We study the problem of detecting adverse drug events in electronic health records. The challenge is this work is to aggregate heterogeneous data types involving lab measurements, diagnoses codes and medications codes. An earlier framework proposed for the same problem demonstrated promising predictive performance for the random forest classifier by using only lab measurements as data features. We extend this framework, by additionally including diagnosis and drug prescription codes, concurrently. In addition, we employ the concept of hierarchies of clinical codes as proposed by another work, in order to exploit the inherently complex nature of the medical data. Moreover, we extended the state-of-the-art by considering variable patient history lengths before the occurrence of an ADE event rather than a patient history of an arbitrary length. Our experimental evaluation on eight medical datasets of adverse drug events, five different patient history lengths, and six different classifiers, suggests that the integration of these additional features on the different window lengths provides significant improvements in terms of AUC while employing medically relevant features.

We present a clustering framework for identifying patient groups with Adverse Drug Reactions from Electronic Health Records (EHRs). The increased adoption of EHRs has brought changes in the way drug safety surveillance is carried out and plays an important role in effective drug regulation. Unsupervised machine learning methods using EHRs as their input can identify patients that share common meaningful information, without the need for expert input. In this work, we propose a generalized framework that exploits the strengths of different clustering algorithms and via clustering aggregation identifies consensus patient cluster profiles. Moreover, the inherent hierarchical structure of diagnoses and medication codes is exploited. We assess the statistical significance of the produced clusterings by applying a randomization technique that keeps the data distribution margins fixed, as we are interested in evaluating information that is not conveyed by the marginal distributions. The experimental findings suggest that the framework produces medically meaningful patient groups with regard to adverse drug events by investigating two use-cases, i.e., aplastic anaemia and drug-induced skin eruption.

Epidemics of infectious diseases can pose a serious threat to public health and the global economy. Despite scientific advances, containment and mitigation of infectious diseases remain a challenging task. In this paper, we investigate the potential of reinforcement learning as a decision making tool for epidemic control by constructing a deep Reinforcement Learning simulator, called EpidRLearn, composed of a contact-based, age-structured extension of the SEIR compartmental model, referred to as C-SEIR. We evaluate EpidRLearn by comparing the learned policies to two deterministic policy baselines. We further assess our reward function by integrating an alternative reward into our deep RL model. The experimental evaluation indicates that deep reinforcement learning has the potential of learning useful policies under complex epidemiological models and large state spaces for the mitigation of infectious diseases, with a focus on COVID-19.

Background and Objective: Novel advances in machine learning (ML) and its subfield, deep learning (DL), as well as the recent release of large-scale electrocardiogram (ECG) databases, have driven a sharp increase in research related to automated ECG interpretation. This review aims to summarize the recent ML approaches for automatically interpreting standard 12-lead ECG signals.

Methods: We searched 10 indexing databases, for original research in English, referring to the application of ML/DL techniques in 12-lead, raw ECG signal analysis. The retrieved titles were filtered based on their relevance. The results were summarized and reported.

Key Content and Findings: More than 80% of studies integrated a DL approach, while fewer attempts applied a feature extraction method to obtain inputs for training a simple ML classifier. The average diagnostic accuracy was as high as 90%, while several other performance metrics, such as the area under the curve (AUC), F1-score, sensitivity and specificity, were also employed. DL models generally demanded 10-time more samples for training but were capable of better handling multi-class problems. The most frequently involved disease (49% of studies) was myocardial infarction (MI), while atrial fibrillation (AF) was encountered in more than one-third of studies. Various datasets were used for training and testing, constituting either private collections or publicly available databanks [such as the “Physikalisch-Technische Bundesanstalt” (PTB) dataset and datasets derived from the “China Physiological Signal Challenge” and the “Computing in Cardiology Challenge”]. Overall, DL and simpler ML approaches for automated ECG interpretation display unprecedented growth, reaching remarkably high performances.

Conclusions: While such novel tools can support clinicians in reaching reliable diagnoses for life-threatening conditions on the spot, concerns regarding their accountability do exist. Generalizability of the developed approaches is still an issue, possibly mitigable with the extensive deployment of developed models, so as to become massively accessible and validatable. Finally, the observed heterogeneity of the various attempts underlines the need for transparency and reproducibility in the development processes.

Sepsis refers to a potentially life-threatening situation where the immune system of the human body has an extreme response to an infection. In the presence of underlying comorbidities, the situation can become even worse and result in death. Employing unsupervised machine learning techniques, such as clustering, can assist in providing a better understanding of patient phenotypes by unveiling subgroups characterized by distinct sepsis progression and treatment patterns. More concretely, this study introduces M-ClustEHR, a clustering approach that utilizes medical data of multiple modalities by employing a multimodal autoencoder for learning comprehensive sepsis patient representations. M-ClustEHR consistently outperforms traditional clustering approaches in terms of several internal clustering performance metrics, as well as cluster stability in identifying phenotypes in the sepsis cohort. The unveiled patterns, supported by existing medical literature and clinicians, highlight the importance of multimodal clustering for advancing personalized sepsis care.