| 1. |
- Pilotto, Francesca, et al.
(författare)
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Biodiversity shifts : data-driven insights from modern ecology, archaeology, and quaternary sciences
- 2024
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- To understand the implications of past changes in climate, landscape and human activity on contemporary biodiversity patterns, data from modern and palaeoecological studies must be connected. The Strategic Environmental Archaeology Database (SEAD) provides access to big data from archaeology and Quaternary science and is an enormous potential resource for investigating past changes in biodiversity. By linking SEAD to SBDI, past species distributions can be analysed for their implications for landscape and climate change. Recent macroecological research using SEAD/ SBDI illustrates trends in Late Holocene anthropogenic landscape change in north-western Europe. Over the past few thousand years, humans have impacted insect biodiversity as much as climate change did after the last Ice Age. This demonstrates that data from archaeology, and the consequences of human activity, are essential for fulfilling the promi- se of using data driven ecology for guiding future conservation practices in response to climate change.
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| 2. |
- Verberk, Janneke D. M., et al.
(författare)
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The augmented value of using clinical notes in semi-automated surveillance of deep surgical site infections after colorectal surgery
- 2023
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Ingår i: Antimicrobial Resistance and Infection Control. - 2047-2994. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundIn patients who underwent colorectal surgery, an existing semi-automated surveillance algorithm based on structured data achieves high sensitivity in detecting deep surgical site infections (SSI), however, generates a significant number of false positives. The inclusion of unstructured, clinical narratives to the algorithm may decrease the number of patients requiring manual chart review. The aim of this study was to investigate the performance of this semi-automated surveillance algorithm augmented with a natural language processing (NLP) component to improve positive predictive value (PPV) and thus workload reduction (WR).MethodsRetrospective, observational cohort study in patients who underwent colorectal surgery from January 1, 2015, through September 30, 2020. NLP was used to detect keyword counts in clinical notes. Several NLP-algorithms were developed with different count input types and classifiers, and added as component to the original semi-automated algorithm. Traditional manual surveillance was compared with the NLP-augmented surveillance algorithms and sensitivity, specificity, PPV and WR were calculated.ResultsFrom the NLP-augmented models, the decision tree models with discretized counts or binary counts had the best performance (sensitivity 95.1% (95%CI 83.5-99.4%), WR 60.9%) and improved PPV and WR by only 2.6% and 3.6%, respectively, compared to the original algorithm.ConclusionsThe addition of an NLP component to the existing algorithm had modest effect on WR (decrease of 1.4-12.5%), at the cost of sensitivity. For future implementation it will be a trade-off between optimal case-finding techniques versus practical considerations such as acceptability and availability of resources.
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| 3. |
- Weegar, Rebecka, 1982-
(författare)
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Mining Clinical Text in Cancer Care
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Health care and clinical practice generate large amounts of text detailing symptoms, test results, diagnoses, treatments, and outcomes for patients. This clinical text, documented in health records, is a potential source of knowledge and an underused resource for improved health care. The focus of this work has been text mining of clinical text in the domain of cancer care, with the aim to develop and evaluate methods for extracting relevant information from such texts. Two different types of clinical documentation have been included: clinical notes from electronic health records in Swedish and Norwegian pathology reports.Free text, and clinical text in particular, is considered as a kind of unstructured information, which is difficult to process automatically. Therefore, information extraction can be applied to create a more structured representation of a text, making its content more accessible for machine learning and statistics. To this end, this thesis describes the development of an efficient and accurate tool for information extraction for pathology reports.Another application for clinical text mining is risk prediction and diagnosis prediction. The goal for such prediction is to create a machine learning model capable of identifying patients at risk of a specific disease or some other adverse outcome. The motivation for cancer diagnosis prediction is that an early diagnosis can be beneficial for the outcome of treatment. Here, a disease prediction model was developed and evaluated for prediction of cervical cancer. To create this model, health records of patients diagnosed with cervical cancer were processed in two steps. First, clinical events were extracted from free text clinical notes through the use of named entity recognition. The extracted events were next combined with other event types, such as diagnosis codes and drug codes from the same health records. Finally, machine learning models were trained for predicting cervical cancer, and evaluation showed that events extracted from the free text records were the most informative event type for the diagnosis prediction.
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| 4. |
- Weegar, Rebecka, 1982-, et al.
(författare)
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Reducing Workload in Short Answer Grading Using Machine Learning
- 2023
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Ingår i: International Journal of Artificial Intelligence in Education. - : Springer Science and Business Media LLC. - 1560-4292 .- 1560-4306.
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Tidskriftsartikel (refereegranskat)abstract
- Machine learning methods can be used to reduce the manual workload in exam grading, making it possible for teachers to spend more time on other tasks. However, when it comes to grading exams, fully eliminating manual work is not yet possible even with very accurate automated grading, as any grading mistakes could have significant consequences for the students. Here, the evaluation of an automated grading approach is therefore extended from measuring workload in relation to the accuracy of automated grading, to also measuring the overall workload required to correctly grade a full exam, with and without the support of machine learning. The evaluation was performed during an introductory computer science course with over 400 students. The exam consisted of 64 questions with relatively short answers and a two-step approach for automated grading was applied. First, a subset of answers to the exam questions was manually graded and next used as training data for machine learning models classifying the remaining answers. A number of different strategies for how to select which answers to include in the training data were evaluated. The time spent on different grading actions was measured along with the reduction of effort using clustering of answers and automated scoring. Compared to fully manual grading, the overall reduction of workload was substantial-between 64% and 74%-even with a complete manual review of all classifier output to ensure a fair grading.
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