Browsing by Author "Peltonen, Laura Maria"

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Clustering Nursing Sentences-Comparing Three Sentence Embedding Methods
(2022-05-25) Moen, Hans; Suhonen, Henry; Salanterä, Sanna; Salakoski, Tapio; Peltonen, Laura Maria
A4 Artikkeli konferenssijulkaisussa
In health sciences, high-quality text embeddings may augment qualitative data analysis of large amounts of text by enabling, e.g., searching and clustering of health information. This study aimed to evaluate three different sentence-level embedding methods in clustering sentences in nursing narratives from individual patients' hospital care episodes. Two of these embeddings are generated from language models based on the BERT framework, and the third on the Sent2Vec method. These embedding methods were used to cluster sentences from 20 patient care episodes and the results were manually evaluated. Findings suggest that the best clusters were produced by the embeddings from a BERT model fine-tuned for the proxy task of predicting subject headings for nursing text.
Electronic Health Records as Information Source in Assessment of the Effectiveness of Delivered Care - A Pilot Study
(2023-05-18) Von Gerich, Hanna; Moen, Hans; Peltonen, Laura Maria
A4 Artikkeli konferenssijulkaisussa
Effectiveness is a key element of high quality health services. The aim of this pilot study was to explore the potential of electronic health records (EHR) as an information source for assessing the effectiveness of nursing care by investigating the appearance of nursing processes in the documentation of care. Deductive and inductive content analysis were used in a manual annotation of ten patients' EHRs. The analysis resulted in the identification of 229 documented nursing processes. The results indicate that EHRs can be used in decision support systems for assessing effectiveness of nursing care, however, future work is needed to verify these findings in a larger data set and extend to other dimensions related to care quality.
Identifying nursing sensitive indicators from electronic health records in acute cardiac care―Towards intelligent automated assessment of care quality
(2022-11) von Gerich, Hanna; Moen, Hans; Peltonen, Laura Maria
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Aim: The aim of this study is to explore the potential of using electronic health records for assessment of nursing care quality through nursing-sensitive indicators in acute cardiac care. Background: Nursing care quality is a multifaceted phenomenon, making a holistic assessment of it difficult. Quality assessment systems in acute cardiac care units could benefit from big data-based solutions that automatically extract and help interpret data from electronic health records. Methods: This is a deductive descriptive study that followed the theory of value-added analysis. A random sample from electronic health records of 230 patients was analysed for selected indicators. The data included documentation in structured and free-text format. Results: One thousand six hundred seventy-six expressions were extracted and divided into (1) established and (2) unestablished expressions, providing positive, neutral and negative descriptions related to care quality. Conclusions: Electronic health records provide a potential source of information for information systems to support assessment of care quality. More research is warranted to develop, test and evaluate the effectiveness of such tools in practice. Implications for Nursing Management: Knowledge-based health care management would benefit from the development and implementation of advanced information systems, which use continuously generated already available real-time big data for improved data access and interpretation to better support nursing management in quality assessment.
VMS: Interactive Visualization to Support the Sensemaking and Selection of Predictive Models
(2024-03-18) He, Chen; Raj, Vishnu; Moen, Hans; Gröhn, Tommi; Wang, Chen; Peltonen, Laura Maria; Koivusalo, Saila; Marttinen, Pekka; Jacucci, Giulio
A4 Artikkeli konferenssijulkaisussa
To compare and select machine learning models, relying on performance measures alone may not always be sufficient. This is particularly the case where different subsets, features, and predicted results may vary in importance relative to the task at hand. Explanation and visualization techniques are required to support model sensemaking and informed decision-making. However, a review shows that existing systems are mostly designed for model developers and not evaluated with target users in their effectiveness. To address this issue, this research proposes an interactive visualization, VMS (Visualization for Model Sensemaking and Selection), for users of the model to compare and select predictive models. VMS integrates performance-, instance-, and feature-level analysis to evaluate models from multiple angles. Particularly, a feature view integrating the value and contribution of hundreds of features supports model comparison on local and global scales. We exemplified VMS for comparing models predicting patients' hospital length of stay through time-series health records and evaluated the prototype with 16 participants from the medical field. Results reveal evidence that VMS supports users to rationalize models in multiple ways and enables users to select the optimal models with a small sample size. User feedback suggests future directions on incorporating domain knowledge in model training, such as for different patient groups considering different sets of features as important.