Identifying the joint signature of brain atrophy and gene variant scores in Alzheimer's Disease.

The joint modeling of genetic data and brain imaging information allows for determining the pathophysiological pathways of neurodegenerative diseases such as Alzheimer's disease (AD). This task has typically been approached using mass-univariate methods that rely on a complete set of Single Nucleotide Polymorphisms (SNPs) to assess their association with selected image-derived phenotypes (IDPs). However, such methods are prone to multiple comparisons bias and, most importantly, fail to account for potential cross-feature interactions, resulting in insufficient detection of significant associations. Ways to overcome these limitations while reducing the number of traits aim at conveying genetic information at the gene level and capturing the integrated genetic effects of a set of genetic variants, rather than looking at each SNP individually. Their associations with brain IDPs are still largely unexplored in the current literature, though they can uncover new potential genetic determinants for brain modulations in the AD continuum. In this work, we explored an explainable multivariate model to analyze the genetic basis of the grey matter modulations, relying on the AD Neuroimaging Initiative (ADNI) phase 3 dataset. Cortical thicknesses and subcortical volumes derived from T1-weighted Magnetic Resonance were considered to describe the imaging phenotypes. At the same time the genetic counterpart was represented by gene variant scores extracted by the Sequence Kernel Association Test (SKAT) filtering model. Moreover, transcriptomic analysis was carried on to assess the expression of the resulting genes in the main brain structures as a form of validation. Results highlighted meaningful genotype-phenotype interactionsas defined by three latent components showing a significant difference in the projection scores between patients and controls. Among the significant associations, the model highlighted EPHX1 and BCAS1 gene variant scores involved in neurodegenerative and myelination processes, hence relevant for AD. In particular, the first was associated with decreased subcortical volumes and the second with decreasedtemporal lobe thickness. Noteworthy, BCAS1 is particularly expressed in the dentate gyrus. Overall, the proposed approach allowed capturing genotype-phenotype interactions in a restricted study cohort that was confirmed by transcriptomic analysis, offering insights into the underlying mechanisms of neurodegeneration in AD in line with previous findings and suggesting new potential disease biomarkers.

From narrative descriptions to MedDRA: automagically encoding adverse drug reactions.

CONTEXT: The collection of narrative spontaneous reports is an irreplaceable source for the prompt detection of suspected adverse drug reactions (ADRs). In such task qualified domain experts manually revise a huge amount of narrative descriptions and then encode texts according to MedDRA standard terminology. The manual annotation of narrative documents with medical terminology is a subtle and expensive task, since the number of reports is growing up day-by-day. OBJECTIVES: Natural Language Processing (NLP) applications can support the work of people responsible for pharmacovigilance. Our objective is to develop NLP algorithms and tools for the detection of ADR clinical terminology. Efficient applications can concretely improve the quality of the experts' revisions. NLP software can quickly analyze narrative texts and offer an encoding (i.e., a list of MedDRA terms) that the expert has to revise and validate. METHODS: MagiCoder, an NLP algorithm, is proposed for the automatic encoding of free-text descriptions into MedDRA terms. MagiCoder procedure is efficient in terms of computational complexity. We tested MagiCoder through several experiments. In the first one, we tested it on a large dataset of about 4500 manually revised reports, by performing an automated comparison between human and MagiCoder encoding. Moreover, we tested MagiCoder on a set of about 1800 reports, manually revised ex novo by some experts of the domain, who also compared automatic solutions with the gold reference standard. We also provide two initial experiments with reports written in English, giving a first evidence of the robustness of MagiCoder w.r.t. the change of the language. RESULTS: For the current base version of MagiCoder, we measured an average recall and precision of 86.9% and 91.8%, respectively. CONCLUSIONS: From a practical point of view, MagiCoder reduces the time required for encoding ADR reports. Pharmacologists have only to review and validate the MedDRA terms proposed by the application, instead of choosing the right terms among the 70 K low level terms of MedDRA. Such improvement in the efficiency of pharmacologists' work has a relevant impact also on the quality of the subsequent data analysis. We developed MagiCoder for the Italian pharmacovigilance language. However, our proposal is based on a general approach, not depending on the considered language nor the term dictionary.

Machine learning-based algorithms applied to drug prescriptions and other healthcare services in the Sicilian claims database to identify acromegaly as a model for the earlier diagnosis of rare diseases.

Acromegaly is a rare disease characterized by a diagnostic delay ranging from 5 to 10 years from the symptoms' onset. The aim of this study was to develop and internally validate machine-learning algorithms to identify a combination of variables for the early diagnosis of acromegaly. This retrospective population-based study was conducted between 2011 and 2018 using data from the claims databases of Sicily Region, in Southern Italy. To identify combinations of potential predictors of acromegaly diagnosis, conditional and unconditional penalized multivariable logistic regression models and three machine learning algorithms (i.e., the Recursive Partitioning and Regression Tree, the Random Forest and the Support Vector Machine) were used, and their performance was evaluated. The random forest (RF) algorithm achieved the highest Area under the ROC Curve value of 0.83 (95% CI 0.79-0.87). The sensitivity in the test set, computed at the optimal threshold of predicted probabilities, ranged from 28% for the unconditional logistic regression model to 69% for the RF. Overall, the only diagnosis predictor selected by all five models and algorithms was the number of immunosuppressants-related pharmacy claims. The other predictors selected by at least two models were eventually combined in an unconditional logistic regression to develop a meta-score that achieved an acceptable discrimination accuracy (AUC = 0.71, 95% CI 0.66-0.75). Findings of this study showed that data-driven machine learning algorithms may play a role in supporting the early diagnosis of rare diseases such as acromegaly.

Discovering predictive temporal patterns for Acute Kidney Injury from critical care data.

Acute Kidney Injury is a severe clinical condition with a high risk of multi-organs complications and mortality. For this reason, early recognition is crucial. Our proposal based on a 3-window framework discovers all hidden regularities, called Approximate Predictive Functional Dependencies, with the aim to enable early recognition of high-risk patients during hospitalization in the Intensive Care Unit (ICU). We evaluated the different severity stages according to the Kidney Disease Improving Global Outcomes (KDIGO) guidelines, building different pathological state patterns, from admission to the discharge from ICU. According to the clinical practice, for each patient, we examined various characteristics expressed as a temporal history of events that may predict a pathological state pattern. We evaluated our proposal exploiting the MIMIC-IV dataset, a collection of Electronic Medical Records from ICU. The obtained results showed promising possibilities to use this type of dependency to support clinical practice.

The IHI Rochester Report 2022 on Healthcare Informatics Research: Resuming After the CoViD-19.

In 2020, the CoViD-19 pandemic spread worldwide in an unexpected way and suddenly modified many life issues, including social habits, social relationships, teaching modalities, and more. Such changes were also observable in many different healthcare and medical contexts. Moreover, the CoViD-19 pandemic acted as a stress test for many research endeavors, and revealed some limitations, especially in contexts where research results had an immediate impact on the social and healthcare habits of millions of people. As a result, the research community is called to perform a deep analysis of the steps already taken, and to re-think steps for the near and far future to capitalize on the lessons learned due to the pandemic. In this direction, on June 09th-11th, 2022, a group of twelve healthcare informatics researchers met in Rochester, MN, USA. This meeting was initiated by the Institute for Healthcare Informatics-IHI, and hosted by the Mayo Clinic. The goal of the meeting was to discuss and propose a research agenda for biomedical and health informatics for the next decade, in light of the changes and the lessons learned from the CoViD-19 pandemic. This article reports the main topics discussed and the conclusions reached. The intended readers of this paper, besides the biomedical and health informatics research community, are all those stakeholders in academia, industry, and government, who could benefit from the new research findings in biomedical and health informatics research. Indeed, research directions and social and policy implications are the main focus of the research agenda we propose, according to three levels: the care of individuals, the healthcare system view, and the population view.

Querying temporal clinical databases on granular trends.

This paper focuses on the identification of temporal trends involving different granularities in clinical databases, where data are temporal in nature: for example, while follow-up visit data are usually stored at the granularity of working days, queries on these data could require to consider trends either at the granularity of months ("find patients who had an increase of systolic blood pressure within a single month") or at the granularity of weeks ("find patients who had steady states of diastolic blood pressure for more than 3 weeks"). Representing and reasoning properly on temporal clinical data at different granularities are important both to guarantee the efficacy and the quality of care processes and to detect emergency situations. Temporal sequences of data acquired during a care process provide a significant source of information not only to search for a particular value or an event at a specific time, but also to detect some clinically-relevant patterns for temporal data. We propose a general framework for the description and management of temporal trends by considering specific temporal features with respect to the chosen time granularity. Temporal aspects of data are considered within temporal relational databases, first formally by using a temporal extension of the relational calculus, and then by showing how to map these relational expressions to plain SQL queries. Throughout the paper we consider the clinical domain of hemodialysis, where several parameters are periodically sampled during every session.

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.

Clinical Information Systems and Artificial Intelligence: Recent Research Trends.

OBJECTIVES: This survey aims at reviewing the literature related to Clinical Information Systems (CIS), Hospital Information Systems (HIS), Electronic Health Record (EHR) systems, and how collected data can be analyzed by Artificial Intelligence (AI) techniques. METHODS: We selected the major journals (11 journals) collecting papers (more than 7,000) over the last five years from the top members of the research community, and read and analyzed the papers (more than 200) covering the topics. Then, we completed the analysis using search engines to also include papers from major conferences over the same five years. RESULTS: We defined a taxonomy of major features and research areas of CIS, HIS, EHR systems. We also defined a taxonomy for the use of Artificial Intelligence (AI) techniques on healthcare data. In the light of these taxonomies, we report on the most relevant papers from the literature. CONCLUSIONS: We highlighted some major research directions and issues which seem to be promising and to need further investigations over a medium- or long-term period.

Mining compact predictive pattern sets using classification model.

In this paper, we develop a new framework for mining predictive patterns that aims to describe compactly the condition (or class) of interest. Our framework relies on a classification model that considers and combines various predictive pattern candidates and selects only those that are important for improving the overall class prediction performance. We test our approach on data derived from MIMIC-III EHR database, focusing on patterns predictive of sepsis. We show that using our classification approach we can achieve a significant reduction in the number of extracted patterns compared to the state-of-the-art methods based on minimum predictive pattern mining approach, while preserving the overall classification accuracy of the model.

Normalizing Spontaneous Reports Into MedDRA: Some Experiments With MagiCoder.

Text normalization into medical dictionaries is useful to support clinical tasks. A typical setting is pharmacovigilance (PV). The manual detection of suspected adverse drug reactions (ADRs) in narrative reports is time consuming and natural language processing (NLP) provides a concrete help to PV experts. In this paper, we carry out experiments for testing performances of MagiCoder, an NLP application designed to extract MedDRA terms from narrative clinical text. Given a narrative description, MagiCoder proposes an automatic encoding. The pharmacologist reviews, (possibly) corrects, and then, validates the solution. This drastically reduces the time needed for the validation of reports with respect to a completely manual encoding. In previous work, we mainly tested MagiCoder performances on Italian written spontaneous reports. In this paper, we include some new features, change the experiment design, and carry on more tests about MagiCoder. Moreover, we do a change of language, moving to English documents. In particular, we tested MagiCoder on the CADEC dataset, a corpus of manually annotated posts about ADRs collected from the social media.

A Methodological Framework for the Integrated Design of Decision-Intensive Care Pathways-an Application to the Management of COPD Patients.

Healthcare processes are by nature complex, mostly due to their multidisciplinary character that requires continuous coordination between care providers. They encompass both organizational and clinical tasks, the latter ones driven by medical knowledge, which is inherently incomplete and distributed among people having different expertise and roles. Care pathways refer to planning and coordination of care processes related to specific groups of patients in a given setting. The goal in defining and following care pathways is to improve the quality of care in terms of patient satisfaction, costs reduction, and medical outcome. Thus, care pathways are a promising methodological tool for standardizing care and decision-making. Business process management techniques can successfully be used for representing organizational aspects of care pathways in a standard, readable, and accessible way, while supporting process development, analysis, and re-engineering. In this paper, we introduce a methodological framework that fosters the integrated design, implementation, and enactment of care processes and related decisions, while considering proper representation and management of organizational and clinical information. We focus here and discuss in detail the design phase, which encompasses the simulation of care pathways. We show how business process model and notation (BPMN) and decision model and notation (DMN) can be combined for supporting intertwined aspects of decision-intensive care pathways. As a proof-of-concept, the proposed methodology has been applied to design care pathways related to chronic obstructive pulmonary disease (COPD) in the region of Veneto, in Italy.

Temporal representation and reasoning in medicine: Research directions and challenges.

OBJECTIVE: The main aim of this paper is to propose and discuss promising directions of research in the field of temporal representation and reasoning in medicine, taking into account the recent scientific literature and challenging issues of current interest as viewed from the different research perspectives of the authors of the paper. BACKGROUND: Temporal representation and reasoning in medicine is a well-known field of research in the medical as well as computer science community. It encompasses several topics, such as summarizing data from temporal clinical databases, reasoning on temporal clinical data for therapeutic assessments, and modeling uncertainty in clinical knowledge and data. It is also related to several medical tasks, such as monitoring intensive care patients, providing treatments for chronic patients, as well as planning and scheduling clinical routine activities within complex healthcare organizations. METHODOLOGY: The authors jointly identified significant research areas based on their importance as for temporal representation and reasoning issues; the subjects were considered to be promising topics of future activity. Every subject was addressed in detail by one or two authors and then discussed with the entire team to achieve a consensus about future fields of research. RESULTS: We identified and focused on four research areas, namely (i) fuzzy logic, time, and medicine, (ii) temporal reasoning and data mining, (iii) health information systems, business processes, and time, and (iv) temporal clinical databases. For every area, we first highlighted a few basic notions that would permit any reader--including those who are unfamiliar with the topic--to understand the main goals. We then discuss interesting and promising directions of research, taking into account the recent literature and underlining the yet unresolved medical/clinical issues that deserve further scientific investigation. The considered research areas are by no means disjointed, because they share common theoretical and methodological features. Moreover, subjects of imminent interest in medicine are represented in many of the fields considered. CONCLUSIONS: We propose and discuss promising subjects of future research that deserve investigation to develop software systems that will properly manage the multifaceted temporal aspects of information and knowledge encountered by physicians during their clinical work. As the subjects of research have resulted from merging the different perspectives of the authors involved in this study, we hope the paper will succeed in stimulating discussion and multidisciplinary work in the described fields of research.

Telemedicine for Developing Countries. A Survey and Some Design Issues.

BACKGROUND: Developing countries need telemedicine applications that help in many situations, when physicians are a small number with respect to the population, when specialized physicians are not available, when patients and physicians in rural villages need assistance in the delivery of health care. Moreover, the requirements of telemedicine applications for developing countries are somewhat more demanding than for developed countries. Indeed, further social, organizational, and technical aspects need to be considered for successful telemedicine applications in developing countries. OBJECTIVE: We consider all the major projects in telemedicine, devoted to developing countries, as described by the proper scientific literature. On the basis of such literature, we want to define a specific taxonomy that allows a proper classification and a fast overview of telemedicine projects in developing countries. Moreover, by considering both the literature and some recent direct experiences, we want to complete such overview by discussing some design issues to be taken into consideration when developing telemedicine software systems. METHODS: We considered and reviewed the major conferences and journals in depth, and looked for reports on the telemedicine projects. RESULTS: We provide the reader with a survey of the main projects and systems, from which we derived a taxonomy of features of telemedicine systems for developing countries. We also propose and discuss some classification criteria for design issues, based on the lessons learned in this research area. CONCLUSIONS: We highlight some challenges and recommendations to be considered when designing a telemedicine system for developing countries.

Merging multimedia presentations and semistructured temporal data: a graph-based model and its application to clinical information.

OBJECTIVE: In this paper, we focus on the issue of providing physicians with the capability of representing in a seamless way both temporal aspects of multimedia semistructured data and their temporal presentation requirements. BACKGROUND: Semistructured data are data having some structure, that may be irregular or incomplete and does not necessarily conform to a fixed schema. Semistructured data often contain the description of histories of the considered real world. The eXtensible Markup Language (XML) is becoming a cross compatible and standardized means for representing semistructured clinical data. In the field of medical informatics, there are many ongoing activities concerning XML. In the field of multimedia database systems, the topic related to the integration of several media objects (with their temporal aspects) have been considered both for data modeling and querying issues and for modeling multimedia presentations. METHODOLOGY: We first propose the Multimedia Temporal Graphical Model (MTGM), by representing a clinical database for cardiology patients undergoing cardiac angiographies and then describe it in a formal way. We deal with the problem of expressing MTGM data by XML and of managing MTGM clinical data through an XML-based system. We provide both a technique for translating (a part of) an MTGM database into an XML document and some techniques allowing us to obtain presentations defined by means of the Synchronized Multimedia Integration Language (SMIL) from MTGM presentations. RESULTS: MTGM allows one to represent and store clinical information in a semistructured, temporal, and multimedia database. The physician can define multimedia presentations based on the stored data. Multimedia presentations are then stored in the same MTGM database together with temporal clinical information and are thus represented according to the same data model. A prototype based on an XML native database system has been designed and implemented. DISCUSSION AND CONCLUSIONS: In this work we have considered the theoretical and methodological issues concerning the definition of a general data model for describing temporal and multimedia features of semistructured clinical information. Other research and application oriented features, which have not been considered in MTGM, could be investigated for completing MTGM with regard to its applicability to clinical domains: MTGM does not allow one to express times at different levels of granularities, i.e. with different time units, or with indeterminacy; besides the considered valid time, it could be interesting to manage also other temporal dimensions such as the transaction and availability times. Besides being useful for managing multimedia data stored according to widely accepted standards as MPEG and DICOM, nowadays semistructured data, and XML in particular, are becoming the most important way for expressing and exchanging medical knowledge and data: MTGM can be considered as a data model allowing the seamless representation of both (multimedia and temporal) clinical data and knowledge.

Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes.

BACKGROUND: Over the past 30 years, the international conference on Artificial Intelligence in MEdicine (AIME) has been organized at different venues across Europe every 2 years, establishing a forum for scientific exchange and creating an active research community. The Artificial Intelligence in Medicine journal has published theme issues with extended versions of selected AIME papers since 1998. OBJECTIVES: To review the history of AIME conferences, investigate its impact on the wider research field, and identify challenges for its future. METHODS: We analyzed a total of 122 session titles to create a taxonomy of research themes and topics. We classified all 734 AIME conference papers published between 1985 and 2013 with this taxonomy. We also analyzed the citations to these conference papers and to 55 special issue papers. RESULTS: We identified 30 research topics across 12 themes. AIME was dominated by knowledge engineering research in its first decade, while machine learning and data mining prevailed thereafter. Together these two themes have contributed about 51% of all papers. There have been eight AIME papers that were cited at least 10 times per year since their publication. CONCLUSIONS: There has been a major shift from knowledge-based to data-driven methods while the interest for other research themes such as uncertainty management, image and signal processing, and natural language processing has been stable since the early 1990s. AIME papers relating to guidelines and protocols are among the most highly cited.

Spatiotemporal data visualisation for homecare monitoring of elderly people.

OBJECTIVE: Elderly people who live alone can be assisted by home monitoring systems that identify risk scenarios such as falls, fatigue symptoms or burglary. Given that these systems have to manage spatiotemporal data, human intervention is required to validate automatic alarms due to the high number of false positives and the need for context interpretation. The goal of this work was to provide tools to support human action, to identify such potential risk scenarios based on spatiotemporal data visualisation. METHODS AND MATERIALS: We propose the MTA (multiple temporal axes) model, a visual representation of temporal information of the activity of a single person at different locations. The main goal of this model is to visualize the behaviour of a person in their home, facilitating the identification of health-risk scenarios and repetitive patterns. We evaluate the model's insight capacity compared with other models using a standard evaluation protocol. We also test its practical suitability of the MTA graphical model in a commercial home monitoring system. In particular, we implemented 8VISU, a visualization tool based on MTA. RESULTS: MTA proved to be more than 90% accurate in identify non-risk scenarios, independently of the length of the record visualised. When the spatial complexity was increased (e.g. number of rooms) the model provided good accuracy form up to 5 rooms. Therefore, user preferences and user performance seem to be balanced. Moreover, it also gave high sensitivity levels (over 90%) for 5-8 rooms. Fall is the most recurrent incident for elderly people. The MTA model outperformed the other models considered in identifying fall scenarios (66% of correctness) and was the second best for burglary and fatigue scenarios (36% of correctness). Our experiments also confirm the hypothesis that cyclic models are the most suitable for fatigue scenarios, the Spiral and MTA models obtaining most positive identifications. CONCLUSIONS: In home monitoring systems, spatiotemporal visualization is a useful tool for identifying risk and preventing home accidents in elderly people living alone. The MTA model helps the visualisation in different stages of the temporal data analysis process. In particular, its explicit representation of space and movement is useful for identifying potential scenarios of risk, while the spiral structure can be used for the identification of recurrent patterns. The results of the experiments and the experience using the visualization tool 8VISU proof the potential of the MTA graphical model to mine temporal data and to support caregivers using home monitoring infrastructures.

Mining approximate temporal functional dependencies with pure temporal grouping in clinical databases.

Functional dependencies (FDs) typically represent associations over facts stored by a database, such as "patients with the same symptom get the same therapy." In more recent years, some extensions have been introduced to represent both temporal constraints (temporal functional dependencies - TFDs), as "for any given month, patients with the same symptom must have the same therapy, but their therapy may change from one month to the next one," and approximate properties (approximate functional dependencies - AFDs), as "patients with the same symptomgenerallyhave the same therapy." An AFD holds most of the facts stored by the database, enabling some data to deviate from the defined property: the percentage of data which violate the given property is user-defined. According to this scenario, in this paper we introduce approximate temporal functional dependencies (ATFDs) and use them to mine clinical data. Specifically, we considered the need for deriving new knowledge from psychiatric and pharmacovigilance data. ATFDs may be defined and measured either on temporal granules (e.g.grouping data by day, week, month, year) or on sliding windows (e.g.a fixed-length time interval which moves over the time axis): in this regard, we propose and discuss some specific and efficient data mining techniques for ATFDs. We also developed two running prototypes and showed the feasibility of our proposal by mining two real-world clinical data sets. The clinical interest of the dependencies derived considering the psychiatry and pharmacovigilance domains confirms the soundness and the usefulness of the proposed techniques.

User-oriented views in health care information systems.

In this paper, we present the methodology we adopted in designing and developing an object-oriented database system for the management of medical records. The designed system provides technical solutions to important requirements of most clinical information systems, such as 1) the support of tools to create and manage views on data and view schemas, offering to different users specific perspectives on data tailored to their needs; 2) the capability to handle in a suitable way the temporal aspects related to clinical information; and 3) the effective integration of multimedia data. Remote data access for authorized users is also considered. As clinical application, we describe here the prototype of a user-oriented clinical information system for the archiving and the management of multimedia and temporally oriented clinical data related to percutaneous transluminal coronary angioplasty (PTCA) patients. Suitable view schemas for various user roles (cath-lab physician, ward nurse, general practitioner) have been modeled and implemented on the basis of a detailed analysis of the considered clinical environment, carried out by an object-oriented approach.