28,499 research outputs found

    Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU

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    Machine learning approaches have been effective in predicting adverse outcomes in different clinical settings. These models are often developed and evaluated on datasets with heterogeneous patient populations. However, good predictive performance on the aggregate population does not imply good performance for specific groups. In this work, we present a two-step framework to 1) learn relevant patient subgroups, and 2) predict an outcome for separate patient populations in a multi-task framework, where each population is a separate task. We demonstrate how to discover relevant groups in an unsupervised way with a sequence-to-sequence autoencoder. We show that using these groups in a multi-task framework leads to better predictive performance of in-hospital mortality both across groups and overall. We also highlight the need for more granular evaluation of performance when dealing with heterogeneous populations.Comment: KDD 201

    Processing of Electronic Health Records using Deep Learning: A review

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    Availability of large amount of clinical data is opening up new research avenues in a number of fields. An exciting field in this respect is healthcare, where secondary use of healthcare data is beginning to revolutionize healthcare. Except for availability of Big Data, both medical data from healthcare institutions (such as EMR data) and data generated from health and wellbeing devices (such as personal trackers), a significant contribution to this trend is also being made by recent advances on machine learning, specifically deep learning algorithms

    Prediction of future hospital admissions - what is the tradeoff between specificity and accuracy?

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    Large amounts of electronic medical records collected by hospitals across the developed world offer unprecedented possibilities for knowledge discovery using computer based data mining and machine learning. Notwithstanding significant research efforts, the use of this data in the prediction of disease development has largely been disappointing. In this paper we examine in detail a recently proposed method which has in preliminary experiments demonstrated highly promising results on real-world data. We scrutinize the authors' claims that the proposed model is scalable and investigate whether the tradeoff between prediction specificity (i.e. the ability of the model to predict a wide number of different ailments) and accuracy (i.e. the ability of the model to make the correct prediction) is practically viable. Our experiments conducted on a data corpus of nearly 3,000,000 admissions support the authors' expectations and demonstrate that the high prediction accuracy is maintained well even when the number of admission types explicitly included in the model is increased to account for 98% of all admissions in the corpus. Thus several promising directions for future work are highlighted.Comment: In Proc. International Conference on Bioinformatics and Computational Biology, April 201

    Mining health knowledge graph for health risk prediction

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    Nowadays classification models have been widely adopted in healthcare, aiming at supporting practitioners for disease diagnosis and human error reduction. The challenge is utilising effective methods to mine real-world data in the medical domain, as many different models have been proposed with varying results. A large number of researchers focus on the diversity problem of real-time data sets in classification models. Some previous works developed methods comprising of homogeneous graphs for knowledge representation and then knowledge discovery. However, such approaches are weak in discovering different relationships among elements. In this paper, we propose an innovative classification model for knowledge discovery from patients’ personal health repositories. The model discovers medical domain knowledge from the massive data in the National Health and Nutrition Examination Survey (NHANES). The knowledge is conceptualised in a heterogeneous knowledge graph. On the basis of the model, an innovative method is developed to help uncover potential diseases suffered by people and, furthermore, to classify patients’ health risk. The proposed model is evaluated by comparison to a baseline model also built on the NHANES data set in an empirical experiment. The performance of proposed model is promising. The paper makes significant contributions to the advancement of knowledge in data mining with an innovative classification model specifically crafted for domain-based data. In addition, by accessing the patterns of various observations, the research contributes to the work of practitioners by providing a multifaceted understanding of individual and public health

    Affective Medicine: a review of Affective Computing efforts in Medical Informatics

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    Background: Affective computing (AC) is concerned with emotional interactions performed with and through computers. It is defined as “computing that relates to, arises from, or deliberately influences emotions”. AC enables investigation and understanding of the relation between human emotions and health as well as application of assistive and useful technologies in the medical domain. Objectives: 1) To review the general state of the art in AC and its applications in medicine, and 2) to establish synergies between the research communities of AC and medical informatics. Methods: Aspects related to the human affective state as a determinant of the human health are discussed, coupled with an illustration of significant AC research and related literature output. Moreover, affective communication channels are described and their range of application fields is explored through illustrative examples. Results: The presented conferences, European research projects and research publications illustrate the recent increase of interest in the AC area by the medical community. Tele-home healthcare, AmI, ubiquitous monitoring, e-learning and virtual communities with emotionally expressive characters for elderly or impaired people are few areas where the potential of AC has been realized and applications have emerged. Conclusions: A number of gaps can potentially be overcome through the synergy of AC and medical informatics. The application of AC technologies parallels the advancement of the existing state of the art and the introduction of new methods. The amount of work and projects reviewed in this paper witness an ambitious and optimistic synergetic future of the affective medicine field

    Discovering Hidden Signs and Symptoms of Heart Failure in the Electronic Health Record Using the Omaha System

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    Purpose/Background/Significance: For the past 30 years, heart failure has been in the top 3 readmission diagnoses with patients discharged to community care. This is costly to the healthcare system and negatively impacts the patient’s quality of life. The purpose of this study is to evaluate a community care database to determine if previously under-considered latent variables exist that could provide early detection of heart failure signs and symptoms. Theoretical/Conceptual Framework: The theoretical and conceptual frameworks surrounding this work are the Omaha System and Donabedian’s structure, process, and outcomes theory for healthcare quality improvement supported by Neuman’s Systems Model. The Omaha System was constructed on the combined basis of these theoretical underpinnings by three components: The Problem Classification Scheme, The Intervention Scheme, and The Problem Rating Scale for Outcomes. Methods: This study was a retrospective, descriptive, observational, comparative study using secondary data. Major HF-associated signs and symptoms related to problems of circulation and respiration were queried. Latent Class Analysis (LCA) was used to identify if other significant groupings of signs and symptoms were associated with heart failure signs and symptoms. Findings: Evaluation of the sample for signs and symptoms of HF related to the Omaha System Problems of Respiration and Circulation revealed 4215 individuals. LCA revealed four significant groupings of signs and symptoms related to the problems of Mental health, Cognition, Heart failure and General/Other. Further analysis determined that the HF group had the most interventions and visits yet had the lowest change in Knowledge, Behavior, and Status scores indicating that HF required intensive outpatient care to maintain their status in the community care environment without benefiting from significant final status improvement. Analysis revealed that patients with Cognition group benefited the most from increased visits and interventions. Conclusion: Patients exhibiting signs and symptoms of heart failure may also experience signs and symptoms of Mental health and Cognition changes, which may either contribute to heart failure exacerbation, or be as a result of the heart failure disease process. Further research is needed to examine possible mechanisms that may help defer HF exacerbations
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