Hospital readmission prediction with long clinical notes

Electronic health records (EHR) data is captured across many healthcare institutions, resulting in large amounts of diverse information that can be analysed for diagnosis, prognosis, treatment and prevention of disease. One type of data captured by EHRs are clinical notes, which are unstructured data written in natural language. We can leverage Natural Language Processing (NLP) to build machine learning (ML) models to gain understanding from clinical notes that will enable us to predict clinical outcomes. ClinicalBERT is a pre-trained Transformer based model which is trained on clinical text and is able to predict 30-day hospital readmission from clinical notes. Although the performance is good, it suffers from a limitation on the size of the text sequence that is fed as input to the model. Models using longer sequences have been shown to perform better on different ML tasks, even with clinical text. In this work, a ML model called Longformer which pre-trained then fine-tuned on clinical text and is able to learn from longer sequences than previous models is evaluated. Performance is evaluated against the Deep Averaging Network (DAN) and Long short-term memory (LSTM) baselines and previous state-of-the-art models in terms of Area under the receiver operating characteristic curve (AUROC), Area under the precision-recall curve (AUPRC) and Recall at precision of 70% (RP70). Longformer is able to best ClinicalBERT on two performance metrics, however it is not able to surpass one of the baselines in any of the metrics. Training the model on early notes did not result in substantial difference when compared to training on discharge summaries. Our analysis shows that the model suffers from out-of-vocabulary words, as many biomedical concepts are missing from the original pre-training corpus

Implementing system-wide risk stratification approaches: a review of critical success and failure factors

An Evidence Check rapid review brokered by the Sax Institute for the NSW Agency for Clinical Innovatio

Contributions from computational intelligence to healthcare data processing

80 p.The increasing ability to gather, store and process health care information, through the electronic health records and improved communication methods opens the door for new applications intended to improve health care in many different ways. Crucial to this evolution is the development of new computational intelligence tools, related to machine learning and statistics. In this thesis we have dealt with two case studies involving health data. The first is the monitoring of children with respiratory diseases in the pediatric intensive care unit of a hospital. The alarm detection is stated as a classification problem predicting the triage selected by the nurse or medical doctor. The second is the prediction of readmissions leading to hospitalization in an emergency department of a hospital. Both problems have great impact in economic and personal well being. We have tackled them with a rigorous methodological approach, obtaining results that may lead to a real life implementation. We have taken special care in the treatment of the data imbalance. Finally we make propositions to bring these techniques to the clinical environment

Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup

Mixup is a highly successful technique to improve generalization of neural networks by augmenting the training data with combinations of random pairs. Selective mixup is a family of methods that apply mixup to specific pairs, e.g. only combining examples across classes or domains. These methods have claimed remarkable improvements on benchmarks with distribution shifts, but their mechanisms and limitations remain poorly understood. We examine an overlooked aspect of selective mixup that explains its success in a completely new light. We find that the non-random selection of pairs affects the training distribution and improve generalization by means completely unrelated to the mixing. For example in binary classification, mixup across classes implicitly resamples the data for a uniform class distribution - a classical solution to label shift. We show empirically that this implicit resampling explains much of the improvements in prior work. Theoretically, these results rely on a regression toward the mean, an accidental property that we identify in several datasets. We have found a new equivalence between two successful methods: selective mixup and resampling. We identify limits of the former, confirm the effectiveness of the latter, and find better combinations of their respective benefits

Artificial intelligence in healthcare delivery: Prospects and pitfalls

This review provides a comprehensive examination of the integration of Artificial Intelligence (AI) into healthcare, focusing on its transformative implications and challenges. Utilising a systematic search strategy across electronic databases such as PubMed, Scopus, Embase, and ScienceDirect, relevant peer-reviewed articles published in English between January 2010 till date were identified. Findings reveal AI's significant impact on healthcare delivery, including its role in enhancing diagnostic precision, enabling treatment personalisation, facilitating predictive analytics, automating tasks, and driving robotics. AI algorithms demonstrate high accuracy in analysing medical images for disease diagnosis and enable the creation of tailored treatment plans based on patient data analysis. Predictive analytics identify high-risk patients for proactive interventions, while AI-powered tools streamline workflows, improving efficiency and patient experience. Additionally, AI-driven robotics automate tasks and enhance care delivery, particularly in rehabilitation and surgery. However, challenges such as data quality, interpretability, bias, and regulatory frameworks must be addressed for responsible AI implementation. Recommendations emphasise the need for robust ethical and legal frameworks, human-AI collaboration, safety validation, education, and comprehensive regulation to ensure the ethical and effective integration of AI in healthcare. This review provides valuable insights into AI's transformative potential in healthcare while advocating for responsible implementation to ensure patient safety and efficacy

Functional Cognition Assessments: Implications for Skilled Nursing Facilities

Functional cognition is a critical topic in occupational therapy practice. Functional cognition can be considered a client factor that impacts one’s occupations, performance skills, and patterns. As Giles (2017) stated: “Functional cognition is known as the interaction of cognitive skills and self-care, and community living skills” (p. 1). The purpose of this knowledge translation doctoral project was to educate occupational therapy practitioners and students on the importance of functional cognition assessments and interventions for older adults in skilled nursing settings. Three knowledge translation projects were developed with an emphasis on functional cognition assessments and interventions for older adults in the skilled nursing setting. The first project was a presentation designed for members of the South Dakota Occupational Therapy Association. The second project was an educational module for entry-level doctoral occupational therapy graduate students. The final project was a manuscript proposed for an American Occupational Therapy Association magazine or newsletter. These projects summarized evidence and proposed recommendations regarding the important role of occupational therapy in addressing functional cognition necessary to support safety, performance, and level of assistance needed for completing activities of daily living

How 5G wireless (and concomitant technologies) will revolutionize healthcare?

The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution