Using machine learning to predict subsequent events after EMS non-conveyance decisions

Background

Predictors of subsequent events after Emergency Medical Services (EMS) non-conveyance decisions are still unclear, though patient safety is the priority in prehospital emergency care. The aim of this study was to find out whether machine learning can be used in this context and to identify the predictors of subsequent events based on narrative texts of electronic patient care records (ePCR).

Methods

This was a prospective cohort study of EMS patients in Finland. The data was collected from three different regions between June 1 and November 30, 2018. Machine learning, in form of text classification, and manual evaluation were used to predict subsequent events from the clinical notes after a non-conveyance mission.

Results

FastText-model (AUC 0.654) performed best in prediction of subsequent events after EMS non-conveyance missions (n = 11,846). The model and manual analyses showed that many of the subsequent events were planned before, EMS guided the patients to visit primary health care facilities or ED next or following days after non-conveyance. The most frequent signs and symptoms as subsequent event predictors were musculoskeletal-, infection-related and non-specific complaints. 1 in 5 the EMS documentation was inadequate and many of these led to a subsequent event.

Conclusion

Machine learning can be used to predict subsequent events after EMS non-conveyance missions. From the patient safety perspective, it is notable that subsequent event does not necessarily mean that patient safety is compromised. There were a number of subsequent visits to primary health care or EDs, which were planned before by EMS. This demonstrates the appropriate use of limited resources to avoid unnecessary conveyance to the ED. However, further studies are needed without planned subsequent events to find out the harmful subsequent events, where EMS non-conveyance puts patient safety at risk.

Text Classification Model Explainability for Keyword Extraction - Towards Keyword-Based Summarization of Nursing Care Episodes

Tools to automate the summarization of nursing entries in electronic health records (EHR) have the potential to support healthcare professionals to obtain a rapid overview of a patient's situation when time is limited. This study explores a keyword-based text summarization method for the nursing text that is based on machine learning model explainability for text classification models. This study aims to extract keywords and phrases that provide an intuitive overview of the content in multiple nursing entries in EHRs written during individual patients' care episodes. The proposed keyword extraction method is used to generate keyword summaries from 40 patients' care episodes and its performance is compared to a baseline method based on word embeddings combined with the PageRank method. The two methods were assessed with manual evaluation by three domain experts. The results indicate that it is possible to generate representative keyword summaries from nursing entries in EHRs and our method outperformed the baseline method.

Augmenting Soft Tissue Contrast Using Edge-Enhancing Phase-Imaging Techniques in X-Ray Microtomography

Conventional X-ray imaging is based on the attenuation of X-rays and the technique provides sufficient contrast when the difference between attenuation coefficients of neighboring structures is sufficient. A promising imaging possibility on a μCT is the use of phase information of an X-ray beam to generate an image of the sample. This is known as phase-contrast imaging. Propagation-based phase imaging sets the least amount of requirements on the imaging setup - lateral coherence for the X-ray source and a movable detector and source. The Zeiss Xradia MicroXCT-400 at our laboratory provides this possibility. Therefore, the phase-contrast imaging protocol, which provides an edge-enhancement effect, on the μCT device was optimized using thin polylactic acid fibers in order to enhance the visibility of low density samples. The optimization consisted of source and detector distance variation measurements. To demonstrate the contrast enhancement results, the optimization was applied to two types of collagen samples embedded in air, ethanol, and water.The results showed enhanced contrast for the edge-enhanced phase-contrast images compared to absorption images. Most importantly, the results indicated that the source does not need to placed at the negative limit to obtain useful phase information. Additionally, the visibility increases with increasing sample-to-detector distance. Finally, significantly enhanced contrast was obtained for the collagen sample embedded in water using phase-imaging techniques. The technique is limited due to the focal spot size and voltage of the X-ray source. The phase-imaging technique has the possibility to enhance contrast of low density samples and to reveal structures that cannot be seen using other imaging techniques.