Detection of Bleeding Events in Electronic Health Record Notes Using Convolutional Neural Network Models Enhanced With Recurrent Neural Network Autoencoders: Deep Learning Approach

BACKGROUND: Bleeding events are common and critical and may cause significant morbidity and mortality. High incidences of bleeding events are associated with cardiovascular disease in patients on anticoagulant therapy. Prompt and accurate detection of bleeding events is essential to prevent serious consequences. As bleeding events are often described in clinical notes, automatic detection of bleeding events from electronic health record (EHR) notes may improve drug-safety surveillance and pharmacovigilance. OBJECTIVE: We aimed to develop a natural language processing (NLP) system to automatically classify whether an EHR note sentence contains a bleeding event. METHODS: We expert annotated 878 EHR notes (76,577 sentences and 562,630 word-tokens) to identify bleeding events at the sentence level. This annotated corpus was used to train and validate our NLP systems. We developed an innovative hybrid convolutional neural network (CNN) and long short-term memory (LSTM) autoencoder (HCLA) model that integrates a CNN architecture with a bidirectional LSTM (BiLSTM) autoencoder model to leverage large unlabeled EHR data. RESULTS: HCLA achieved the best area under the receiver operating characteristic curve (0.957) and F1 score (0.938) to identify whether a sentence contains a bleeding event, thereby surpassing the strong baseline support vector machines and other CNN and autoencoder models. CONCLUSIONS: By incorporating a supervised CNN model and a pretrained unsupervised BiLSTM autoencoder, the HCLA achieved high performance in detecting bleeding events

Predicting Gastrointestinal Adverse Events in Dogs Treated with Chemotherapeutic Medication

Despite significant advances in the treatment of cancer in canine patients, gastrointestinal toxicity still remains a relatively common finding after chemotherapeutic treatment. These adverse events may cause a fundamental reduction in the patient’s quality of life. To improve the well-being of the patients, and to minimize the risk of adverse events following chemotherapy, the mechanisms and reasons behind the development of adverse events have to be understood. There are several possible factors that might affect the risk of developing chemotherapy-induced gastrointestinal toxicity, however, there are currently no standardized methods for reviewing, mapping or measuring these factors within the field of study. The use of questionnaires, in combination with non-invasive biomarkers, could therefore potentially be a stress-free and favourable way of investigating the correlations between chemotherapy and gastrointestinal adverse events as well as a way of predicting which animals that are at risk for gastrointestinal toxicity. A prospective study at University Animal Hospital (UDS) in Uppsala, Sweden, was performed with the ambition of investigating the connection between chemotherapeutic treatment and the development of gastrointestinal toxicity. The main aim of this study was to find possible influential factors leading to the development of gastrointestinal adverse events after chemotherapeutic treatment. The study was divided into two questionnaire-based parts with questions directed to owners of dogs with a cancer diagnosis. The first questionnaire reviewed potential influential factors in the everyday life and diet of the dog which could be related to the development of chemotherapy-induced adverse events. This questionnaire also examined the frequency of gastrointestinal events as well as concurrent illnesses and treatments. The second questionnaire focused on the occurrence and assessment of gastrointestinal toxicity in dogs treated with chemotherapeutic medication based on VCOG-CTCAE (version 2). A total of eight dogs with cancer of different ages, sexes, and breeds were included in the study. According to the owners, 87% (n=7) of the dogs had experienced some form of mild gastrointestinal disturbance without the need for supportive therapies during the last year. Three of the eight canine patients continued with chemotherapeutic treatment and could be assessed through the second questionnaire. In total, 67% (n=2) dogs experienced different grades of gastrointestinal adverse events (loss of appetite, diarrhoea) within three to five weeks after their first chemotherapeutic treatment. The questionnaires show promise to be used in studies with similar aims, possibly in combination with the analysis of biomarkers. However, due to the small study population, the results from this study may not be representative of a larger population. It is not possible to determine whether the gastrointestinal events that were reported in this study were caused by the cytostatic agents or if they had another aetiology. Therefore, further studies must be performed regarding potential influential factors as well as to investigate the actual clinical utility of the questionnaires. Further studies regarding the use of non-invasive biomarkers such as calprotectin and/or gut microbiota may also be of importance to examine the association with the development of gastrointestinal adverse events

Machine Learning for the Early Detection of Acute Episodes in Intensive Care Units

In Intensive Care Units (ICUs), mere seconds might define whether a patient lives or dies. Predictive models capable of detecting acute events in advance may allow for anticipated interventions, which could mitigate the consequences of those events and promote a greater number of lives saved. Several predictive models developed for this purpose have failed to meet the high requirements of ICUs. This might be due to the complexity of anomaly prediction tasks, and the inefficient utilization of ICU data. Moreover, some essential intensive care demands, such as continuous monitoring, are often not considered when developing these solutions, making them unfit to real contexts. This work approaches two topics within the mentioned problem: the relevance of ICU data used to predict acute episodes and the benefits of applying Layered Learning (LL) techniques to counter the complexity of these tasks. The first topic was undertaken through a study on the relevance of information retrieved from physiological signals and clinical data for the early detection of Acute Hypotensive Episodes (AHE) in ICUs. Then, the potentialities of LL were accessed through an in-depth analysis of the applicability of a recently proposed approach on the same topic. Furthermore, different optimization strategies enabled by LL configurations were proposed, including a new approach aimed at false alarm reduction. The results regarding data relevance might contribute to a shift in paradigm in terms of information retrieved for AHE prediction. It was found that most of the information commonly used in the literature might be wrongly perceived as valuable, since only three features related to blood pressure measures presented actual distinctive traits. On another note, the different LL-based strategies developed confirm the versatile possibilities offered by this paradigm. Although these methodologies did not promote significant performance improvements in this specific context, they can be further explored and adapted to other domains.Em Unidades de Cuidados Intensivos (UCIs), meros segundos podem ser o fator determinante entre a vida e a morte de um paciente. Modelos preditivos para a previsão de eventos adversos podem promover intervenções antecipadas, com vista à mitigação das consequências destes eventos, e traduzir-se num maior número de vidas salvas. Múltiplos modelos desenvolvidos para este propósito não corresponderam às exigências das UCIs. Isto pode dever-se à complexidade de tarefas de previsão de anomalias e à ineficiência no uso da informação gerada em UCIs. Além disto, algumas necessidades inerentes à provisão de cuidados intensivos, tais como a monitorização contínua, são muitas vezes ignoradas no desenvolvimento destas soluções, tornando-as desadequadas para contextos reais. Este projeto aborda dois tópicos dentro da problemática introduzida, nomeadamente a relevância da informação usada para prever episódios agudos, e os benefícios de técnicas de Aprendizagem em Camadas (AC) para contrariar a complexidade destas tarefas. Numa primeira fase, foi conduzido um estudo sobre o impacto de diversos sinais fisiológicos e dados clínicos no contexto da previsão de episódios agudos de hipotensão. As potencialidades do paradigma de AC foram avaliadas através da análise de uma abordagem proposta recentemente para o mesmo caso de estudo. Nesta segunda fase, diversas estratégias de otimização compatíveis com configurações em camadas foram desenvolvidas, incluindo um modelo para reduzir falsos alarmes. Os resultados relativos à relevância da informação podem contribuir para uma mudança de paradigma em termos da informação usada para treinar estes modelos. A maior parte da informação poderá estar a ser erroneamente considerada como importante, uma vez que apenas três variáveis, deduzidas dos valores de pressão arterial, foram identificadas como realmente impactantes. Por outro lado, as diferentes estratégias baseadas em AC confirmaram a versatilidade oferecida por este paradigma. Apesar de não terem promovido melhorias significativas neste contexto, estes métodos podem ser adaptados a outros domínios

Emerging Challenges and Opportunities in Infectious Disease Epidemiology.

Much of the intellectual tradition of modern epidemiology stems from efforts to understand and combat chronic diseases persisting through the 20th century epidemiologic transition of countries such as the United States and United Kingdom. After decades of relative obscurity, infectious disease epidemiology has undergone an intellectual rebirth in recent years amid increasing recognition of the threat posed by both new and familiar pathogens. Here, we review the emerging coalescence of infectious disease epidemiology around a core set of study designs and statistical methods bearing little resemblance to the chronic disease epidemiology toolkit. We offer our outlook on challenges and opportunities facing the field, including the integration of novel molecular and digital information sources into disease surveillance, the assimilation of such data into models of pathogen spread, and the increasing contribution of models to public health practice. We next consider emerging paradigms in causal inference for infectious diseases, ranging from approaches to evaluating vaccines and antimicrobial therapies to the task of ascribing clinical syndromes to etiologic microorganisms, an age-old problem transformed by our increasing ability to characterize human-associated microbiota. These areas represent an increasingly important component of epidemiology training programs for future generations of researchers and practitioners

The intersection of pharmacology, imaging, and genetics in the development of personalized medicine

We currently rely on large randomized trials and meta-analyses to make clinical decisions; this places us at a risk of discarding subgroup or individually specific treatment options owing to their failure to prove efficacious across entire populations. There is a new era emerging in personalized medicine that will focus on individual differences that are not evident phenomenologically. Much research is directed towards identifying genes, endophenotypes, and biomarkers of disease that will facilitate diagnosis and predict treatment outcome. We are at the threshold of being able to predict treatment response, primarily through genetics and neuroimaging. In this review we discuss the most promising markers of treatment response and adverse effects emerging from the areas of pharmacogenetics and neuroimaging in depression and schizophrenia