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

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

A manifesto on explainability for artificial intelligence in medicine.

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, output to users. This concern is especially legitimate in biomedical contexts, where patient safety is of paramount importance. This position paper brings together seven researchers working in the field with different roles and perspectives, to explore in depth the concept of explainable AI, or XAI, offering a functional definition and conceptual framework or model that can be used when considering XAI. This is followed by a series of desiderata for attaining explainability in AI, each of which touches upon a key domain in biomedicine

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

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

A 3-Window Framework for the Discovery and Interpretation of Predictive Temporal Functional Dependencies

Clinical databases collect large volume of data. Relationships and patterns within these data could provide new medical knowledge. Temporal data mining has as major scope the discovery of potential hidden knowledge from large amounts of data, offering the possibility to identify different features less visible or hidden to common analysis techniques. In this work, we show how temporal data mining, precisely mining of functional dependencies, can be fruitfully exploited to improve clinical prediction. To develop an early prediction model, a window-based data aggregation approach could be a good starting point, therefore we introduce a new temporal framework based on three temporal windows designed to extract predictive information. In particular, we propose a methodology for deriving a new kind of predictive temporal patterns. We exploit the predictive aspect of the approximate temporal functional dependencies, formally introducing the concept of Predictive Functional Dependency (PFD), a new type of approximate temporal functional dependency. We discuss some first results we obtained by pre-processing and mining ICU data from the MIMIC III database, focusing on functional dependencies predictive of Acute kidney injury (AKI)

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

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

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

A Reproducible ETL Approach for Window-based Prediction of Acute Kidney Injury in Critical Care Unit and Some Preliminary Results with Support Vector Machines

Acute kidney injury (AKI) is a frequent complication in hospitalized patients, and is associated with worse short and long-term outcomes. An early prediction of AKI to detect the patients at risk could be a first step in the discovery and assessment of new therapies, and in improvements of patient outcomes. The advances in clinical informatics and the increasing availability of electronic medical records have allowed the development of predictive models of AKI diagnosis. In this research work we provide a consistent reproducible ETL pipeline for Intensive Care Unit (ICU) data, in particular regarding the MIMIC III database, to support the early prediction of AKI. Then, we build different predictive models aimed at early identifying subjects who could experience AKI syndrome in their next 7 days after the ICU admission. The entire procedure is based on a recently proposed rolling observational window approach. We consider two predictive models, Gradient Boosting Decision Trees and Support Vector Machines, via different platforms

Treatment of aortoiliac occlusive or dilatative disease concomitant with kidney transplantation: how and when?

AbstractBackground and PurposeAortoiliac (AI) lesions (both dilatative and occlusive) can occur in kidney allograft recipients. The correct timing of vascular imaging and treatment is controversial. Aim of the present paper is to report our experience.Methodsbetween January 2010 and December 2012, 106 patients included in our waiting list for kidney transplant underwent computed tomography (CT) angiogram to study AI axis. In 21 cases an AI lesion was identified before transplant. In 3 cases surgery was mandatory before kidney transplant, and in 18 cases lesions were treated simultaneously with kidney transplantation.Main findingsAI pathology distribution was as follows: 15 iliac stenoses treated with thromboendarterectomy (TEA), 2 Leriche syndrome and 1 aortic aneurism treated with an aortobisiliac bypass (AI-BP), and 3 aneurysms treated with endovascular aortic repair (EVAR). In two cases a postoperative hematoma occurred. In one case occlusion of a stent-graft branch was treated with a femoro-femoral crossover bypass and transplant was then performed on the contralateral iliac axis. Perioperative mortality was 0%, and graft survival rate was 100% at 1 year in all cases.ConclusionsA CT angiogram is useful in order to detect AI lesions and to be able to evaluate the best treatment option for the kidney transplantation and the correct timing for additional vascular surgery. The EVAR procedure should be safe, and does not compromise anastomosis success and graft survival, with less postoperative complications than open surgery

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

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