Antibiotic resistance is one of the major global threats to human health and occurs when antibiotics lose their ability to combat bacterial infections. In this problem, a clinical decision support system could use phenotypes in order to alert clinicians of the emergence of patterns of antibiotic resistance in patients. Patient phenotyping is the task of finding a set of patient characteristics related to a specific medical problem such as the one described in this work. However, a single explanation of a medical phenomenon might be useless in the eyes of a clinical expert and be discarded. The discovery of multiple patient phenotypes for the same medical phenomenon would be useful in such cases. Therefore, in this work, we define the problem of mining diverse top-k phenotypes and propose the EDSLM algorithm, which is based on the Subgroup Discovery technique, the subgroup list model, and the Minimum Description Length principle. Our proposal provides clinicians with a method with which to obtain multiple and diverse phenotypes of a set of patients. We show a real use case of phenotyping in antimicrobial resistance using the well-known MIMIC-III dataset.

Nosocomial infections are a great source of concern for healthcare organizations. The spatial layout of hospitals and the movements of patients play significant roles in the spread of outbreaks. However, the existing models are ad-hoc for a specific hospital and research topic. This work shows the design of a data model to study the spread of infections among hospital patients. Its spatial dimension describes the hospital layout with several levels of detail, and the temporal dimension describes everything that happens to the patients in the form of events, which can relate to the spatial dimension. The model is meant to be sufficiently general to fit any hospital layout and to be used for different epidemiological research topics. We proved the model's suitability by defining six queries based on patients' movements and contacts that could assist in several epidemiological research tasks, such as discovering potential transmission routes. The model was implemented as an RDF* knowledge graph, and the queries were in SPARQL*. Finally, we designed two experiments in which two outbreaks of Clostridium difficile were analyzed using several queries (four in the first experiment and two in the second) on a knowledge graph (105,000 nodes, 185,000 edges) with synthetic data.

Alerts are a common functionality of clinical decision support systems (CDSSs). Although they have proven to be useful in clinical practice, the alert burden can lead to alert fatigue and significantly reduce their usability and acceptance. Based on a literature review, we propose a unified framework consisting of a set of meaningful timestamps that allows the use of state-of-the-art measures for alert burden, such as alert dwell time, alert think time, and response time. In addition, it can be used to investigate other measures that could be relevant as regards dealing with this problem. Furthermore, we provide a case study concerning three different types of alerts to which the framework was successfully applied. We consider that our framework can easily be adapted to other CDSSs and that it could be useful for dealing with alert burden measurement thus contributing to its appropriate management.

OBJECTIVES: To examine recent literature in order to present a comprehensive overview of the current trends as regards the computational models used to represent the propagation of an infectious outbreak in a population, paying particular attention to those that represent network-based transmission. METHODS: a systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Papers published in English between 2010 and September 2021 were sought in the ACM Digital Library, IEEE Xplore, PubMed and Scopus databases. RESULTS: Upon considering their titles and abstracts, 832 papers were obtained, of which 192 were selected for a full content-body check. Of these, 112 studies were eventually deemed suitable for quantitative and qualitative analysis. Emphasis was placed on the spatial and temporal scales studied, the use of networks or graphs, and the granularity of the data used to evaluate the models. The models principally used to represent the spreading of outbreaks have been stochastic (55.36%), while the type of networks most frequently used are relationship networks (32.14%). The most common spatial dimension used is a region (19.64%) and the most used unit of time is a day (28.57%). Synthetic data as opposed to an external source were used in 51.79% of the papers. With regard to the granularity of the data sources, aggregated data such as censuses or transportation surveys are the most common. CONCLUSION: We identified a growing interest in the use of networks to represent disease transmission. We detected that research is focused on only certain combinations of the computational model, type of network (in both the expressive and the structural sense) and spatial scale, while the search for other interesting combinations has been left for the future.

The current situation of critical progression as regards the resistance of bacteria to antibiotics has led to the use of machine learning techniques in order to provide clinicians with new knowledge for decision making. One of the key aspects is precision medicine, which focuses on finding phenotypes of patients for whom treatments may be more effective or detecting high risk patients whose progress must be closely monitored. The identification of these phenotypes requires the application of a methodology whose results are consistent and interpretable, along with the control of the process by a clinical expert. Studies concerning machine learning phenotyping use conventional clustering or subgroup algorithms that require information to be obtained a priori. We propose a new unsupervised machine learning technique, denominated as Trace-based clustering, and a 5-step methodology in order to support clinicians when identifying patient phenotypes. The steps proposed are: (1) Extraction and transformation of data and analysis of clustering tendency, (2) Selection of clustering algorithm and parameters, (3) Automatic generation of candidate clusters, (4) Visual support for selection of candidate clusters, and (5) Evaluation by clinical experts. We undertake an antimicrobial resistance use case by employing the MIMIC-III open-access database for patients infected with the Methicillin-resistant Staphylococcus Aereus and Enterococcus Faecium treated with Vancomycin. The experiments were carried out using the Hopkins statistic in order to evaluate the clustering tendency of the data, the K-Means algorithm for clustering, and the Dice coefficient to measure the similarity of the clusters. Our experiments computed 370 potential patient sets (clusters) so as to obtain 19 candidate clusters for their final evaluation. We evaluated the final result with a classification model in order to ensure the consistency of the phenotypes obtained and we compared the result with a traditional clustering approach. We found a reduced set of consistent candidate clusters with a common phenotype (resistance and death), which were different from the other candidate clusters. An expert in the domain could add labels with clinical meaning to the reduced number of clusters. We show that the proposed methodology allows physicians to identify consistent patient phenotypes. Our experiments confirm that quality measures, and the visual analysis could help expert clinicians to control the knowledge discovery process and obtain interpretable results. Our approach provides a new perspective: that of finding patient sets using clustering techniques evaluated by overlapping clusters of the previous partitions. The method proposed is general and can be easily adapted to any other problem and any other clinical settings.

High spatial resolution vegetation products are fundamental in different fields, such as improving the understanding of crop seasonality at regional scales. Here, two new vegetation products such as the Leaf Area Index (LAI) and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) are downscaled at continental scales. A novel HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HIS-TARFM) is used to generate the gap-free time series of Landsat surface reflectance data by fusing MODIS and Landsat reflectance for the contiguous United States. An artificial neural network is trained to capture the relationship between the gap free Landsat surface reflectance and the MODIS LAI/FAPAR products and allows to predict both biophysical variables at 30 meters spatial resolution. The results confirm that both vegetation products largely agree with the test dataset, providing low error and high explained variance.

One of the major problems of healthcare institutions is the treatment of infections caused by bacteria that are resistant to antimicrobials. The early prediction of such infections can improve the patient's evolution as well as minimise the spread of antimicrobial resistance. The creation of effective prediction models is particularly limited due to the high dimensionality of data, the imbalanced datasets and the concept drift problem. In this paper, we face these challenges from a machine learning perspective, considering the interpretability of the resulting models as essential. In particular, we present a study of multiple techniques focused on the mitigation of these problems, that are used in combination with interpretable models. Our results indicate that the use of oversampling along with sliding windows can improve the resulting AUC of models (up to reaching a mean AUC of 0.80 in our dataset), and FCBF can be used to drastically reduce the number of predictors, obtaining simpler models with a slight AUC reduction (from a mean number of predictors of 69.78 to 16.28, achieving a mean AUC of 0.76). According to our results, we show that the combination of multiple techniques for dealing with the aforementioned data-mining problems can clearly improve the performance of prediction models for antimicrobial resistance.

Case-Based Reasoning (CBR) learns new knowledge from data and so can cope with changing environments. CBR is very different from model-based systems since it can learn incrementally as new data is available, storing new cases in its case-base. This means that it can benefit from readily available new data, but also case-base maintenance (CBM) is essential to manage the cases, deleting and compacting the case-base. In the 50th anniversary of CNN (considered the first CBM algorithm), new CBM methods are proposed to deal with the new requirements of Big Data scenarios. In this paper, we present an accessible historic perspective of CBM and we classify and analyse the most recent approaches to deal with these requirements.

Abstract: The temporal dimension is highly present in almost all critical medical scenarios (e.g. intensive care units, burn units or cardiology departments), considering that the temporal evolution of the patient is a key factor in providing an effective healthcare. While the irruption of new technologies in hospital services provides large amounts of data, knowledge‐based systems are not often considered by clinicians, as part of the clinical information process. Case‐based reasoning (CBR) is a field of artificial intelligence that tackles new problems by referring to analogous problems that have already been solved in the past. Therefore, CBR seems to be an effective approach in medical domains since cases refer directly to patient episodes within the health records. Despite the importance of the temporal dimension, no in‐depth study of the impact of time on the CBR systems has been carried out in critical medical domains. This work focuses on the development of case retrieval systems based on their temporal similarity in the medical domain. We present T‐CARE, a temporal case retrieval system that combines classical and non‐classical approaches to measure temporal similarity of cases which are composed of temporal sequences of time point events and intervals. Finally, we show its practical implementation in an intensive care burn unit.

The disruption of the circadian system in humans has been associated with the development of chronic illnesses and the worsening of pre-existing pathologies. Therefore, the assessment of human circadian system function under free living conditions using non-invasive techniques needs further research. Traditionally, overt rhythms such as activity and body temperature have been analyzed separately; however, a comprehensive index could reduce individual recording artifacts. Thus, a new variable (TAP), based on the integrated analysis of three simultaneous recordings: skin wrist temperature (T), motor activity (A) and body position (P) has been developed. Furthermore, we also tested the reliability of a single numerical index, the Circadian Function Index (CFI), to determine the circadian robustness. An actimeter and a temperature sensor were placed on the arm and wrist of the non-dominant hand, respectively, of 49 healthy young volunteers for a period of one week. T, A and P values were normalized for each subject. A non-parametric analysis was applied to both TAP and the separate variables to calculate their interdaily stability, intradaily variability and relative amplitude, and these values were then used for the CFI calculation. Modeling analyses were performed in order to determine TAP and CFI reliability. Each variable (T, A, P or TAP) was independently correlated with rest-activity logs kept by the volunteers. The highest correlation (r= -0.993, p<0.0001), along with highest specificity (0.870), sensitivity (0.740) and accuracy (0.904), were obtained when rest-activity records were compared to TAP. Furthermore, the CFI proved to be very sensitive to changes in circadian robustness. Our results demonstrate that the integrated TAP variable and the CFI calculation are powerful methods to assess circadian system status, improving sensitivity, specificity and accuracy in differentiating activity from rest over the analysis of wrist temperature, body position or activity alone.

The early diagnosis and the correct therapy for generalized infections is an important factor for patient survival in intensive care burn units (ICBUs). Due to the number of pathologies involved, there is not a specific etiology and, therefore, it is difficult for physicians to quantify the patient severity to state the diagnosis. In this scenario, CBR finds problems to obtain a reliable solution when retrieved cases are highly similar. For example, in ICBU patients slight variations of monitored parameters have a deep impact on the patient's severity evaluation. Therefore, it seems necessary to extend the system outcome in order to indicate the reliance of the solution obtained. Main efforts in the literature for CBR evaluation focus on case retrieval (i.e. similarity) or on a retrospective analysis. However, these approaches do not seem to suffice when cases are very close. In this work, we propose and implement a CBR system to state the chance of a patient to survive. The system has been tested using a database of 89 patients from an ICBU, obtaining about 76% accuracy. Furthermore, in order to evaluate the behaviour of the CBR system in this kind of scenarios, we propose three techniques to obtain a reliance solution degree, one based on case retrieval and two based on case reuse.