| 1. |
- Berndorfer, Stefan, et al.
(författare)
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Automated Diagnosis Coding with Combined Text Representations
- 2017
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Ingår i: Informatics for Health. - : IOS Press. - 9781614997528 - 9781614997535 ; , s. 201-205
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Konferensbidrag (refereegranskat)abstract
- Automated diagnosis coding can be provided efficiently by learning predictive models from historical data; however, discriminating between thousands of codes while allowing a variable number of codes to be assigned is extremely difficult. Here, we explore various text representations and classification models for assigning ICD-9 codes to discharge summaries in MIMIC-III. It is shown that the relative effectiveness of the investigated representations depends on the frequency of the diagnosis code under consideration and that the best performance is obtained by combining models built using different representations.
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| 2. |
- Dziadek, Juliusz, et al.
(författare)
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Improving Terminology Mapping in Clinical Text with Context-Sensitive Spelling Correction
- 2017
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Ingår i: Informatics for Health. - : IOS Press. - 9781614997528 - 9781614997535 ; , s. 241-245
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Konferensbidrag (refereegranskat)abstract
- The mapping of unstructured clinical text to an ontology facilitates meaningful secondary use of health records but is non-trivial due to lexical variation and the abundance of misspellings in hurriedly produced notes. Here, we apply several spelling correction methods to Swedish medical text and evaluate their impact on SNOMED CT mapping; first in a controlled evaluation using medical literature text with induced errors, followed by a partial evaluation on clinical notes. It is shown that the best-performing method is context-sensitive, taking into account trigram frequencies and utilizing a corpus-based dictionary.
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| 3. |
- Henriksson, Aron, et al.
(författare)
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Prevalence Estimation of Protected Health Information in Swedish Clinical Text
- 2017
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Ingår i: Informatics for Health. - : IOS Press. - 9781614997528 - 9781614997535 ; , s. 216-220
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Konferensbidrag (refereegranskat)abstract
- Obscuring protected health information (PHI) in the clinical text of health records facilitates the secondary use of healthcare data in a privacy-preserving manner. Although automatic de-identification of clinical text using machine learning holds much promise, little is known about the relative prevalence of PHI in different types of clinical text and whether there is a need for domain adaptation when learning predictive models from one particular domain and applying it to another. In this study, we address these questions by training a predictive model and using it to estimate the prevalence of PHI in clinical text written (1) in different clinical specialties, (2) in different types of notes (i.e., under different headings), and (3) by persons in different professional roles. It is demonstrated that the overall PHI density is 1.57%; however, substantial differences exist across domains.
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| 4. |
- Sundvall, Erik, 1973-, et al.
(författare)
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Querying archetype-based Electronic Health Records using Hadoop and Dewey encoding of openEHR models
- 2017
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Ingår i: Informatics for Health. - Amsterdam, The Netherlands : IOS Press. - 9781614997528 - 9781614997535 ; , s. 406-410
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Konferensbidrag (refereegranskat)abstract
- Archetype-based Electronic Health Record (EHR) systems using generic reference models from e.g. openEHR, ISO 13606 or CIMI should be easy to update and reconfigure with new types (or versions) of data models or entries, ideally with very limited programming or manual database tweaking. Exploratory research (e.g. epidemiology) leading to ad-hoc querying on a population-wide scale can be a challenge in such environments. This publication describes implementation and test of an archetype-aware Dewey encoding optimization that can be used to produce such systems in environments supporting relational operations, e.g. RDBMs and distributed map-reduce frameworks like Hadoop. Initial testing was done using a nine-node 2.2 GHz quad-core Hadoop cluster querying a dataset consisting of targeted extracts from 4+ million real patient EHRs, query results with sub-minute response time were obtained.
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| 5. |
- Gharehbaghi, Arash, et al.
(författare)
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A Decision Support System for Cardiac Disease Diagnosis Based on Machine Learning Methods
- 2017
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Ingår i: Studies in Health Technology and Informatics. - : IOS Press. - 0926-9630 .- 1879-8365. - 9781614997528 ; 235, s. 43-47
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a decision support system for screening pediatric cardiac disease in primary healthcare centres relying on the heart sound time series analysis. The proposed system employs our processing method which is based on the hidden Markov model for extracting appropriate information from the time series. The binary output resulting from the method is discriminative for the two classes of time series existing in our databank, corresponding to the children with heart disease and the healthy ones. A total 90 children referrals to a university hospital, constituting of 55 healthy and 35 children with congenital heart disease, were enrolled into the study after obtaining the informed consent. Accuracy and sensitivity of the method was estimated to be 86.4% and 85.6%, respectively, showing a superior performance than what a paediatric cardiologist could achieve performing auscultation. The method can be easily implemented using mobile and web technology to develop an easy-To-use tool for paediatric cardiac disease diagnosis. © 2017 European Federation for Medical Informatics (EFMI) and IOS Press.
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