Prokaryote growth temperature prediction with machine learning

Archaea and bacteria can be divided into four groups based on their growth temperature adaptation: mesophiles, thermophiles, hyperthermophiles, and psychrophiles. The thermostability of proteins is a sum of multiple different physical forces such as van der Waals interactions, chemical polarity, and ionic interactions. Genes causing the adaptation have not been identified and this thesis aims to identify temperature adaptation linked genes and predict temperature adaptation based on the absence or presence of genes. A dataset of 4361 genes from 711 prokaryotes was analyzed with four different machine learning algorithms: neural network, random forest, gradient boosting machine, and logistic regression. Logistic regression was chosen to be an explanatory and predictive model based on micro averaged AUC and Occam’s razor principle. Logistic regression was able to predict temperature adaptation with good performance. Machine learning is a powerful predictor for temperature adaptation and less than 200 genes were needed for the prediction of each adaptation. This technique can be used to predict the adaptation of uncultivated prokaryotes. However, the statistical importance of genes connected to temperature adaptation was not verified and this thesis did not provide much additional support for previously proposed temperature adaptation linked genes

MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation

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

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.</p