- A Framework for Data-Driven Adaptive GUI Generation Based on DICOM. [Journal Article]
- JBJ Biomed Inform 2018 Nov 09
- Computer applications for diagnostic medical imaging provide generally a wide range of tools to support physicians in their daily diagnosis activities. Unfortunately, some functionalities are special...
Computer applications for diagnostic medical imaging provide generally a wide range of tools to support physicians in their daily diagnosis activities. Unfortunately, some functionalities are specialized for specific diseases or imaging modalities, while other ones are useless for the images under investigation. Nevertheless, the corresponding Graphical User Interface (GUI) widgets are still present on the screen reducing the image visualization area. As a consequence, the physician may be affected by cognitive overload and visual stress causing a degradation of performances, mainly due to unuseful widgets. In clinical environments, a GUI must represent a sequence of steps for image investigation following a well-defined workflow. This paper proposes a software framework aimed at addressing the issues outlined before. Specifically, we designed a DICOM based mechanism of data-driven GUI generation, referring to the examined body part and imaging modality as well as to the medical image analysis task to perform. In this way, the self-configuring GUI is generated on-the-fly, so that just specific functionalities are active according to the current clinical scenario. Such a solution provides also a tight integration with the DICOM standard, which considers various aspects of the technology in medicine but does not address GUI specification issues. The proposed workflow is designed for diagnostic workstations with a local file system on an interchange media acting inside or outside the hospital ward. Accordingly, the DICOMDIR conceptual data model, defined by a hierarchical structure, is exploited and extended to include the GUI information thanks to a new Information Object Module (IOM), which reuses the DICOM information model. The proposed framework exploits the DICOM standard representing an enabling technology for an auto-consistent solution in medical diagnostic applications. In this paper we present a detailed description of the framework, its software design, and a proof-of-concept implementation as a suitable plug-in of the OsiriX imaging software.
- A Method for Harmonization of Clinical Abbreviation and Acronym Sense Inventories. [Journal Article]
- JBJ Biomed Inform 2018 Nov 07
- CONCLUSIONS: Given the high coverage, harmonizing acronym sense inventories is a promising methodology to improve their comprehensiveness. Our method is automated, leverages the extensive resources already devoted to developing institution-specific inventories in the United States, and may help generalize sense inventories to institutions who lack the resources to develop them. Future work should address quality issues in source inventories and explore additional approaches to establishing synonymy.
- A Smartwatch-Based Framework for Real-Time and Online Assessment and Mobility Monitoring. [Journal Article]
- JBJ Biomed Inform 2018 Nov 07
- Smartphone and smartwatch technology is changing the transmission and monitoring landscape for patients and research participants to communicate their healthcare information in real time. Flexible, b...
Smartphone and smartwatch technology is changing the transmission and monitoring landscape for patients and research participants to communicate their healthcare information in real time. Flexible, bidirectional and real-time control of communication allows development of a rich set of healthcare applications that can provide interactivity with the participant and adapt dynamically to their changing environment. Additionally, smartwatches have a variety of sensors suitable for collecting physical activity and location data. The combination of all these features makes it possible to transmit the collected data to a remote server, and thus, to monitor physical activity and potentially social activity in real time. As smartwatches exhibit high user acceptability and increasing popularity, they are ideal devices for monitoring activities for extended periods of time to investigate the physical activity patterns in free-living condition and their relationship with the seemingly random occurring illnesses, which have remained a challenge in the current literature. Therefore, the purpose of this study was to develop a smartwatch-based framework for real-time and online assessment and mobility monitoring (ROAMM). The proposed ROAMM framework will include a smartwatch application and server. The smartwatch application will be used to collect and preprocess data. The server will be used to store and retrieve data, remote monitor, and for other administrative purposes. With the integration of sensor-based and user-reported data collection, the ROAMM framework allows for data visualization and summary statistics in real-time.
- Lifetime trajectory simulation of chronic disease progression and comorbidity development. [Journal Article]
- JBJ Biomed Inform 2018 Nov 07
- CONCLUSIONS: The lifetime disease progression trajectory constructed for each person in the cohort describes how a person starts healthy, becomes at risk, then progresses to one or more chronic conditions, and finally deteriorates to various complications over the years. This study may help us have a better understanding of chronic disease progression and comorbidity development, hence add values to chronic disease prevention and management.
- Integrated Bioinformatical Analysis for Identificating the Therapeutic Targets of Aspirin in Small Cell Lung Cancer. [Journal Article]
- JBJ Biomed Inform 2018 Nov 07
- CONCLUSIONS: The integrated bioinformatical analysis could improve our understanding of the underlying molecular mechanism about how aspirin working in SCLC. Integrated bioinformatical analysis may be considered as a new paradigm for guiding future studies about interaction in drugs and diseases.
- CBN: Constructing a Clinical Bayesian Network based on Data from the Electronic Medical Record. [Journal Article]
- JBJ Biomed Inform 2018 Nov 03
- The process of learning candidate causal relationships involving diseases and symptoms from electronic medical records (EMRs) is the first step towards learning models that perform diagnostic inferen...
The process of learning candidate causal relationships involving diseases and symptoms from electronic medical records (EMRs) is the first step towards learning models that perform diagnostic inference directly from real healthcare data. However, the existing diagnostic inference systems rely on knowledge bases such as ontology that are manually compiled through a labour-intensive process or automatically derived using simple pairwise statistics. We explore CBN, a Clinical Bayesian Network construction for medical ontology probabilistic inference, to learn high-quality Bayesian topology and complete ontology directly from EMRs. Specifically, we first extract medical entity relationships from over 10,000 deidentified patient records and adopt the odds ratio (OR value) calculation and the K2 greedy algorithm to automatically construct a Bayesian topology. Then, Bayesian estimation is used for the probability distribution. Finally, we employ a Bayesian network to complete the causal relationship and probability distribution of ontology to enhance the ontology inference capability. By evaluating the learned topology versus the expert opinions of physicians and entropy calculations and by calculating the ontology-based diagnosis classification, our study demonstrates that the direct and automated construction of a high-quality health topology and ontology from medical records is feasible. Our results are reproducible, and we will release the source code and CN-Stroke knowledge graph of this work after publication1.
- Toward analyzing and synthesizing previous research in early prediction of cardiac arrest using machine learning based on a multi-layered integrative framework. [Review]
- JBJ Biomed Inform 2018 Oct 30
- CONCLUSIONS: According to the results, machine learning techniques can improve the outcome of cardiac arrest prediction. However, future research should be carried out to evaluate the efficiency of rarely-used algorithms and to address the challenges of external validation, implementation and adoption of machine learning models in real clinical environments.
- Using Clinical Natural Language Processing for Health Outcomes Research: Overview and Actionable Suggestions for Future Advances. [Journal Article]
- JBJ Biomed Inform 2018 Oct 24
- The importance of incorporating Natural Language Processing(NLP) methods in clinical informatics research has been increasingly recognized over the past years, and has led to transformative advances....
The importance of incorporating Natural Language Processing(NLP) methods in clinical informatics research has been increasingly recognized over the past years, and has led to transformative advances. Typically, clinical NLP systems are developed and evaluated on word, sentence, or document level annotations that model specific attributes and features, such as document content (e.g., patient status, or report type), document section types (e.g., current medications, past medical history, or discharge summary), named entities and concepts (e.g., diagnoses, symptoms, or treatments) or semantic attributes (e.g., negation, severity, or temporality). From a clinical perspective, on the other hand, research studies are typically modelled and evaluated on a patient- or population-level, such as predicting how a patient group might respond to specific treatments or patient monitoring over time. While some NLP tasks consider predictions at the individual or group user level, these tasks still constitute a minority. Owing to the discrepancy between scientific objectives of each field, and because of differences in methodological evaluation priorities, there is no clear alignment between these evaluation approaches. Here we provide a broad summary and outline of the challenging issues involved in defining appropriate intrinsic and extrinsic evaluation methods for NLP research that is to be used for clinical outcomes research, and vice-versa. A particular focus is placed on mental health research, an area still relatively understudied by the clinical NLP research community, but where NLP methods are of notable relevance. Recent advances in clinical NLP method development have been significant, but we propose more emphasis needs to be placed on rigorous evaluation for the field to advance further. To enable this, we provide actionable suggestions, including a minimal protocol that could be used when reporting clinical NLP method development and its evaluation.
- The Internet of Things (IoT): Informatics Methods for IoT-enabled Health Care. [Editorial]
- JBJ Biomed Inform 2018 Oct 17
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- Causal Discovery from Sequential Data in ALS Disease based on Entropy Criteria. [Journal Article]
- JBJ Biomed Inform 2018 Oct 16
- One of the most important issues in predictive modeling is to determine major cause factors of a phenomenon and causal relationships between them. Extracting causal relationships between parameters i...
One of the most important issues in predictive modeling is to determine major cause factors of a phenomenon and causal relationships between them. Extracting causal relationships between parameters in a natural phenomenon can be accomplished through checking the parameters' changes in consecutive events. In addition, using information and probabilistic theory help better conception of causal relationships of a phenomenon. Therefore, probabilistic causal discovery from sequential data of a natural phenomenon can be useful for dimension reduction and predicting the future trend of a process. In this paper, we introduce a novel method for causal discovery from a sequential data based on a probabilistic causal graph. In this method, first, Causal Feature Dependency matrix (CFD matrix) is generated based on the features' changes in consecutive events. Then, a probabilistic causal graph is created from CFD matrix. In this graph, some valueless features will be eliminated on the basis of entropy value of each conditional density function. Finally, prediction operation is performed based on the output of causal graph. Experimental results on the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) sequential data set from Amyotrophic Lateral Sclerosis (ALS) disease show that our proposed algorithm can predict the progression rate of ALS disease properly with high precision.