Automated Identification of Diagnosis and Co-morbidity in Clinical Records

2009 ◽  
Vol 48 (06) ◽  
pp. 546-551 ◽  
Author(s):  
C. Cano ◽  
A. Blanco ◽  
L. Peshkin
Keyword(s):  
Rapid Prototyping ◽  
Computational Linguistics ◽  
High Performance ◽  
Ad Hoc ◽  
Expert Knowledge ◽  
Free Text ◽  
Automated Identification ◽  
Domain Specific ◽  
Co Morbidity ◽  
Clinical Records

Summary Objectives: Automated understanding of clinical records is a challenging task involving various legal and technical difficulties. Clinical free text is inherently redundant, unstructured, and full of acronyms, abbreviations and domain-specific language which make it challenging to mine automatically. There is much effort in the field focused on creating specialized ontology, lexicons and heuristics based on expert knowledge of the domain. However, ad-hoc solutions poorly generalize across diseases or diagnoses. This paper presents a successful approach for a rapid prototyping of a diagnosis classifier based on a popular computational linguistics platform. Methods: The corpus consists of several hundred of full length discharge summaries provided by Partners Healthcare. The goal is to identify a diagnosis and assign co-morbidity. Our approach is based on the rapid implementation of a logistic regression classifier using an existing toolkit: LingPipe (http://alias-i.com/lingpipe). We implement and compare three different classifiers. The baseline approach uses character 5-grams as features. The second approach uses a bag-of-words representation enriched with a small additional set of features. The third approach reduces a feature set to the most informative features according to the information content. Results: The proposed systems achieve high performance (average F-micro 0.92) for the task. We discuss the relative merit of the three classifiers. Supplementary material with detailed results is available at: http://decsai.ugr.es/~ccano/LR/supplementary_material/ Conclusions: We show that our methodology for rapid prototyping of a domain-unaware system is effective for building an accurate classifier for clinical records.

Data Science and Computational Biology

Biotechnology ◽  
2019 ◽  
pp. 1277-1292
Author(s):  
Singaraju Jyothi ◽  
Bhargavi P
Keyword(s):  
Computational Biology ◽  
High Performance ◽  
Data Science ◽  
Personalised Medicine ◽  
Big Data Analytics ◽  
Biological Data ◽  
Domain Specific ◽  
Data Volume ◽  
Domain Specific Knowledge ◽  
Clinical Records

Data Science and Computational biology is an interdisciplinary program that brings together the domain specific knowledge of science and engineering with relevant areas of computing and bioinformatics. Data science has the potential to revolutionise healthcare, and respond to the increasing volume and complexity in biomedical and bioinformatics data. From genomics to clinical records, from imaging to mobile health and personalised medicine, the data volume in biomedical research presents urgent challenges for computer science. This chapter elevates the researchers in what way data science play important role in Computational Biology such as Bio-molecular Computation, Computational Photonics, Medical Imaging, Scientific Computing, Structural Biology, Bioinformatics and Bio-Computing etc. Big data analytics of biological data bases, high performance computing in large sequence of genome database and Scientific Visualization are also discussed in this chapter.

Data Science and Computational Biology

2018 ◽  
pp. 152-172
Author(s):  
Singaraju Jyothi ◽  
Bhargavi P
Keyword(s):  
Computational Biology ◽  
High Performance ◽  
Data Science ◽  
Personalised Medicine ◽  
Big Data Analytics ◽  
Biological Data ◽  
Domain Specific ◽  
Data Volume ◽  
Domain Specific Knowledge ◽  
Clinical Records

Data Science and Computational biology is an interdisciplinary program that brings together the domain specific knowledge of science and engineering with relevant areas of computing and bioinformatics. Data science has the potential to revolutionise healthcare, and respond to the increasing volume and complexity in biomedical and bioinformatics data. From genomics to clinical records, from imaging to mobile health and personalised medicine, the data volume in biomedical research presents urgent challenges for computer science. This chapter elevates the researchers in what way data science play important role in Computational Biology such as Bio-molecular Computation, Computational Photonics, Medical Imaging, Scientific Computing, Structural Biology, Bioinformatics and Bio-Computing etc. Big data analytics of biological data bases, high performance computing in large sequence of genome database and Scientific Visualization are also discussed in this chapter.

APA Book Talk: The Psychology of High Performance: Developing Human Potential Into Domain-Specific Talent

2020 ◽  
Author(s):  
Jamie Buck ◽  
Rena Subotnik ◽  
Frank Worrell ◽  
Paula Olszewski-Kubilius ◽  
Chi Wang
Keyword(s):  
High Performance ◽  
Human Potential ◽  
Domain Specific ◽  
Book Talk

scholarly journals Templated Text Synthesis for Expert-Guided Multi-Label Extraction from Radiology Reports

2021 ◽  
Vol 3 (2) ◽  
pp. 299-317
Author(s):  
Patrick Schrempf ◽  
Hannah Watson ◽  
Eunsoo Park ◽  
Maciej Pajak ◽  
Hamish MacKinnon ◽  
...  
Keyword(s):  
Domain Knowledge ◽  
Expert Knowledge ◽  
Medical Image Analysis ◽  
Imaging Data ◽  
Data Synthesis ◽  
Domain Specific ◽  
Radiology Reports ◽  
Training Examples ◽  
Analysis Models ◽  
Difficult Cases

Training medical image analysis models traditionally requires large amounts of expertly annotated imaging data which is time-consuming and expensive to obtain. One solution is to automatically extract scan-level labels from radiology reports. Previously, we showed that, by extending BERT with a per-label attention mechanism, we can train a single model to perform automatic extraction of many labels in parallel. However, if we rely on pure data-driven learning, the model sometimes fails to learn critical features or learns the correct answer via simplistic heuristics (e.g., that “likely” indicates positivity), and thus fails to generalise to rarer cases which have not been learned or where the heuristics break down (e.g., “likely represents prominent VR space or lacunar infarct” which indicates uncertainty over two differential diagnoses). In this work, we propose template creation for data synthesis, which enables us to inject expert knowledge about unseen entities from medical ontologies, and to teach the model rules on how to label difficult cases, by producing relevant training examples. Using this technique alongside domain-specific pre-training for our underlying BERT architecture i.e., PubMedBERT, we improve F1 micro from 0.903 to 0.939 and F1 macro from 0.512 to 0.737 on an independent test set for 33 labels in head CT reports for stroke patients. Our methodology offers a practical way to combine domain knowledge with machine learning for text classification tasks.

Fuzzy System for DOA Estimation of Coherent Signals

2013 ◽  
Vol 427-429 ◽  
pp. 575-581
Author(s):  
Ya Ling Chen ◽  
Chien Chou Lin
Keyword(s):  
Fuzzy System ◽  
High Performance ◽  
Expert Knowledge ◽  
Doa Estimation ◽  
Coherent Signals ◽  
Fuzzy Logic Systems ◽  
Significant Performance ◽  
And Performance ◽  
Low Sensitivity ◽  
Resolution Estimation

This paper presents an efficient direction-of-arrival (DOA) Estimator for dealing with coherent signals. The empirical results show that significant performance degradation occurs when coherent signals coexist. Therefore, an utilizes the low sensitivity of Bartlett algorithm in estimation of DOAs for coherent signals to yield a low-resolution estimation of DOAs as initial search angle and uses fuzzy logic systems with incorporating expert knowledge to improve the resolution and performance of estimation of DOAs in coherent signals environment. Finally, numerical example was analyzed to illustrate high performance of the proposed method and to confirm the designed procedure.

scholarly journals Revisiting the CompCars Dataset for Hierarchical Car Classification: New Annotations, Experiments, and Results

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 596
Author(s):  
Marco Buzzelli ◽  
Luca Segantin
Keyword(s):  
Real World ◽  
High Performance ◽  
Ad Hoc ◽  
Future Research ◽  
Levels Of Detail ◽  
Excellent Starting Point ◽  
Starting Point ◽  
Hierarchical Nature ◽  
Multiple Levels ◽  
Entire Dataset

We address the task of classifying car images at multiple levels of detail, ranging from the top-level car type, down to the specific car make, model, and year. We analyze existing datasets for car classification, and identify the CompCars as an excellent starting point for our task. We show that convolutional neural networks achieve an accuracy above 90% on the finest-level classification task. This high performance, however, is scarcely representative of real-world situations, as it is evaluated on a biased training/test split. In this work, we revisit the CompCars dataset by first defining a new training/test split, which better represents real-world scenarios by setting a more realistic baseline at 61% accuracy on the new test set. We also propagate the existing (but limited) type-level annotation to the entire dataset, and we finally provide a car-tight bounding box for each image, automatically defined through an ad hoc car detector. To evaluate this revisited dataset, we design and implement three different approaches to car classification, two of which exploit the hierarchical nature of car annotations. Our experiments show that higher-level classification in terms of car type positively impacts classification at a finer grain, now reaching 70% accuracy. The achieved performance constitutes a baseline benchmark for future research, and our enriched set of annotations is made available for public download.

scholarly journals Marine Network Protocols and Security Risks

2021 ◽  
Vol 1 (2) ◽  
pp. 239-251
Author(s):  
Ky Tran ◽  
Sid Keene ◽  
Erik Fretheim ◽  
Michail Tsikerdekis
Keyword(s):  
Network Protocols ◽  
Network Protocol ◽  
Identification System ◽  
Automated Identification ◽  
Security Risks ◽  
Domain Specific ◽  
Protocol Security ◽  
Challenges And Opportunities

Marine network protocols are domain-specific network protocols that aim to incorporate particular features within the specialized marine context that devices are implemented in. Devices implemented in such vessels involve critical equipment; however, limited research exists for marine network protocol security. In this paper, we provide an analysis of several marine network protocols used in today’s vessels and provide a classification of attack risks. Several protocols involve known security limitations, such as Automated Identification System (AIS) and National Marine Electronic Association (NMEA) 0183, while newer protocols, such as OneNet provide more security hardiness. We further identify several challenges and opportunities for future implementations of such protocols.

scholarly journals A META-TOOL TO SUPPORT THE DEVELOPMENT OF KNOWLEDGE ENGINEERING METHODOLOGIES AND PROJECTS

2012 ◽  
Vol 22 (08) ◽  
pp. 1055-1083
Author(s):  
JOSÉ ELOY FLÓREZ ◽  
JAVIER CARBÓ ◽  
FERNANDO FERNÁNDEZ
Keyword(s):  
Knowledge Acquisition ◽  
Ad Hoc ◽  
Knowledge Engineering ◽  
Domain Specific ◽  
Advantages And Disadvantages ◽  
Knowledge Based ◽  
Combined Application ◽  
Processing Information ◽  
Knowledge Acquisition Methods ◽  
Applied Knowledge

Knowledge-based systems (KBSs) or expert systems (ESs) are able to solve problems generally through the application of knowledge representing a domain and a set of inference rules. In knowledge engineering (KE), the use of KBSs in the real world, three principal disadvantages have been encountered. First, the knowledge acquisition process has a very high cost in terms of money and time. Second, processing information provided by experts is often difficult and tedious. Third, the establishment of mark times associated with each project phase is difficult due to the complexity described in the previous two points. In response to these obstacles, many methodologies have been developed, most of which include a tool to support the application of the given methodology. Nevertheless, there are advantages and disadvantages inherent in KE methodologies, as well. For instance, particular phases or components of certain methodologies seem to be better equipped than others to respond to a given problem. However, since KE tools currently available support just one methodology the joint use of these phases or components from different methodologies for the solution of a particular problem is hindered. This paper presents KEManager, a generic meta-tool that facilitates the definition and combined application of phases or components from different methodologies. Although other methodologies could be defined and combined in the KEManager, this paper focuses on the combination of two well-known KE methodologies, CommonKADS and IDEAL, together with the most commonly-applied knowledge acquisition methods. The result is an example of the ad hoc creation of a new methodology from pre-existing methodologies, allowing for the adaptation of the KE process to an organization or domain-specific characteristics. The tool was evaluated by students at Carlos III University of Madrid (Spain).

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