17 research outputs found

    Explainable temporal data mining techniques to support the prediction task in Medicine

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    In the last decades, the increasing amount of data available in all fields raises the necessity to discover new knowledge and explain the hidden information found. On one hand, the rapid increase of interest in, and use of, artificial intelligence (AI) in computer applications has raised a parallel concern about its ability (or lack thereof) to provide understandable, or explainable, results to users. In the biomedical informatics and computer science communities, there is considerable discussion about the `` un-explainable" nature of artificial intelligence, where often algorithms and systems leave users, and even developers, in the dark with respect to how results were obtained. Especially in the biomedical context, the necessity to explain an artificial intelligence system result is legitimate of the importance of patient safety. On the other hand, current database systems enable us to store huge quantities of data. Their analysis through data mining techniques provides the possibility to extract relevant knowledge and useful hidden information. Relationships and patterns within these data could provide new medical knowledge. The analysis of such healthcare/medical data collections could greatly help to observe the health conditions of the population and extract useful information that can be exploited in the assessment of healthcare/medical processes. Particularly, the prediction of medical events is essential for preventing disease, understanding disease mechanisms, and increasing patient quality of care. In this context, an important aspect is to verify whether the database content supports the capability of predicting future events. In this thesis, we start addressing the problem of explainability, discussing some of the most significant challenges need to be addressed with scientific and engineering rigor in a variety of biomedical domains. We analyze the ``temporal component" of explainability, focusing on detailing different perspectives such as: the use of temporal data, the temporal task, the temporal reasoning, and the dynamics of explainability in respect to the user perspective and to knowledge. Starting from this panorama, we focus our attention on two different temporal data mining techniques. The first one, based on trend abstractions, starting from the concept of Trend-Event Pattern and moving through the concept of prediction, we propose a new kind of predictive temporal patterns, namely Predictive Trend-Event Patterns (PTE-Ps). The framework aims to combine complex temporal features to extract a compact and non-redundant predictive set of patterns composed by such temporal features. The second one, based on functional dependencies, we propose a methodology for deriving a new kind of approximate temporal functional dependencies, called Approximate Predictive Functional Dependencies (APFDs), based on a three-window framework. We then discuss the concept of approximation, the data complexity of deriving an APFD, the introduction of two new error measures, and finally the quality of APFDs in terms of coverage and reliability. Exploiting these methodologies, we analyze intensive care unit data from the MIMIC dataset

    Diversity and ethics in trauma and acute care surgery teams: results from an international survey

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    Background Investigating the context of trauma and acute care surgery, the article aims at understanding the factors that can enhance some ethical aspects, namely the importance of patient consent, the perceptiveness of the ethical role of the trauma leader, and the perceived importance of ethics as an educational subject. Methods The article employs an international questionnaire promoted by the World Society of Emergency Surgery. Results Through the analysis of 402 fully filled questionnaires by surgeons from 72 different countries, the three main ethical topics are investigated through the lens of gender, membership of an academic or non-academic institution, an official trauma team, and a diverse group. In general terms, results highlight greater attention paid by surgeons belonging to academic institutions, official trauma teams, and diverse groups. Conclusions Our results underline that some organizational factors (e.g., the fact that the team belongs to a university context or is more diverse) might lead to the development of a higher sensibility on ethical matters. Embracing cultural diversity forces trauma teams to deal with different mindsets. Organizations should, therefore, consider those elements in defining their organizational procedures. Level of evidence Trauma and acute care teams work under tremendous pressure and complex circumstances, with their members needing to make ethical decisions quickly. The international survey allowed to shed light on how team assembly decisions might represent an opportunity to coordinate team member actions and increase performance

    Supporting the Prediction of AKI Evolution Through Interval-Based Approximate Predictive Functional Dependencies

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    In this paper, we focus on the early prediction of patterns related to the severity stage of Acute Kidney Injury (AKI) in an ICU setting. Such problem is challenging from several points of view: (i) AKI in ICU is a high-risk complication for ICU patients and needs to be suitably prevented, and (ii) the detection of AKI pathological states is done with some delay, due to the required data collection. To support the early prediction of AKI diagnosis, we extend a recently-proposed temporal framework to deal with the prediction of multivalued interval-based patterns, representing the evolution of pathological states of patients. We evaluated our approach on the MIMIC-IV dataset

    Discovering predictive trend-event patterns in temporal clinical data

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    Overwhelming amounts of clinical data are retrieved daily, and healthcare stakeholders may want to derive new knowledge from them. One of the methodological tools proposed to analyze clinical data is pattern mining, even with its temporal extensions. In particular, research efforts have been devoted to either mining complex temporal features (e.g., trends of a specific vital sign) or discovering predictive patterns capable of describing the class of interest compactly. In this paper, we propose a methodology for deriving a new kind of predictive temporal patterns, called predictive trend-event patterns (PTE-Ps), that consists of predictive patterns composed by event occurrences and trends of vital signs, they could influence. PTE-Ps are extracted using a classification model that considers and combines various predictive pattern candidates and selects only those that are relevant to improve the performance of the prediction of a specific class (e.g., only those patterns important to predict sepsis). We provide an original algorithm to mine PTE-Ps and describe the tool we implemented for retrieving them. Finally, we discuss some first results we obtained by pre-processing and mining ICU data from the MIMIC III database, focusing on trend-event patterns predictive of sepsis

    Impact of donor ABH-secretor status in ABO-mismatched living donor kidney transplantation

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    The ABO blood group is a major determinant in living donor kidney transplantation since AB antigens are expressed on renal tissue. Little attention has been directed to the ABH-secretor status of the donor kidney. As renal tissue is capable of secreting soluble ABH antigens in secretors, we examined the influence of the ABH-secretor status of kidney donors on outcome in ABO-mismatched living donor kidney transplantation.; We retrospectively analyzed all patients who underwent ABO-mismatched kidney transplantation at the University Hospital Basel from September 2005 to October 2013. The ABH-secretor status was determined in all donors by molecular genetic analysis.; Of all 55 patients who received transplants, we excluded all patients with donor-specific antibodies (n = 4). Forty-one donors were secretors (78%) and 11 were nonsecretors (22%). Recipients of ABH-secretor donor organs showed a significantly higher glomerular filtration rate throughout the first 6 months posttransplant, whereas no significant influence on posttransplant anti-A/B titers was found. Regression analysis revealed a significant impact on humoral rejection, whereas not on vascular or interstitial rejection in protocol kidney biopsies.; The donor ABH-secretor status may have an influence on early posttransplant renal function in patients undergoing ABO-mismatched living donor kidney transplantation. Further prospective studies with long-term follow-up are needed to elucidate involved pathomechanisms
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