Enhancing Bacterial Infection Prediction in Critically Ill Patients by Integrating Clinical Text (2023)

Abstract: Bacterial infection (BI) is an important clinical condition and is related to many diseases that are difficult to treat. Early prediction of BI can lead to better treatment and appropriate use of antimicrobial medications. In this paper, we study a variety of NLP models to predict BI for critically ill patients and compare them with a strong baseline based on clinical measurements. We find that choosing the proper text-based model to combine with measurements can lead to substantial improvements. Our results show the value of clinical text in predicting and managing BI. We also find that the NLP model developed using patients with BI can be transferred to the more general patient cohort for patient risk prediction.

## introduction data-driven ai models for healthcare have much potential to facilitate clinical care, promote healthcare efficiency, and support medical research @xcite . an important domain of medicine that could benefit from ai is infectious disease, where ai can help better understand infections so that we can design more effective approaches to monitor, diagnose, and treat infections @xcite . among the different types of infections, bacterial infection (bi) is one of the most common and is estimated to be associated with more than 13 million deaths in 2019 alone @xcite . previous works have studied various types of ai models to predict the occurrence of bi-related diseases using data from electronic health records (ehr), especially sepsis @xcite . meanwhile, the prediction of bi in general is less studied, whereby structured measurements were used predominantly to develop predictive models @xcite . the value of clinical text in bi prediction remains unclear. in this study, we explore the usefulness of nlp for infection-related prediction task by focusing on bi prediction in critically ill patients. we follow an existing study @xcite on bi prediction that relies on a range of clinical measurements as features, and we compare it with common nlp models that rely solely on routinely collected clinical text (illustrated in figure 1 ). we then use the best performing text encoder to develop multimodal fusion models for bi prediction, which obtains the state-of-the-art result. finally, we study the applicability of nlp models for mortality prediction in different patient cohorts, showing that the model trained using patients with bi is more robust to data shift. ## related work many studies have developed machine learning models to predict diseases caused by bacterial infections, with urinary tract infection @xcite and sepsis @xcite being the two most prominent examples. early identification of these diseases is helpful, and sometimes essential, for clinicians to arrange lifesaving treatments. these studies typically use clinical measurements as features for model development and may sometimes derive features from text as a supplement @xcite . previous work studying bi prediction used clinical measurements @xcite , and this was recently extended in a multicenter study @xcite . although text has been applied to predict specific diseases or organisms @xcite , the contribution of text to bi prediction in general remains understudied. many previous work shows nlp models are effective for various clinical predictive tasks @xcite . typical early prediction targets include patient mortality, length of stay in the hospital, readmission, diagnosis groups, or specific diseases. multimodal fusion of different modalities in the ehr also shows promise in improving classification performance, such as combining clinical notes foot_0 and measurements @xcite . while previous works tend to focus on a specific type of text encoder or fusion mechanism to compare with unimodal modeling, the impact of varying these configurations on performance is not well understood. the transferability of ai or ml models for clinical care is an important topic since many factors in healthcare can cause data shift @xcite . applying models across different patient cohorts is also important in low-resource patient groups and to ensure fairness @xcite . for example, a recent study shows that model trained in adult patients can be successfully transferred to pediatric patients @xcite . more studies are needed to understand the generalisability of models in healthcare. ## conclusion clinical text can help predict bi in critically ill patients and nlp models trained using bi patients can be transported to those without bi. nlp and multimodal models can develop better data-driven strategies to stratify the risk of bi in patients, which can be compared with prompt-based large language models (llms) in future work. clinical co-development will be pursued to ensure that the developed models are optimised for clinical workflow, capable of refining antibiotic therapy in the absence of test results, and have the potential to enhance antimicrobial stewardship, thereby miti-gating antimicrobial resistance. in the future, we would like to investigate how text can help improve bi treatment, such as antimicrobial stewardship and predict potential antimicrobial resistance.