Ambiguity in the fish naming is present in several fish species database, especially for fish in Siluformes order. To fix the ambiguity, a visual intelligent system is needed to automate the fish naming correction in ...
Ambiguity in the fish naming is present in several fish species database, especially for fish in Siluformes order. To fix the ambiguity, a visual intelligent system is needed to automate the fish naming correction in the database. In this study, we developed a deep-learning-based model as the core of the intelligent system. The proposed model achieved 89% accuracy for the classification of three genera in Siluformes order: Mystus, Hemibagrus, and Glyptothorax.
Research software is essential to modern research, but it requires ongoing human effort to sustain: to continually adapt to changes in dependencies, to fix bugs, and to add new features. Software sustainability instit...
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Weeds are one of the organisms that interfere with plant growth. Information about the identity of weeds becomes very important on plantations. Although weeds data digitization has been done a lot, currently there is ...
Weeds are one of the organisms that interfere with plant growth. Information about the identity of weeds becomes very important on plantations. Although weeds data digitization has been done a lot, currently there is still not much weeds data information system can be accessed online. Weeds management is often dealt with weeds herbarium or weeds photographs. In an effort to provide a good information system for farmers, this research aims to create a database of various types of weeds and an information system that can be accessed online. The methodology for developing a weed catalog information system uses the Software Development Life Cycle (SDLC). Weed samples in database were collected through systematic random sampling in the form of images and text. The result of this study is a Weeds Electronic Catalog or Weeds e-Catalog that can facilitate the information of weeds identity such as names, classifications, morphology, life cycles, and habitats of various types of weeds that grow on plantation land. Weeds e-Catalog can be used by plantation practitioners and farmers to make decisions in controlling weeds.
Climate anomalies are considered as an important factor closely related to many disasters causing many human losses, such as airline crash, wildfires, drought and flooding in many areas. Many researchers have projecte...
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Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify ...
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MSC Codes 35B27Computational models in cardiac electrophysiology are notorious for long runtimes, restricting the numbers of nodes and mesh elements in the numerical discretisations used for their solution. This makes...
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In spite of clinical notes in Electronic Health Records (EHR) providing abundant information about patient health, effective modeling of clinical notes remains in its infancy. A patient's clinical notes correspond...
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ISBN:
(数字)9781728108582
ISBN:
(纸本)9781728108599
In spite of clinical notes in Electronic Health Records (EHR) providing abundant information about patient health, effective modeling of clinical notes remains in its infancy. A patient's clinical notes correspond to a sequence of free-form texts generated by health care professionals over time; with each note in turn containing a sequence of words. Additionally, notes are accompanied by external attributes at multiple layers such as the time at which each note was created (note level) or the demographics of the patient (patient level). Thus, EHR notes correspond to a nested structure of text sequences augmented with external multi-layer attributes. To model this complex problem, we propose an Attributed Hierarchical Attention model, named HAC-RNN, that integrates multiple RNN layers that encode nested sequential notes with contextual and temporal attention layers that are conditioned on the external attributes. While the bottom layer of HAC-RNN is responsible for contextual summarization of the note content, the top layer combs through the entire timeline of notes to focus on those which are most relevant. These attention layers, which are each conditioned on layer-specific hierarchical attributes, allow personalized predictions through inferring patient *** evaluate HAC-RNN using three real-world medical tasks, detecting in-hospital acquired infections and predicting patient mortality using critical care database MIMIC-III. Our results demonstrate that our model significantly outperforms state-of-the-art techniques for all tasks.
Clinical notes correspond to sequences of reports about patients written over time by health-care professionals. With the availability of massive Electronic Health Record (EHR) datasets composed of such clinical notes...
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ISBN:
(数字)9781728108582
ISBN:
(纸本)9781728108599
Clinical notes correspond to sequences of reports about patients written over time by health-care professionals. With the availability of massive Electronic Health Record (EHR) datasets composed of such clinical notes, machine learning models on these rich text data series are being developed for patient outcome prediction from infection diagnosis to mortality. While current models focus on content in these unstructured clinical notes, we postulate that timing of the medical events that are explained in the notes are equally crucial. We thus propose a novel attention mechanism composed of dual-attention blocks based on a rich diversity of time representations. We then pair this mechanism with an LSTM, resulting in our proposed time-aware recurrent network TEND-LSTM. TEND-LSTM learns an integrated set of attention weights, with the first attention based on the content of the clinical notes and the second based on when the notes were taken. Together, they are combined using a deep-attention network layer. The proposed dual attention mechanism not only learns a function of time incorporating different aspects of the temporal nature of note instances but also automatically finds a balance between how much attention to put on content versus time. We evaluate our model on six medical tasks using data sets from the publicly-available MIMIC III database from the Beth Israel Deaconess Medical Center. Our results demonstrate that TEND-LSTM outperforms state-of-the-art methods by a large margin.
In general, performing a nonlinearity time series analysis in the modeling of data can reach a robust and increase the quality of the results. Wavelet methods have successfully been applied in a great variety of appli...
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