Venet Osmani

Fondazione Bruno Kessler | FBK

The NeuroArtP3 (NET-2018-12366666) is a multicenter study funded by the Italian Ministry of Health. The aim of the project is to identify the prognostic trajectories of Alzheimer's disease (AD), through the application of artificial intelligence (AI). In literature just few studies investigated the variables associated with cognitive worsening in A...

Background The burden of Parkinson Disease (PD) represents a key public health issue and it is essential to develop innovative and cost-effective approaches to promote sustainable diagnostic and therapeutic interventions. In this perspective the adoption of a P3 (predictive, preventive and personalized) medicine approach seems to be pivotal. The Ne...

Background Bias in medical practice is multifaceted, including treatment variations across race-ethnicity, unconscious bias in healthcare providers’ attitudes, and bias in clinical scores. However, far less is known about the potential racial bias in routinely collected, essential information in clinical decision-making, namely vital signs. Resear...

Representation bias in health data can lead to unfair decisions, compromising the generalisability of research findings and impeding underrepresented subpopulations from benefiting from clinical discoveries. Several approaches have been developed to mitigate representation bias, ranging from simple resampling methods, such as SMOTE, to recent appro...

Artificial intelligence (AI) is widely used in medical applications to support outcome prediction and treatment optimisation based on collected patient data. With the increasing use of AI in medical applications, there is a need to identify and address potential sources of bias that may lead to unfair decisions.There have been many reported cases o...

Introduction: Screening for liver fibrosis continues to rely on laboratory panels and non-invasive tests such as FIB-4-score and transient elastography. In this study, we evaluated the potential of machine learning (ML) methods to predict liver steatosis on abdominal ultrasound and liver fibrosis, namely the intermediate-high risk of advanced fibr...

Background Previous studies have been inconclusive about racial disparities in sepsis. This study evaluated the impact of ethnic background on management and outcome in sepsis and septic shock. Methods This analysis included 17,146 patients suffering from sepsis and septic shock from the multicenter eICU Collaborative Research Database. Generalize...

Severe hyperlactatemia (>10mmol/L) or impaired lactate metabolism are known to correlate with increased mortality. The maximum lactate concentration on day 1 of 10,724 septic patients from the eICU Collaborative Research Database was analyzed and patients were divided into three groups based on maximum lactate in the first 24 h (<5mmol/l; ≥5mmol/l...

Background The SI-CURA project (Soluzioni Innovative per la gestione del paziente e il follow up terapeutico della Colite UlceRosA) is an Italian initiative aimed at the development of artificial intelligence solutions to discriminate pathologies of different nature, including inflammatory bowel disease (IBD), namely Ulcerative Colitis (UC) and Cro...

Background COVID-19 remains a complex disease in terms of its trajectory and the diversity of outcomes rendering disease management and clinical resource allocation challenging. Varying symptomatology in older patients as well as limitation of clinical scoring systems have created the need for more objective and consistent methods to aid clinical d...

Occupational stress has a significant adverse effect on workers’ well-being, productivity, and performance and is becoming a major concern for both individual companies and the overall economy. To reduce negative consequences, early detection of stress is a key factor. In response several stress prediction methods have been proposed, whose primary...

Several approaches have been developed to mitigate algorithmic bias stemming from health data poverty, where minority groups are underrepresented in training datasets. Augmenting the minority class using resampling (such as SMOTE) is a widely used approach due to the simplicity of the algorithms. However, these algorithms decrease data variability...

Introduction Delirium occurrence is common and preventive strategies are resource intensive. Screening tools can prioritize patients at risk. Using machine learning, we can capture time and treatment effects that pose a challenge to delirium prediction. We aim to develop a delirium prediction model that can be used as a screening tool. Methods Fro...

Abstract Introduction Intoxications are common in intensive care units (ICUs). The number of causative substances is large, mortality usually low. This retrospective cohort study aims to characterize differences of intoxicated compared to general ICU patients, point out variations according to causative agents, as well as to highlight differences b...

A bstract Background racial bias has been shown to be present in clinical data, affecting patients unfairly based on their race, ethnicity and socio-economic status. This problem has the potential to be significantly exacerbated in the light of Artificial Intelligence-aided clinical decision making. We sought to investigate whether bias can be int...

Background: Mortality in sepsis remains high. Studies in small cohorts have shown that red cell distribution width (RDW) is associated with mortality. The aim of this study was to validate these findings in a large multi-centre cohort. Methods: We conducted this retrospective analysis of the multi-center eICU Collaborative Research Database in 1...

Optimal performance is desired for decision-making in any field with binary classifiers and diagnostic tests, however common performance measures lack depth in information. The area under the receiver operating characteristic curve (AUC) and the area under the precision recall curve are too general because they evaluate all decision thresholds incl...

Background The COVID-19 pandemic caused by SARS-CoV-2 is challenging health care systems globally. The disease disproportionately affects the elderly population, both in terms of disease severity and mortality risk. Objective The aim of this study was to evaluate machine learning\textendash based prognostication models for critically ill elderly CO...

Background The COVID-19 pandemic caused by SARS-CoV-2 is challenging health care systems globally. The disease disproportionately affects the elderly population, both in terms of disease severity and mortality risk. Objective The aim of this study was to evaluate machine learning–based prognostication models for critically ill elderly COVID-19 pati...

An important target for machine learning research is obtaining unbiased results, which require addressing bias that might be present in the data as well as the methodology. This is of utmost importance in medical applications of machine learning, where trained models should be unbiased so as to result in systems that are widely applicable, reliable...

Screening for colorectal cancer (CRC) continues to rely on colonoscopy and/or fecal occult blood testing since other (non-invasive) risk-stratification systems have not yet been implemented into European guidelines. In this study, we evaluate the potential of machine learning (ML) methods to predict advanced adenomas (AAs) in 5862 individuals parti...

Background Higher survival has been shown for overweight septic patients compared with normal or underweight patients in the past. This study aimed at investigating the management and outcome of septic ICU patients in different body mass index (BMI) categories in a large multicenter database. Methods In total, 16,612 patients of the eICU collabora...

An important target for machine learning research is obtaining unbiased results, which require addressing bias that might be present in the data as well as the methodology. This is of utmost importance in medical applications of machine learning, where trained models should be unbiased so as to result in systems that are widely applicable, reliable...

BACKGROUND The COVID-19 pandemic caused by SARS-CoV-2 is challenging health care systems globally. The disease disproportionately affects the elderly population, both in terms of disease severity and mortality risk. OBJECTIVE The aim of this study was to evaluate machine learning–based prognostication models for critically ill elderly COVID-19 pat...

Elevations in initially obtained serum lactate levels are strong predictors of mortality in critically ill patients. Identifying patients whose serum lactate levels are more likely to increase can alert physicians to intensify care and guide them in the frequency of tending the blood test. We investigate whether machine learning models can predict...

Stress assessment is a complex issue and numerous studies have examined factors that influence stress in working environments. Research studies have shown that monitoring individuals' behaviour parameters during daily life can also help assess stress levels. In this study, we examine assessment of work-related stress using features derived from sen...

Wastewater treatment plants use many sensors to control energy consumption and discharge quality. These sensors produce a vast amount of data which can be efficiently monitored by automatic systems. Consequently, several different statistical and learning methods are proposed in the literature which can automatically detect faults. While these meth...

Blood lactate concentration is a strong indicator of mortality risk in critically ill patients. While frequent lactate measurements are necessary to assess patient's health state, the measurement is an invasive procedure that can increase risk of hospital-acquired infections. For this reason we formally define the problem of lactate prediction as a...

Progress of machine learning in critical care has been difficult to track, in part due to absence of public benchmarks. Other fields of research (such as vision and NLP) have already established various competitions and benchmarks, whereas only recent availability of large clinical datasets has enabled the possibility of public benchmarks. Taking a...

Of the 2652 articles considered, 106 met the inclusion criteria. Review of the included papers resulted in identification of 43 chronic diseases, which were then further classified into 10 disease categories using ICD-10. The majority of studies focused on diseases of the circulatory system (n=38) while endocrine and metabolic diseases were fewest...

Evaluation of dressing activities is essential in the assessment of the performance of patients with psycho-motor impairments. However, the current practice of monitoring dressing activity (performed by the patients in front of the therapist) has a number of disadvantages when considering the personal nature of dressing activity as well as inconsis...

Background: Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the secondary use of electronic health records (EHRs), where patient data are analyzed to conduct clinical and translati...

BACKGROUND Worldwide, the burden of chronic diseases is growing, necessitating novel approaches that complement and go beyond evidence-based medicine. In this respect a promising avenue is the secondary use of Electronic Health Records (EHR) data, where clinical data are analysed to conduct basic and clinical and translational research. Methods bas...

Background. Availability of large amount of clinical data is opening up new research avenues in a number of fields. An exciting field in this respect is healthcare, where secondary use of healthcare data is beginning to revolutionize healthcare. Except for availability of Big Data, both medical data from healthcare institutions (such as EMR data) a...

Availability of large amount of clinical data is opening up new research avenues in a number of fields. An exciting field in this respect is healthcare, where secondary use of healthcare data is beginning to revolutionize healthcare. Except for availability of Big Data, both medical data from healthcare institutions (such as EMR data) and data gene...

Explosion of number of smartphone apps and their diversity has created a fertile ground to study behaviour of smartphone users. Patterns of app usage, specifically types of apps and their duration are influenced by the state of the user and this information can be correlated with the self-reported state of the users. The work in this paper is along...

Motor activity in physical and psychological stress exposure has been studied almost exclusively with self-assessment questionnaires and from reports that derive from human observer, such as verbal rating and simple descriptive scales. However, these methods are limited in objectively quantifying typical behaviour of stress. We propose to use accel...

We describe an innovative framework for prescription of personalised health apps by integrating Personal Health Records (PHR) with disease-specific mobile applications for managing medical conditions and the communication with clinical professionals. The prescribed apps record multiple variables including medical history enriched with innovative fe...

Electronic Health Records (EHR) have been one of the factors in transforming healthcare and health management by providing electronic access to information recorded on paper charts. However, increasing interest of patients to be actively involved in the management of their condition and their health has necessitated evolution of EHRs so as to accom...

We describe an innovative framework for prescription of personalised health apps by integrating Personal Health Records (PHR) with disease-specific mobile applications for managing medical conditions and the communication with clinical professionals. The prescribed apps record multiple variables including medical history enriched with innovative fe...

Objective: Stress at work is a significant occupational health concern. Recent studies have used various sensing modalities to model stress behaviour based on non-obtrusive data obtained from smartphones. However, when the data for a subject is scarce it becomes a challenge to obtain a good model. Methods: We propose an approach based on a combi...

Increase in workload across many organisations and consequent increase in occupational stress is negatively affecting the health of the workforce. Measuring stress and other human psychological dynamics is difficult due to subjective nature of selfreporting and variability between and within individuals. With the advent of smartphones it is now pos...

There is growing amount of scientific evidence that motor activity is the most consistent indicator of bipolar disorder. Motor activity includes several areas such as body movement, motor response time, level of psychomotor activity, and speech related motor activity. Studies of motor activity in bipolar disorder have typically used self-reported q...

Stress at work is a significant occupational health concern nowadays. Thus, researchers are looking to find comprehensive approaches for improving wellness interventions relevant to stress. Recent studies have been conducted for inferring stress in labour settings; they model stress behaviour based on non-obtrusive data obtained from smartphones. H...

hile moderate exposure to stress at work can act as productivity booster, prolonged exposure not only decreases productivity, but it can also lead to an array of health related problems. Therefore, monitoring stress levels and more importantly correlated stressors, becomes prerequisite for a productive workforce. Considering that verbal interaction...

An observational study with patients diagnosed with bipolar disorder investigates whether data from smartphone sensors can be used to recognize bipolar disorder episodes and detect behavior changes that can signal an onset of an episode using objective data.

Explosion of number of smartphone apps and their diversity has created a fertile ground to study behaviour of smartphone users. Patterns of app usage, specifically types of apps and their duration are influenced by the state of the user and this information can be correlated with the self-reported state of the users. The work in this paper is along...

Capabilities of smartphones can be utilised to monitor a range of aspects of users' behaviour. This has potential to affect a number of areas where users' behaviour is considered relevant information. Most notably, healthcare in general and mental health in particular are excellent candidates to utilise capabilities of smartphones, since mental dis...

Social interactions play an important role in the overall well-being. Current practice of monitoring social interactions through questionnaires and surveys is inadequate due to recall bias, memory dependence and high end-user effort. However, sensing capabilities of smart-phones can play a significant role in automatic detection of social interacti...

This paper proposes using mobile technologies to provide an insight into social context at workplace. It provides takeaways for extracting features that are relevant for interpreting social context and types of social interactions, formal or informal. Our approach uses mobile phones and accelerometers to detect interpersonal spatial and speech rela...

Telecommunication networks currently follow the reactive approach; that is, network conditions change based on current behaviour of users and state of network resources. However, network efficiency and resource optimization can increase manifold, if the networks had the capability to anticipate behaviour of users and their network utilization. This...

Today's health care is difficult to imagine without the possibility to objectively measure various physiological parameters related to patients symptoms (from temperature through blood pressure to complex tomographic procedures). Psychiatric care remains a notable exception that heavily relies on patient interviews and self assessment. This is due...

Bipolar Disorder is a disease that is manifested with cycling periods of polar episodes, namely mania and depression. Depressive episodes are manifested through disturbed mood, psychomotor retardation, behaviour change, decrease in energy levels and length of sleep. Manic episodes are manifested through elevated mood, psychomotor acceleration and i...

In this paper we demonstrate how smart phone sensors, specifically acceleration sensor and GPS traces, can be used as an objective “measurement device” for aiding psychiatric diagnosis. In a trial with 12 bipolar disorder patients con- ducted over a total (summed over all patients) of over 1000 days (on average 12 weeks per patient) we have achieve...

Mental disorders can have a significant, nega-tive impact on sufferers' lives, as well as on their friends and family, healthcare systems and other parts of society. Approximately 25 % of all people in Europe and the USA experience a mental disorder at least once in their lifetime. Currently, monitoring mental disorders relies on subjective clinica...

This paper presents a series of challenges for developing mobile health solutions for mental health as a result of MONARCA project three-year activities. The lessons learnt on the design, development and evaluation of a mobile health system for supporting the treatment of bipolar disorder. The findings presented here are the result of over 3 years...

This paper presents a series of challenges for developing mobile health solutions for mental health as a result of MONARCA project three-year activities. The lessons learnt on the design, development and evaluation of a mobile health system for supporting the treatment of bipolar disorder. The findings presented here are the result of over 3 years...

This paper presents a series of reflections on the use of experiences gained in ubiquitous computing research to define their potential exploitation in the field of networking. On this regard, this paper presents preliminary ideas aiming at defining the concept of Human Aware Networking (HAN). The main idea behind HAN is to utilize the sensed conte...

In this paper, we demonstrate how smart-phone sensors, specifically inertial sensors, and GPS traces, can be used as an objective “measurement device” for aiding psychiatric diagnosis. In a trial with 12 bipolar disorder patients conducted over a total (summed over all patients) of over 1000 days (on average 12 weeks per patient) we have achieved...

Mobile computing is changing the landscape of clinical monitoring and self-monitoring. One of the major impacts will be in healthcare, where increase in number of sensing modalities is providing more and more information on the state of overall wellbeing, behaviour and health. There are numerous applications of mobile computing that range from well...

In this paper, we present the concept of grouping individuals and detecting their proximity by emitting/receiving inaudible tones using their mobile phones. The inspiration stems from uniforms metaphor (of different colors) that groups subjects based on the roles, occupations or teams. The goal is to get an insight into the social context and socia...

Localization plays an essential role in many ubiquitous computing applications. While the outdoor location-aware services based on GPS are becoming increasingly popular, their proliferation to indoor environments is limited due to the lack of widely available indoor localization systems. The de-facto standard for indoor positioning is based on Wi-F...

Social interaction is one of the basic components of human life that impacts thoughts, emotions, decisions, and the overall wellbeing of individuals. In this regard, monitoring social activity constitutes an important factor in a number of disciplines, particularly those related to social and health sciences. Sensor-based social interaction data co...

In the area of human behaviour analysis, smartphones are opening new possibilities where a multitude of embedded sensors can be used to regularly monitor users’ daily activities and interactions in a non-obtrusive way. In this paper we focus on proximity detection, which refers to the ability of a system to recognize the co-location of two or more...

This Theme issue focuses on the emerging research of ubiquitous technologies to support mental health. So far, the majority of work presented in the field of ubiquitous healthcare has focused on supporting people affected by somatic diseases. However, increasing number of diseases affecting mental health has prompted research on technologies to sup...

The association between social relationships and psychological health has been established fairly recently, in the last 30-40 years, relying on survey-based methods to record past activities and the psychological responses in individuals. However, using the self-reporting methods for capturing social behavior exhibits a number of shortcomings inclu...

Equipment of mobile phones with various kinds of sensors is transforming these devices from mere capabilities of voice and internet access to devices capable of sensing a number of phenomena pertaining to their users. In this paper we make use of these capabilities of phones to detect social interactions between people and analyze social context by...

The level of social activity is linked to the overall wellbeing and to various disorders, including stress. In this regard, a myriad of automatic solutions for monitoring social interactions have been proposed, usually including audio data analysis. Such approaches often face legal and ethical issues and they may also raise privacy concerns in moni...