| 1. |
- Alam, Mahbub Ul, et al.
(författare)
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Terminology Expansion with Prototype Embeddings : Extracting Symptoms of Urinary Tract Infection from Clinical Text
- 2021
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Ingår i: Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF. - Setúbal : SciTePress. - 9789897584909 ; , s. 47-57
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Konferensbidrag (refereegranskat)abstract
- Many natural language processing applications rely on the availability of domain-specific terminologies containing synonyms. To that end, semi-automatic methods for extracting additional synonyms of a given concept from corpora are useful, especially in low-resource domains and noisy genres such as clinical text, where nonstandard language use and misspellings are prevalent. In this study, prototype embeddings based on seed words were used to create representations for (i) specific urinary tract infection (UTI) symptoms and (ii) UTI symptoms in general. Four word embedding methods and two phrase detection methods were evaluated using clinical data from Karolinska University Hospital. It is shown that prototype embeddings can effectively capture semantic information related to UTI symptoms. Using prototype embeddings for specific UTI symptoms led to the extraction of more symptom terms compared to using prototype embeddings for UTI symptoms in general. Overall, 142 additional UTI symp tom terms were identified, yielding a more than 100% increment compared to the initial seed set. The mean average precision across all UTI symptoms was 0.51, and as high as 0.86 for one specific UTI symptom. This study provides an effective and cost-effective solution to terminology expansion with small amounts of labeled data.
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| 2. |
- Ehrentraut, Claudia, et al.
(författare)
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Detecting hospital-acquired infections : A document classification approach using support vector machines and gradient tree boosting
- 2018
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Ingår i: Health Informatics Journal. - : SAGE Publications. - 1460-4582 .- 1741-2811. ; 24:1, s. 24-42
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Tidskriftsartikel (refereegranskat)abstract
- Hospital-acquired infections pose a significant risk to patient health, while their surveillance is an additional workload for hospital staff. Our overall aim is to build a surveillance system that reliably detects all patient records that potentially include hospital-acquired infections. This is to reduce the burden of having the hospital staff manually check patient records. This study focuses on the application of text classification using support vector machines and gradient tree boosting to the problem. Support vector machines and gradient tree boosting have never been applied to the problem of detecting hospital-acquired infections in Swedish patient records, and according to our experiments, they lead to encouraging results. The best result is yielded by gradient tree boosting, at 93.7percent recall, 79.7percent precision and 85.7percent F1 score when using stemming. We can show that simple preprocessing techniques and parameter tuning can lead to high recall (which we aim for in screening patient records) with appropriate precision for this task.
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| 3. |
- Ehrentraut, Claudia, et al.
(författare)
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Detection of Hospital Acquired Infections in sparse and noisy Swedish patient records : A machine learning approach using Naïve Bayes, Support Vector Machines and C4.5
- 2012
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Ingår i: Proceedings of the Sixth Workshop on Analytics for Noisy Unstructured Text Data.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Hospital Acquired Infections (HAI) pose a significant risk on patients’ health while their surveillance is an additional work load for hospital medical staff and hospital management. Our overall aim is to build a system which reliably retrieves all patient records which potentially include HAI, to reduce the burden of manually checking patient records by the hospital staff. In other words, we emphasize recall when detecting HAI (aiming at 100%) with the highest precision possible. The present study is of experimental nature, focusing on the application of Naïve Bayes (NB), Support Vector Machines (SVM) and a C4.5 Decision Tree to the problem and the evaluation of the efficiency of this approach. The three classifiers showed an overall similar performance. SVM yielded the best recall value, 89.8%, for records that contain HAI. We present a machine learning approach as an alternative to rule-based systems which are more common in this task. The classifiers were applied on a small and noisy dataset, generating results which pinpoint the potentials of using learning algorithms for detecting HAI. Further research will have to focus on optimizing the performance of the classifiers and to test them on larger datasets.
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| 4. |
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| 5. |
- Karlsson Valik, John, et al.
(författare)
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Validation of automated sepsis surveillance based on the Sepsis-3 clinical criteria against physician record review in a general hospital population : observational study using electronic health records data
- 2020
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Ingår i: BMJ Quality and Safety. - : BMJ Publishing Group Ltd. - 2044-5415 .- 2044-5423. ; 29:9, s. 735-745
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Forskningsöversikt (refereegranskat)abstract
- Background: Surveillance of sepsis incidence is important for directing resources and evaluating quality-of-care interventions. The aim was to develop and validate a fully-automated Sepsis-3 based surveillance system in non-intensive care wards using electronic health record (EHR) data, and demonstrate utility by determining the burden of hospital-onset sepsis and variations between wards.Methods: A rule-based algorithm was developed using EHR data from a cohort of all adult patients admitted at an academic centre between July 2012 and December 2013. Time in intensive care units was censored. To validate algorithm performance, a stratified random sample of 1000 hospital admissions (674 with and 326 without suspected infection) was classified according to the Sepsis-3 clinical criteria (suspected infection defined as having any culture taken and at least two doses of antimicrobials administered, and an increase in Sequential Organ Failure Assessment (SOFA) score by >2 points) and the likelihood of infection by physician medical record review.Results: In total 82 653 hospital admissions were included. The Sepsis-3 clinical criteria determined by physician review were met in 343 of 1000 episodes. Among them, 313 (91%) had possible, probable or definite infection. Based on this reference, the algorithm achieved sensitivity 0.887 (95% CI: 0.799 to 0.964), specificity 0.985 (95% CI: 0.978 to 0.991), positive predictive value 0.881 (95% CI: 0.833 to 0.926) and negative predictive value 0.986 (95% CI: 0.973 to 0.996). When applied to the total cohort taking into account the sampling proportions of those with and without suspected infection, the algorithm identified 8599 (10.4%) sepsis episodes. The burden of hospital-onset sepsis (>48 hour after admission) and related in-hospital mortality varied between wards.Conclusions: A fully-automated Sepsis-3 based surveillance algorithm using EHR data performed well compared with physician medical record review in non-intensive care wards, and exposed variations in hospital-onset sepsis incidence between wards.
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| 6. |
- Naucler, Pontus, et al.
(författare)
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HAI-Proactive : Development of an Automated Surveillance System for Healthcare-Associated Infections in Sweden
- 2020
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Ingår i: Infection control and hospital epidemiology. - : Cambridge University Press. - 0899-823X .- 1559-6834. ; 41, s. S39-S39
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Background: Healthcare-associated infection (HAI) surveillance is essential for most infection prevention programs and continuous epidemiological data can be used to inform healthcare personal, allocate resources, and evaluate interventions to prevent HAIs. Many HAI surveillance systems today are based on time-consuming and resource-intensive manual reviews of patient records. The objective of HAI-proactive, a Swedish triple-helix innovation project, is to develop and implement a fully automated HAI surveillance system based on electronic health record data. Furthermore, the project aims to develop machine-learning–based screening algorithms for early prediction of HAI at the individual patient level. Methods: The project is performed with support from Sweden’s Innovation Agency in collaboration among academic, health, and industry partners. Development of rule-based and machine-learning algorithms is performed within a research database, which consists of all electronic health record data from patients admitted to the Karolinska University Hospital. Natural language processing is used for processing free-text medical notes. To validate algorithm performance, manual annotation was performed based on international HAI definitions from the European Center for Disease Prevention and Control, Centers for Disease Control and Prevention, and Sepsis-3 criteria. Currently, the project is building a platform for real-time data access to implement the algorithms within Region Stockholm. Results: The project has developed a rule-based surveillance algorithm for sepsis that continuously monitors patients admitted to the hospital, with a sensitivity of 0.89 (95% CI, 0.85–0.93), a specificity of 0.99 (0.98–0.99), a positive predictive value of 0.88 (0.83–0.93), and a negative predictive value of 0.99 (0.98–0.99). The healthcare-associated urinary tract infection surveillance algorithm, which is based on free-text analysis and negations to define symptoms, had a sensitivity of 0.73 (0.66–0.80) and a positive predictive value of 0.68 (0.61–0.75). The sensitivity and positive predictive value of an algorithm based on significant bacterial growth in urine culture only was 0.99 (0.97–1.00) and 0.39 (0.34–0.44), respectively. The surveillance system detected differences in incidences between hospital wards and over time. Development of surveillance algorithms for pneumonia, catheter-related infections and Clostridioides difficile infections, as well as machine-learning–based models for early prediction, is ongoing. We intend to present results from all algorithms. Conclusions: With access to electronic health record data, we have shown that it is feasible to develop a fully automated HAI surveillance system based on algorithms using both structured data and free text for the main healthcare-associated infections.Funding: Sweden’s Innovation Agency and Stockholm County CouncilDisclosures: None
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| 7. |
- Tanushi, Hideyuki, et al.
(författare)
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Calculating Prevalence of Comorbidity and Comorbidity Combinations with Diabetes in Hospital Care in Sweden Using a Health Care Record Database
- 2011
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Konferensbidrag (refereegranskat)abstract
- Access to reliable data from electronic health records is of high importance in several key areas in patient care, biomedical research, and education. However, many of the clinical entities are negated in the patient record text. Detecting what is a negation and what is not is therefore a key to high quality text mining. In this study we used the NegEx system adapted for Swedish to investigate negated clinical entities. We applied the system to a subset of free-text entries under a heading containing the word ‘assessment’ from the Stockholm EPR corpus, containing in total 23,171,559 tokens. Specifically, the explored entities were the SNOMED CT terms having the semantic categories ‘finding’ or ‘disorder’. The study showed that the proportion of negated clinical entities was around 9%. The results thus support that negations are abundant in clinical text and hence negation detection is vital for high quality text mining in the medical domain.
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| 8. |
- Tanushi, Hideyuki, et al.
(författare)
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Detection of Healthcare-Associated Urinary Tract Infection in Swedish Electronic Health Records
- 2014
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Ingår i: Innovation in Medicine and Healthcare. - : IOS Press. - 9781614994732 - 9781614994749 ; , s. 330-339
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Konferensbidrag (refereegranskat)abstract
- The prevalence of healthcare-associated infections (HAI) stresses the need for automatic surveillance in order to follow the effect of preventive measures. A number of detection systems have been set up for several languages, but none is known for Swedish hospitals. We plan a series of infection type specific programs for detection of HAI in electronic health records at a Swedish university hospital. Also, we aim at detecting HAI for patients entering hospital with HAI from previous care, a task that is not often addressed. This first study aims at surveillance of healthcare-associated urinary tract infections. The created rule-based system depends on acquiring the essential clinical information, and a combination of data and text mining is used. The wide range of diverse clinics with different traditions of documentation poses difficulties for detection. Results from evaluation on 1,867 care episodes from Oncology and Surgery show high precision (0.98), specificity (0.99) and negative predictive value (0.99), but an intermediate recall (0.60). An error analysis of the evaluation is presented and discussed.
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| 9. |
- Tanushi, Hideyuki, et al.
(författare)
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Negation Scope Delimitation in Clinical Text Using Three Approaches : NegEx, PyConTextNLP and SynNeg
- 2013
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Ingår i: Proceedings of the 19th Nordic Conference of Computational Linguistics (NODALIDA 2013). - Linköping : Linköping University Electronic Press. - 9789175195896 ; , s. 387-474
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Konferensbidrag (refereegranskat)abstract
- Negation detection is a key component in clinical information extraction systems, as health record text contains reasonings in which the physician excludes different diagnoses by negating them. Many systems for negation detection rely on negation cues (e.g. not), but only few studies have investigated if the syntactic structure of the sentences can be used for determining the scope of these cues. We have in this paper compared three different systems for negation detection in Swedish clinical text (NegEx, PyConTextNLP and SynNeg), which have different approaches for determining the scope of negation cues. NegEx uses the distance between the cue and the disease, PyConTextNLP relies on a list of conjunctions limiting the scope of a cue, and in SynNeg the boundaries of the sentence units, provided by a syntactic parser, limit the scope of the cues. The three systems produced similar results, detecting negation with an F-score of around 80%, but using a parser had advantages when handling longer, complex sentences or short sentences with contradictory statements.
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| 10. |
- Valik, John Karlsson, et al.
(författare)
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Predicting sepsis onset using a machine learned causal probabilistic network algorithm based on electronic health records data
- 2023
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Sepsis is a leading cause of mortality and early identification improves survival. With increasing digitalization of health care data automated sepsis prediction models hold promise to aid in prompt recognition. Most previous studies have focused on the intensive care unit (ICU) setting. Yet only a small proportion of sepsis develops in the ICU and there is an apparent clinical benefit to identify patients earlier in the disease trajectory. In this cohort of 82,852 hospital admissions and 8038 sepsis episodes classified according to the Sepsis-3 criteria, we demonstrate that a machine learned score can predict sepsis onset within 48 h using sparse routine electronic health record data outside the ICU. Our score was based on a causal probabilistic network model-SepsisFinder-which has similarities with clinical reasoning. A prediction was generated hourly on all admissions, providing a new variable was registered. Compared to the National Early Warning Score (NEWS2), which is an established method to identify sepsis, the SepsisFinder triggered earlier and had a higher area under receiver operating characteristic curve (AUROC) (0.950 vs. 0.872), as well as area under precision-recall curve (APR) (0.189 vs. 0.149). A machine learning comparator based on a gradient-boosting decision tree model had similar AUROC (0.949) and higher APR (0.239) than SepsisFinder but triggered later than both NEWS2 and SepsisFinder. The precision of SepsisFinder increased if screening was restricted to the earlier admission period and in episodes with bloodstream infection. Furthermore, the SepsisFinder signaled median 5.5 h prior to antibiotic administration. Identifying a high-risk population with this method could be used to tailor clinical interventions and improve patient care.
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