-diagnosed patients, and identify and classify at-risk people in terms of health with the goal of reducing healthcare cost, we introduce how data mining technologies (in each area of classification, clustering, and association) have been used for a multitude of purposes, including research in the biomedical and healthcare fields. A discussion of the technologies available to enable the prediction of healthcare costs (including length of hospital stay), disease diagnosis and prognosis, and the discovery of hidden biomedical and healthcare patterns from related databases is offered along with a discussion of the use of data mining to discover such relationships as those between health conditions and a disease, relationships among diseases, and relationships among drugs. The article concludes with a discussion of the problems that hamper the clinical use of data mining by health professionals. Modern, densely instrumented, smart buildings generate large amounts of raw data. This poses significant challenges from both the data management perspective as well as leveraging the associated information for enabling advanced energy management, fault detection and control strategies. Networks of intelligent sensors, controllers and actuators currently allow fine grained monitoring of the building state but shift the challenge to exploiting these large quantities of data in an efficient manner. We discuss methods for black-box modelling of input-output data stemming from buildings. Using exploratory analysis it is argued that data mining inspired approaches allow for fast and effective assessment of building state and associated predictions. These are illustrated using a case study on real data collected from commercial-grade air handling units of a research building. Conclusions point out to the feasibility of this approach as well as potential for data mining techniques in smart building control applications. This paper analyzes the results of a critical peak pricing (CPP) experiment involving 123 residential customers of the City of Anaheim Public Utilities (APU) over the period June 1, 2005 to October 14, 2005. Using a nonparametric condition mean estimation framework that allows for customer-specific fixed effects and day-of-sample fixed effects, I find that customers in the treatment group consumed an average of 12 percent less electricity during the peak hours of the day on CPP days than customers in the control group. There is also evidence that this reduction in consumption for customers in the treatment group relative to customers in the control group is larger on higher temperature CPP days. The impact of CPP events is confined to the peak periods of CPP days. Mean electricity consumption by customers in the treatment group is not significantly different from that of customers in the control group during the peak or off-peak periods of the day before or day after a CPP event. Much of the estimated consumption reduction of treatment consumers relative to control group consumers during peak periods of CPP days is due to reductions from a higher level of consumption by treatment group customers in non-CPP days. The consumption reductions paid rebates during CPP days are almost 7 times the reduction in consumption due to CPP events predicted by the treatment effects estimate, which provides strong evidence of an overly generous method for setting the reference level for peak period consumption relative to which customers are issued refunds during CPP days. The paper closes with a discussion of the challenges associated with implementing a CPP rate with a rebate mechanism as the default rate for residential customers. Years of heavy regulation and bureaucratic inefficiency have slowed innovation for electronic medical records (EMRs). We now face a critical need for such innovation, as personalization and data science prompt patients to engage in the details of their healthcare and restore agency over their medical data. In this paper, we propose MedRec: a novel, decentralized record management system to handle EMRs, using blockchain technology. Our system gives patients a comprehensive, immutable log and easy access to their medical information across providers and treatment sites. Leveraging unique blockchain properties, MedRec manages authentication, confidentiality, accountability and data sharing -- crucial considerations when handling sensitive information. A modular design integrates with providers' existing, local data storage solutions, facilitating interoperability and making our system convenient and adaptable. We incentivize medical stakeholders (researchers, public health authorities, etc.) to participate in the network as blockchain &#x0022;miners&#x0022;. This provides them with access to aggregate, anonymized data as mining rewards, in return for sustaining and securing the network via Proof of Work. MedRec thus enables the emergence of data economics, supplying big data to empower researchers while engaging patients and providers in the choice to release metadata. The purpose of this short paper is to expose, prior to field tests, a working prototype through which we analyze and discuss our approach. Background— Renal insufficiency after coronary artery bypass graft (CABG) surgery is associated with increased short-term and long-term mortality. We hypothesized that preoperative patient characteristics could be used to predict the patient-specific risk of developing postoperative renal insufficiency. @PARASPLIT Methods and Results— Data were prospectively collected on 11 301 patients in northern New England who underwent isolated CABG surgery between 2001 and 2005. Based on National Kidney Foundation definitions, moderate renal insufficiency was defined as a GFR 12 000, prior CABG, congestive heart failure, peripheral vascular disease, diabetes, hypertension, and preoperative intraaortic balloon pump. The predictive model was significant with χ2 150.8, probability value <0.0001. The model discriminated well, ROC 0.72 (95 CI: 0.68 to 0.75). The model was well calibrated according to the Hosmer-Lemeshow test. @PARASPLIT Conclusions— We developed a robust prediction rule to assist clinicians in identifying patients with normal, or near normal, preoperative renal function who are at high risk of developing severe renal insufficiency. Physicians may be able to take steps to limit this adverse outcome and its associated increase in morbidity and mortality. An extensive amount of information is currently available to clinical specialists, ranging from details of clinical symptoms to various types of biochemical data and outputs of imaging devices. Each type of data provides information that must be evaluated and assigned to a particular pathology during the diagnostic process. To streamline the diagnostic process in daily routine and avoid misdiagnosis, artificial intelligence methods (especially computer aided diagnosis and artificial neural networks) can be employed. These adaptive learning algorithms can handle diverse types of medical data and integrate them into categorized outputs. In this paper, we briefly review and discuss the philosophy, capabilities, and limitations of artificial neural networks in medical diagnosis through selected examples. We review the history and current trends in the field of Educational Data Mining (EDM). We consider the methodological profile of research in the early years of EDM, compared to in 2008 and 2009, and discuss trends and shifts in the research conducted by this community. In particular, we discuss the increased emphasis on prediction, the emergence of work using existing models to make scientific discoveries (“discovery with models”), and the reduction in the frequency of relationship mining within the EDM community. We discuss two ways that researchers have attempted to categorize the diversity of research in educational data mining research, and review the types of research problems that these methods have been used to address. The mostcited papers in EDM between 1995 and 2005 are listed, and their influence on the EDM community (and beyond the EDM community) is discussed. Self-disclosure of stigmatized conditions is known to yield therapeutic benefits. Social media sites are emerging as promising platforms enabling disclosure around a variety of stigmatized concerns, including mental illness. What kind of behavioral changes precede and follow such disclosures? Do the therapeutic benefits of "opening up" manifest in these changes? In this paper, we address these questions by focusing on disclosures of schizophrenia diagnoses made on Twitter. We adopt a clinically grounded quantitative approach to first identify temporal phases around disclosure during which symptoms of schizophrenia are likely to be significant. Then, to quantify behaviors before and after disclosures, we define linguistic measures drawing from literature on psycholinguistics and the socio-cognitive model of schizophrenia. Along with significant linguistic differences before and after disclosures, we find indications of therapeutic outcomes following disclosures, including improved readability and coherence in language, future orientation, lower self preoccupation, and reduced discussion of symptoms and stigma perceptions. We discuss the implications of social media as a new therapeutic tool in supporting disclosures of stigmatized conditions. The enormous amount of information stored in unstructured texts cannot simply be used for further processing by computers, which typically handle text as simple sequences of character strings. Therefore, specific (pre-)processing methods and algorithms are required in order to extract useful patterns. Text mining refers generally to the process of extracting interesting information and knowledge from unstructured text. In this article, we discuss text mining as a young and interdisciplinary field in the intersection of the related areas information retrieval, machine learning, statistics, computational linguistics and especially data mining. We describe the main analysis tasks preprocessing, classification, clustering, information extraction and visualization. In addition, we briefly discuss a number of successful applications of text mining. Background: Despite the availability of psychosocial evidence-based practices (EBPs), treatment and outcomes for persons with mental disorders remain suboptimal. Replicating Effective Programs (REP), an effective implementation strategy, still resulted in less than half of sites using an EBP. The primary aim of this cluster randomized trial is to determine, among sites not initially responding to REP, the effect of adaptive implementation strategies that begin with an External Facilitator (EF) or with an External Facilitator plus an Internal Facilitator (IF) on improved EBP use and patient outcomes in 12 months. Methods Design: This study employs a sequential multiple assignment randomized trial (SMART) design to build an adaptive implementation strategy. The EBP to be implemented is life goals (LG) for patients with mood disorders across 80 community-based outpatient clinics (N = 1,600 patients) from different U.S. regions. Sites not initially responding to REP (defined as <50 patients receiving ≥3 EBP sessions) will be randomized to receive additional support from an EF or both EF IF. Additionally, sites randomized to EF and still not responsive will be randomized to continue with EF alone or to receive EF IF. The EF provides technical expertise in adapting LG in routine practice, whereas the on-site IF has direct reporting relationships to site leadership to support LG use in routine practice. The primary outcome is mental health-related quality of life; secondary outcomes include receipt of LG sessions, mood symptoms, implementation costs, and organizational change. In exploratory tensor mining, a common problem is how to analyze a set of variables across a set of subjects whose observations do not align naturally. For example, when modeling medical features across a set of patients, the number and duration of treatments may vary widely in time, meaning there is no meaningful way to align their clinical records across time points for analysis purposes. To handle such data, the state-of-the-art tensor model is the so-called PARAFAC2, which yields interpretable and robust output and can naturally handle sparse data. However, its main limitation up to now has been the lack of efficient algorithms that can handle large-scale datasets. In this work, we fill this gap by developing a scalable method to compute the PARAFAC2 decomposition of large and sparse datasets, called SPARTan. Our method exploits special structure within PARAFAC2, leading to a novel algorithmic reformulation that is both faster (in absolute time) and more memory-efficient than prior work. We evaluate SPARTan on both synthetic and real datasets, showing 22X performance gains over the best previous implementation and also handling larger problem instances for which the baseline fails. Furthermore, we are able to apply SPARTan to the mining of temporally-evolving phenotypes on data taken from real and medically complex pediatric patients. The clinical meaningfulness of the phenotypes identified in this process, as well as their temporal evolution over time for several patients, have been endorsed by clinical experts. Internet and the new technologies are generating new scenarios with and a significant increase of data volumes. The treatment of this huge quantity of information is impossible with traditional methodologies and we need to design new approaches towards distributed paradigms such as MapReduce. This situation is widely known in the literature as Big Data. This contribution presents a first approach to handle fuzzy emerging patterns in big data environments. This new algorithm is called EvAFP-Spark and is development in Apache Spark based on MapReduce. The use of this paradigm allows us the analysis of huge datasets efficiently. The main idea of EvAEFP-Spark is to modify the methodology of evaluation of the populations in the evolutionary process. In this way, a population is evaluated in the different maps, obtained in the Map phase of the paradigm, and for each one a confusion matrix is obtained. Then, the Reduce function accumulates the confusion matrix for each map in a general matrix in order to evaluate the fitness of the individuals. An experimental study with high dimensional datasets is performed in order to show the advantages of this algorithm in emerging patterns mining. Objective: To examine the accuracy and effectiveness of a novel virtual reality (VR) simulation tool in capturing Parkinson’s disease psychosis (PDP) Background: Despite its frequency, there remains confusion regarding diagnosing PDP. This may reflect knowledge gaps of its spectrum (i.e., illusions, hallucinations, delusions), overlapping non-motor issues (i.e., vivid dreams, dementia), failure of patients caregivers and healthcare providers (HCP) to discuss PDP, or an inability to “see what patients see” in visual hallucinations. With VR technology, one can simulate experiences not possible in “real life,” and these simulations may provide useful educational tools. Design methods: We developed a novel VR simulation of PDP using Oculus rift technology and descriptions by patients, caregivers, and HCP. The simulation, which included two hallucinatory scenarios, was presented at the 2015 AAN and Movement Disorder Society meetings. Viewers at the latter meeting completed an IRB-approved questionnaire, using a 5-point Likert-scale (“very unlikely” to “very likely”), to rate the simulation’s accuracy of capturing PDP symptoms and impact, effectiveness as a teaching tool, influence on medical practice, and side effects. Results: Questionnaires were completed by 502 602 (83.4[percnt]) viewers (56.8[percnt] Movement Disorder specialists, 40[percnt] Allied HCP Other). Both VR scenarios accurately (“likely very likely”) captured PDP symptoms reported by patients and caregivers (76.4[percnt], 67.6[percnt], respectively) and their impact on patients and caregivers (78.4[percnt], 66.2[percnt], respectively). The scenarios were “likely very likely” to be effective teaching tools for HCP (87.5[percnt]) and caregivers (90.8[percnt]), though slightly less for patients (76.1[percnt]). About 50[percnt] of respondents would “likely very likely” change their medical practice. Few experienced difficulties using the simulation, though mild motion sickness was occasionally reported. Conclusions: Using virtual technology, viewers were able to experience PDP first-hand. The PDP VR simulation has potential as an educational tool for not only HCP, but also caregivers and patients. Greater awareness of PDP may help reduce diagnostic confusion and associated stigma. Disclosure: Dr. Goldman has received personal compensation for activities with Acadia, Teva and Pfizer as a consultant and Advisory Board Member. Dr. Goldman has received research support from NIH, Michael J. Fox Foundation, Rush University Medical Center, Parkinson9 Dr. Stebbins has nothing to disclose. Dr. Fredericks has nothing to disclose. Dr. Upchurch has nothing to disclose. Sooner the Cancer is diagnosed, better the chances of overall survival. Diagnosis and Prognosis are the two major challenging aspects which are to be addressed in treating cancer. Early diagnosis helps treating cancer before they become metastatic; in addition, prognosis will reveal the survival pattern for different attributes i.e., for specific drug, before and after the treatment. Better the diagnosis and prognosis, better the treatment outcome for Cancer. The very high-throughput technologies like NGS generatesexome data of a single cancer patient that ranges from 10 Giga bytes to 15 Giga bytes. This large amount of Omics data can only be analyzed and interpreted using data-sciences techniques and big-data computational models. Here we present a clinical expert system for predicting the outcome of cancer patients from big-data genomics using the inference engine which makes decision from our in-house knowledge base. Multiple treatments are available for nearly all the mood disorders. This range of treatment options adds a new dimension of choice to clinical decision making. In addition to prescribing the best initial treatment, clinicians should have an algorithm for deciding if and when to make subsequent changes in treatment to take advantage of second-line treatment options when necessary. This article aims to 1) show that a wide variety of clinical decisions can be framed as choices among adaptive (within-patient) threshold-based strategies or algorithms, illustrating the generality of the concept; 2) illustrate two ways to design randomized clinical trials to compare treatment strategies with each other to decide which strategy is best; and 3) discuss some of the advantages offered by these designs, in terms of both patient acceptability and adherence to experimental protocols. Clinicians spend a significant amount of time inputting free-form textual notes into Electronic Health Records (EHR) systems. Much of this documentation work is seen as a burden, reducing time spent with patients and contributing to clinician burnout. With the aspiration of AI-assisted note-writing, we propose a new language modeling task predicting the content of notes conditioned on past data from a patient's medical record, including patient demographics, labs, medications, and past notes. We train generative models using the public, de-identified MIMIC-III dataset and compare generated notes with those in the dataset on multiple measures. We find that much of the content can be predicted, and that many common templates found in notes can be learned. We discuss how such models can be useful in supporting assistive note-writing features such as error-detection and auto-complete. There is growing interest in how best to adapt and re-adapt treatments to individuals to maximize clinical benefit. In response, adaptive treatment strategies (ATS), which operationalize adaptive, sequential clinical decision making, have been developed. From a patient's perspective an ATS is a sequence of treatments, each individualized to the patient's evolving health status. From a clinician's perspective, an ATS is a sequence of decision rules that input the patient's current health status and output the next recommended treatment. Sequential multiple assignment randomized trials (SMART) have been developed to address the sequencing questions that arise in the development of ATSs, but SMARTs are relatively new in clinical research. This article provides an introduction to ATSs and SMART designs. This article also discusses the design of SMART pilot studies to address feasibility concerns, and to prepare investigators for a full-scale SMART. As an example, we consider an example SMART for the development of an ATS in the treatment of pediatric generalized anxiety disorders. Using the example SMART, we identify and discuss design issues unique to SMARTs that are best addressed in an external pilot study prior to the full-scale SMART. We also address the question of how many participants are needed in a SMART pilot study. A properly executed pilot study can be used to effectively address concerns about acceptability and feasibility in preparation for (that is, prior to) executing a full-scale SMART. Recent advances in information extraction have led to huge knowledge bases (KBs), which capture knowledge in a machine-readable format. Inductive Logic Programming (ILP) can be used to mine logical rules from the KB. These rules can help deduce and add missing knowledge to the KB. While ILP is a mature field, mining logical rules from KBs is different in two aspects: First, current rule mining systems are easily overwhelmed by the amount of data (state-of-the art systems cannot even run on today's KBs). Second, ILP usually requires counterexamples. KBs, however, implement the open world assumption (OWA), meaning that absent data cannot be used as counterexamples. In this paper, we develop a rule mining model that is explicitly tailored to support the OWA scenario. It is inspired by association rule mining and introduces a novel measure for confidence. Our extensive experiments show that our approach outperforms state-of-the-art approaches in terms of precision and coverage. Furthermore, our system, AMIE, mines rules orders of magnitude faster than state-of-the-art approaches. Implantable medical devices (IMDs) improve patients' quality of life and help sustain their lives. In this study, we explore patient views and values regarding their devices to inform the design of computer security for wireless IMDs. We interviewed 13 individuals with implanted cardiac devices. Key questions concerned the evaluation of 8 mockups of IMD security systems. Our results suggest that some systems that are technically viable are nonetheless undesirable to patients. Patients called out a number of values that affected their attitudes towards the systems, including perceived security, safety, freedom from unwanted cultural and historical associations, and self-image. In our analysis, we extend the Value Sensitive Design value dams and flows technique in order to suggest multiple, complementary systems; in our discussion, we highlight some of the usability, regulatory, and economic complexities that arise from offering multiple options. We conclude by offering design guidelines for future security systems for IMDs.