Aachen-Heerlen annotated steel microstructure dataset.

Studying steel microstructures yields important insights regarding its mechanical characteristics. Within steel, microstructures transform based on a multitude of factors including chemical composition, transformation temperatures, and cooling rates. Martensite-austenite (MA) islands in bainitic steel appear as blocky structures with abstract shapes that are difficult to identify and differentiate from other types of microstructures. In this regard, material science may benefit from machine learning models that are able to automatically and accurately detect these structures. However, the training process of the state-of-the-art machine learning models requires a large amount of high-quality data. In this dataset, we provide 1.705 scanning electron microscopy images along with a set of 8.909 expert-annotated polygons to describe the geometry of the MA islands that appear on the images. We envision that this dataset will be useful for material scientists to explore the relationship between the morphology of bainitic steel and mechanical characteristics. Moreover, computer vision researchers and practitioners may use this data for training state-of-the-art object segmentation models for abstract geometries such as MA islands.

Indexing the Event Calculus: Towards practical human-readable Personal Health Systems.

Personal Health Systems (PHS) are mobile solutions tailored to monitoring patients affected by chronic non communicable diseases. In general, a patient affected by a chronic disease can generate large amounts of events: for example, in Type 1 Diabetic patients generate several glucose events per day, ranging from at least 6 events per day (under normal monitoring) to 288 per day when wearing a continuous glucose monitor (CGM) that samples the blood every 5 minutes for several days. Just by itself, without considering other physiological parameters, it would be impossible for medical doctors to individually and accurately follow every patient, highlighting the need of simple approaches towards querying physiological time series. Achieving this with current technology is not an easy task, as on one hand it cannot be expected that medical doctors have the technical knowledge to query databases and on the other hand these time series include thousands of events, which requires to re-think the way data is indexed. Anyhow, handling data streams efficiently is not enough. Domain experts' knowledge must be explicitly included into PHSs in a way that it can be easily readed and modified by medical staffs. Logic programming represents the perfect programming paradygm to accomplish this task. In this work, an Event Calculus-based reasoning framework to standardize and express domain-knowledge in the form of monitoring rules is suggested, and applied to three different use cases. However, if online monitoring has to be achieved, the reasoning performance must improve dramatically. For this reason, three promising mechanisms to index the Event Calculus Knowledge Base are proposed. All of them are based on different types of tree indexing structures: k-d trees, interval trees and red-black trees. The paper then compares and analyzes the performance of the three indexing techniques, by computing the time needed to check different type of rules (and eventually generating alerts), when the number of recorded events (e.g. values of physiological parameters) increases. The results show that customized jREC performs much better when the event average inter-arrival time is little compared to the checked rule time-window. Instead, where the events are more sparse, the use of k-d trees with standard EC is advisable. Finally, the Multi-Agent paradigm helps to wrap the various components of the system: the reasoning engines represent the agent minds, and the sensors are its body. The said agents have been developed in MAGPIE, a mobile event based Java agent platform.

Type 2 Diabetes Patients Benefit from the COMODITY12 mHealth System: Results of a Randomised Trial.

Patient acceptance is one of the major barriers toward widespread use of mHealth systems. The aim of this study was to assess system operability and whole trial feasibility, including patients' experience with their use of COMMODITY12 mHealth system under. Secondary study aims included assessment of several metabolic parameters as well as patient adherence to the treatment. This was a prospective parallel-arm randomized controlled trial in outpatients diagnosed with DM2, being treated in the primary care settings in Lodz region, Poland, with 6 weeks period of follow-up. Patients opinions were collected with 7-item questionnaire, assessing different aspects of system use, as well as EuroQol-5D-5 L questionnaire, assessing health-related quality of life. Sixty patients (female, 24, male, 36, mean age +/- SD 59.5 +/- 6.8) completed study. All four layers of the COMMODITY12 system proved to work smooth under real-life conditions, without major problems. All dimensions of experience with system use were assessed well, with maximum values for clearness of instructions, and ease of use (4.80, and 4.63, respectively). Health related quality of life, as assessed with cumulative utility measure, improved significantly in COMMODITY12 system users (P < 0.05). mHealth system modestly improved glycaemic and blood pressure control, assuring high level of patient adherence with overall adherence reaching 92.9 %. Study proved that the COMODITY12 system is well accepted by type 2 diabetes patients taking part in clinical trial, leading to several clinical benefits, and improved quality of life. Nevertheless, before future commercialisation of the system, several minor problems identified during the study need to be addressed.

Processing Diabetes Mellitus Composite Events in MAGPIE.

The focus of this research is in the definition of programmable expert Personal Health Systems (PHS) to monitor patients affected by chronic diseases using agent oriented programming and mobile computing to represent the interactions happening amongst the components of the system. The paper also discusses issues of knowledge representation within the medical domain when dealing with temporal patterns concerning the physiological values of the patient. In the presented agent based PHS the doctors can personalize for each patient monitoring rules that can be defined in a graphical way. Furthermore, to achieve better scalability, the computations for monitoring the patients are distributed among their devices rather than being performed in a centralized server. The system is evaluated using data of 21 diabetic patients to detect temporal patterns according to a set of monitoring rules defined. The system's scalability is evaluated by comparing it with a centralized approach. The evaluation concerning the detection of temporal patterns highlights the system's ability to monitor chronic patients affected by diabetes. Regarding the scalability, the results show the fact that an approach exploiting the use of mobile computing is more scalable than a centralized approach. Therefore, more likely to satisfy the needs of next generation PHSs. PHSs are becoming an adopted technology to deal with the surge of patients affected by chronic illnesses. This paper discusses architectural choices to make an agent based PHS more scalable by using a distributed mobile computing approach. It also discusses how to model the medical knowledge in the PHS in such a way that it is modifiable at run time. The evaluation highlights the necessity of distributing the reasoning to the mobile part of the system and that modifiable rules are able to deal with the change in lifestyle of the patients affected by chronic illnesses.

Performance comparison of multi-label learning algorithms on clinical data for chronic diseases.

We are motivated by the issue of classifying diseases of chronically ill patients to assist physicians in their everyday work. Our goal is to provide a performance comparison of state-of-the-art multi-label learning algorithms for the analysis of multivariate sequential clinical data from medical records of patients affected by chronic diseases. As a matter of fact, the multi-label learning approach appears to be a good candidate for modeling overlapped medical conditions, specific to chronically ill patients. With the availability of such comparison study, the evaluation of new algorithms should be enhanced. According to the method, we choose a summary statistics approach for the processing of the sequential clinical data, so that the extracted features maintain an interpretable link to their corresponding medical records. The publicly available MIMIC-II dataset, which contains more than 19,000 patients with chronic diseases, is used in this study. For the comparison we selected the following multi-label algorithms: ML-kNN, AdaBoostMH, binary relevance, classifier chains, HOMER and RAkEL. Regarding the results, binary relevance approaches, despite their elementary design and their independence assumption concerning the chronic illnesses, perform optimally in most scenarios, in particular for the detection of relevant diseases. In addition, binary relevance approaches scale up to large dataset and are easy to learn. However, the RAkEL algorithm, despite its scalability problems when it is confronted to large dataset, performs well in the scenario which consists of the ranking of the labels according to the dominant disease of the patient.

Multi-label classification of chronically ill patients with bag of words and supervised dimensionality reduction algorithms.

OBJECTIVE: This research is motivated by the issue of classifying illnesses of chronically ill patients for decision support in clinical settings. Our main objective is to propose multi-label classification of multivariate time series contained in medical records of chronically ill patients, by means of quantization methods, such as bag of words (BoW), and multi-label classification algorithms. Our second objective is to compare supervised dimensionality reduction techniques to state-of-the-art multi-label classification algorithms. The hypothesis is that kernel methods and locality preserving projections make such algorithms good candidates to study multi-label medical time series. METHODS: We combine BoW and supervised dimensionality reduction algorithms to perform multi-label classification on health records of chronically ill patients. The considered algorithms are compared with state-of-the-art multi-label classifiers in two real world datasets. Portavita dataset contains 525 diabetes type 2 (DT2) patients, with co-morbidities of DT2 such as hypertension, dyslipidemia, and microvascular or macrovascular issues. MIMIC II dataset contains 2635 patients affected by thyroid disease, diabetes mellitus, lipoid metabolism disease, fluid electrolyte disease, hypertensive disease, thrombosis, hypotension, chronic obstructive pulmonary disease (COPD), liver disease and kidney disease. The algorithms are evaluated using multi-label evaluation metrics such as hamming loss, one error, coverage, ranking loss, and average precision. RESULTS: Non-linear dimensionality reduction approaches behave well on medical time series quantized using the BoW algorithm, with results comparable to state-of-the-art multi-label classification algorithms. Chaining the projected features has a positive impact on the performance of the algorithm with respect to pure binary relevance approaches. CONCLUSIONS: The evaluation highlights the feasibility of representing medical health records using the BoW for multi-label classification tasks. The study also highlights that dimensionality reduction algorithms based on kernel methods, locality preserving projections or both are good candidates to deal with multi-label classification tasks in medical time series with many missing values and high label density.