| 1. |
- Ehrentraut, Claudia, et al.
(author)
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Detecting Healthcare-Associated Infections in Electronic Health Records : Evaluation of Machine Learning and Preprocessing Techniques
- 2014
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In: Proceedings of the 6th International Symposium on Semantic Mining in Biomedicine (SMBM 2014). - : University of Aveiro. ; , s. 3-10
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Conference paper (peer-reviewed)abstract
- Healthcare-associated infections (HAI) are in- fections that patients acquire in the course of medical treatment. Being a severe pub- lic health problem, detecting and monitoring HAI in healthcare documentation is an impor- tant topic to address. Research on automated systems has increased over the past years, but performance is yet to be enhanced. The dataset in this study consists of 214 records obtained from a Point-Prevalence Survey. The records are manually classified into HAI and NoHAI records. Nine different preprocess- ing steps are carried out on the data. Two learning algorithms, Random Forest (RF) and Support Vector Machines (SVM), are applied to the data. The aim is to determine which of the two algorithms is more applicable to the task and if preprocessing methods will affect the performance. RF obtains the best performance results, yielding an F1 -score of 85% and AUC of 0.85 when lemmatisation is used as a preprocessing technique. Irrespec- tive of which preprocessing method is used, RF yields higher recall values than SVM, with a statistically significant difference for all but one preprocessing method. Regarding each classifier separately, the choice of preprocess- ing method led to no statistically significant improvement in performance results.
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| 2. |
- Ehrentraut, Claudia, et al.
(author)
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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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In: Health Informatics Journal. - : SAGE Publications. - 1460-4582 .- 1741-2811. ; 24:1, s. 24-42
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Journal article (peer-reviewed)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.
(author)
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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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In: Proceedings of the Sixth Workshop on Analytics for Noisy Unstructured Text Data.
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Conference paper (other academic/artistic)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. |
- Ehrentraut, Claudia, et al.
(author)
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Exploration of known and unknown early symptoms of cervical cancer and development of a symptom spectrum : Outline of a data and text mining based approach
- 2015
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In: Proceedings of the CAiSE-2015 Industry Track. ; , s. 34-44
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Conference paper (peer-reviewed)abstract
- This position paper lays up the structure of some experiments to detect early symptoms of cervical cancer. We are using a large corpora of electronic patient records texts in Swedish from Karolinska University Hosptital from the years 2009-2010, where we extracted in total 1,660 patients with the diagnosis code C53. We used a Named Entity Recogniser called Clinical Entity Finder to detect the diagnosis and symptoms expressed in these clinical texts containing in total 2,988,118 words. We found 28,218 symptoms and diagnoses on these 1,660 patients. We present some initial findings, and discuss them and propose a set of experiments to find possible early symptoms or at least a spectrum or finger prints for early symptoms of cervical cancer.
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| 6. |
- Ehrentraut, Claudia, et al.
(author)
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Text Analysis to support structuring and modelling a public policy problem : Outline of an algorithm to extract inferences from textual data
- 2014
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In: DSV writers hut 2014. - Stockholm : Department of Computer and Systems Sciences, Stockholm University. - 9789163774577
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Conference paper (other academic/artistic)abstract
- Policy making situations are real-world problems that exhibit complexity in that they are composed of many interrelated problems and issues. To be effective, policies must holistically address the complexity of the situation rather than propose solutions to single problems. Formulating and understanding the situation and its complex dynamics, therefore, is a key to finding holistic solutions. Analysis of text based information on the policy problem, using Natural Language Processing (NLP) and Text analysis techniques, can support modelling of public policy problem situations in a more objective way based on domain experts’ knowledge and scientific evidence. The objective behind this study is to support modelling of public policy problem situations, using text analysis of verbal descriptions of the problem. We propose a formal methodology for analysis of qualitative data from multiple information sources on a policy problem to construct a causal diagram of the problem. The analysis process aims at identifying key variables, linking them by cause-effect relationships and mapping that structure into a graphical representation that is adequate for designing action alternatives, i.e., policy options. This study describes the outline of an algorithm used to automate the initial step of a larger methodological approach, which is so far done manually. In this initial step, inferences about key variables and their interrelationships are extracted from textual data to support a better problem structuring. A small prototype for this step is also presented.
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