Analytical Workflows to Unlock Predictive Power in Biotherapeutic Developability.

PURPOSE: Forming accurate data models that assist the design of developability assays is one area that requires a deep and practical understanding of the problem domain. We aim to incorporate expert knowledge into the model building process by creating new metrics from instrument data and by guiding the choice of input parameters and Machine Learning (ML) techniques. METHODS: We generated datasets from the biophysical characterisation of 5 monoclonal antibodies (mAbs). We explored combinations of techniques and parameters to uncover the ones that better describe specific molecular liabilities, such as conformational and colloidal instability. We also employed ML algorithms to predict metrics from the dataset. RESULTS: We found that the combination of Differential Scanning Calorimetry (DSC) and Light Scattering thermal ramps enabled us to identify domain-specific aggregation in mAbs that would be otherwise overlooked by common developability workflows. We also found that the response to different salt concentrations provided information about colloidal stability in agreement with charge distribution models. Finally, we predicted DSC transition temperatures from the dataset, and used the order of importance of different metrics to increase the explainability of the model. CONCLUSIONS: The new analytical workflows enabled a better description of molecular behaviour and uncovered links between structural properties and molecular liabilities. In the future this new understanding will be coupled with ML algorithms to unlock their predictive power during developability assessment.

Leaf aging of Amazonian canopy trees as revealed by spectral and physiochemical measurements.

Leaf aging is a fundamental driver of changes in leaf traits, thereby regulating ecosystem processes and remotely sensed canopy dynamics. We explore leaf reflectance as a tool to monitor leaf age and develop a spectra-based partial least squares regression (PLSR) model to predict age using data from a phenological study of 1099 leaves from 12 lowland Amazonian canopy trees in southern Peru. Results demonstrated monotonic decreases in leaf water (LWC) and phosphorus (Pmass ) contents and an increase in leaf mass per unit area (LMA) with age across trees; leaf nitrogen (Nmass ) and carbon (Cmass ) contents showed monotonic but tree-specific age responses. We observed large age-related variation in leaf spectra across trees. A spectra-based model was more accurate in predicting leaf age (R2  = 0.86; percent root mean square error (%RMSE) = 33) compared with trait-based models using single (R2  = 0.07-0.73; %RMSE = 7-38) and multiple (R2  = 0.76; %RMSE = 28) predictors. Spectra- and trait-based models established a physiochemical basis for the spectral age model. Vegetation indices (VIs) including the normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2), normalized difference water index (NDWI) and photosynthetic reflectance index (PRI) were all age-dependent. This study highlights the importance of leaf age as a mediator of leaf traits, provides evidence of age-related leaf reflectance changes that have important impacts on VIs used to monitor canopy dynamics and productivity and proposes a new approach to predicting and monitoring leaf age with important implications for remote sensing.

Energy poverty and the convergence hypothesis across EU member states.

Energy poverty is an emerging issue towards global affairs. Currently, the development of energy-related policies is becoming essential, with regard to new societies, social inclusion and social rights. In this paper, we examine the dynamic patterns of energy poverty among 27 EU member states between 2005 and 2020. We use the log-t regression test to investigate the convergence hypothesis, and the P&S data-driven algorithm to detect potential convergence clubs. The empirical results of energy poverty indicators are mixed, and the convergence hypothesis of the states is rejected. Instead, convergence clubs are exhibited, implying that groups of countries converge to different steady states in the long run. In view of the convergence clubs, we suggest that the affordability of heating services is potentially explained by structural conditions of housing, climate conditions and energy costs. Besides, the adverse financial and social conditions for the European households have significantly triggered the arrears on utility bills. Moreover, a significant proportion of households do not have basic sanitation services.

A Method of Estimating Time-to-Recovery for a Disease Caused by a Contagious Pathogen Such as SARS-CoV-2 Using a Time Series of Aggregated Case Reports.

During the outbreak of a disease caused by a pathogen with unknown characteristics, the uncertainty of its progression parameters can be reduced by devising methods that, based on rational assumptions, exploit available information to provide actionable insights. In this study, performed a few (~6) weeks into the outbreak of COVID-19 (caused by SARS-CoV-2), one of the most important disease parameters, the average time-to-recovery, was calculated using data publicly available on the internet (daily reported cases of confirmed infections, deaths, and recoveries), and fed into an algorithm that matches confirmed cases with deaths and recoveries. Unmatched cases were adjusted based on the matched cases calculation. The mean time-to-recovery, calculated from all globally reported cases, was found to be 18.01 days (SD 3.31 days) for the matched cases and 18.29 days (SD 2.73 days) taking into consideration the adjusted unmatched cases as well. The proposed method used limited data and provided experimental results in the same region as clinical studies published several months later. This indicates that the proposed method, combined with expert knowledge and informed calculated assumptions, could provide a meaningful calculated average time-to-recovery figure, which can be used as an evidence-based estimation to support containment and mitigation policy decisions, even at the very early stages of an outbreak.

Acceptance of Artificial Intelligence in Supporting Cancer Patients.

ASCAPE Project is a study aiming to implement the advances of Artificial Intelligence (AI), to support prostate cancer survivors, regarding quality of life issues. The aim of the study is to determine characteristics of patients who accepted to join ASCAPE project. It results that participants of the study mainly originate from higher-educated societies that are better informed about the potential benefits of AI in medicine. Therefore, efforts should be focused on eliminating patients' reluctancy by better informing them on the potential benefits of AI.

Training ChatGPT Models in Assisting Urologists in Daily Practice.

Artificial Intelligence (AI) has shown the ability to enhance the accuracy and efficiency of physicians. ChatGPT is an AI chatbot that can interact with humans through text, over the internet. It is trained with machine learning algorithms, using large datasets. In this study, we compare the performance of using a ChatGPT API 3.5 Turbo model to a general model, in assisting urologists in obtaining accurate, valid medical information. The API was accessed through a Python script that was applied specifically for this study based on 2023 EAU guidelines in PDF format. This custom-trained model leads to providing doctors with more precise, prompt answers about specific urologic subjects, thus helping them, ultimately, providing better patient care.

Measuring the impact of anonymization on real-world consolidated health datasets engineered for secondary research use: Experiments in the context of MODELHealth project.

Introduction: Electronic Health Records (EHRs) are essential data structures, enabling the sharing of valuable medical care information for a diverse patient population and being reused as input to predictive models for clinical research. However, issues such as the heterogeneity of EHR data and the potential compromisation of patient privacy inhibit the secondary use of EHR data in clinical research. Objectives: This study aims to present the main elements of the MODELHealth project implementation and the evaluation method that was followed to assess the efficiency of its mechanism. Methods: The MODELHealth project was implemented as an Extract-Transform-Load system that collects data from the hospital databases, performs harmonization to the HL7 FHIR standard and anonymization using the k-anonymity method, before loading the transformed data to a central repository. The integrity of the anonymization process was validated by developing a database query tool. The information loss occurring due to the anonymization was estimated with the metrics of generalized information loss, discernibility and average equivalence class size for various values of k. Results: The average values of generalized information loss, discernibility and average equivalence class size obtained across all tested datasets and k values were 0.008473 ± 0.006216252886, 115,145,464.3 ± 79,724,196.11 and 12.1346 ± 6.76096647, correspondingly. The values of those metrics appear correlated with factors such as the k value and the dataset characteristics, as expected. Conclusion: The experimental results of the study demonstrate that it is feasible to perform effective harmonization and anonymization on EHR data while preserving essential patient information.

Using Association Rules in Antimicrobial Resistance in Stone Disease Patients.

Association rule mining is a very popular unsupervised machine learning technique for discovering patterns in large datasets. Patients with stone disease commonly suffer from urinary tract infections (UTI), complicated by the emergence of antimicrobial resistance (AMR), due to the excessive use of antibiotics. In this study, we explore the use of association rule mining in the AMR profile of patients suffering from stone disease.

Cluster Analysis Assessment in Proposing a Surgical Technique for Benign Prostatic Enlargement.

Benign prostatic enlargement (BPE) is a common disease in men over 50 years old. The phenotype of patients with BPE is heterogenous, regarding both baseline patient characteristics and disease-related parameters. Treatment can be either medical-conservative or surgical. A great variety of surgical techniques are available for surgical management, with three of the most common being monopolar transurethral resection of the prostate (mTUR-P), bipolar transurethral resection of the prostate (bTUR-P), and bipolar transurethral vaporization of the prostate (bTUVis). The selection of each one of these depends on surgeon reasoning, equipment availability, patient characteristics, and preferences. Since all of these techniques are available in our Urology Department, and surgeons are skilled to perform each one of them, we performed a clustering analysis according to patient pre-operative characteristics, using the k-means algorithm, to compare clustering-related technique assignment with the real-life technique used.

VIVID: Independent Living of Aging Adults Suffered a Stroke.

Based on the recent statistics published by the Stroke Association (UK), first-time incidence of stroke occurs almost 17 million times a year worldwide (one every two seconds), making Stroke as the second cause of death in the world. By the age of 75, 1 in 5 women and 1 in 6 men will have a stroke, which is one of the largest causes of disability, as half of all stroke survivors have a disability, making those persons dependent on others (1 in 5 are cared for by family and/or friends). People living longer is a cause for celebration, but older people are more vulnerable to mental health, cognition and physical problems, especially if they have already experienced a stroke (minor or mild). Depression is a main condition after a stroke and may be experienced in the form of sadness, unexplained pains, loss of interest in socializing, weight loss etc. The abovementioned conditions reduce the person's ability to remain active and independent, affecting their well-being and quality of living. Independent living of aging adults that have suffered a stroke is the key motivation for the VIVID project.

A Smartphone Application Designed to Engage the Elderly in Home-Based Rehabilitation.

As life expectancy increases, it is imperative that the elderly take advantage of the benefits of technology to remain active and independent. Mobile health applications are widely used nowadays as they promote a healthy lifestyle and self-management of diseases, opening new horizons in the interactive health service delivery. However, adapting these applications to the needs and requirements of the elderly is still a challenge. This article presents a smartphone application that is part of a multifactorial intervention to support older people with balance disorders. The application aims to enable users to self-evaluate their activity and progress, to communicate with each other and, through strategically selected motivational features, to engage with the system with undiminished interest for a long period of time. Mock-up interfaces were evaluated in semi-structured focus groups and interviews that were performed across three European countries. Further evaluation in the form of four pilot studies with 160 participants will be performed and qualitative and quantitative measures will be used to process the feedback about the use of the application.

Data Quality Challenges in a Learning Health System.

This paper discusses the topic of data quality, which concerns the global research and business community and constitutes a challenging task. The data quality prerequisite becomes even more critical when it pertains to critical and sensitive data, such as the healthcare domain data. To begin with, the paper outlines the basic definitions and concepts of data quality and its dimensions. The related research work on data quality assessment is presented and our approach for data quality assurance is introduced. This approach is implemented in our designed cloud platform, called MODELHealth, which is intended for supporting clinical work and administrative decision-making process.

Automated classification of primary care patient safety incident report content and severity using supervised machine learning (ML) approaches.

Learning from patient safety incident reports is a vital part of improving healthcare. However, the volume of reports and their largely free-text nature poses a major analytic challenge. The objective of this study was to test the capability of autonomous classifying of free text within patient safety incident reports to determine incident type and the severity of harm outcome. Primary care patient safety incident reports (n=31333) previously expert-categorised by clinicians (training data) were processed using J48, SVM and Naïve Bayes.The SVM classifier was the highest scoring classifier for incident type (AUROC, 0.891) and severity of harm (AUROC, 0.708). Incident reports containing deaths were most easily classified, correctly identifying 72.82% of reports. In conclusion, supervised ML can be used to classify patient safety incident report categories. The severity classifier, whilst not accurate enough to replace manual processing, could provide a valuable screening tool for this critical aspect of patient safety.

MODELHealth: Facilitating Machine Learning on Big Health Data Networks.

MODELHealth is a platform that aims to facilitate the implementation of Machine Learning (ML) techniques on medical data in order to upgrade the delivery of healthcare services. MODELHealth platform is a "holistic" approach to the implementation of processes for the development and utilization of ML algorithms in many forms, including Neural Networks, and can be used to assist clinical work and administrative decision-making. It covers the entire lifecycle of these processes, from pumping, homogenization, anonymization, and enrichment of the initial data, to the final disposal of efficient algorithms through Application Program Interfaces for consumption by any authorized Information System.

Utilizing Key Item Method to Manage Musculoskeletal Disorders in a Hospital Workplace.

Management of musculoskeletal disorders (MSDs) is a necessity for the modern work environment. In hospitals, these disorders have a particularly high frequency among health care workers whose work entails lifting and transporting patients as well as washing, dressing and feeding them. This paper, presents an electronic application which is based on the method of basic items (KIM - Key Item Method) in order to reduce incidents of MSDs resulting from manual transport of loads in healthcare facilities. The sample consisted of 15 female hospital meal servers from Metaxa Hospital (Piraeus, Greece) in order to assess the activities of lifting, carrying, transporting, pushing and pulling of loads which are part of their daily work duties. The key requirement for the application was not only helping the risk assessment but also leading to targeted, easily applicable and low cost corrective measures. The results of this electronic tool application showed increased usability and benefits which were associated with the used database and the detailed information relatively to the corrective measures, such as training of the employees to change body posture, replacement of wheels on trolleys and redesigning of serving aisles which proved beneficial.

Leaf age effects on the spectral predictability of leaf traits in Amazonian canopy trees.

Recent work has shown that leaf traits and spectral properties change through time and/or seasonally as leaves age. Current field and hyperspectral methods used to estimate canopy leaf traits could, therefore, be significantly biased by variation in leaf age. To explore the magnitude of this effect, we used a phenological dataset comprised of leaves of different leaf age groups -developmental, mature, senescent and mixed-age- from canopy and emergent tropical trees in southern Peru. We tested the performance of partial least squares regression models developed from these different age groups when predicting traits for leaves of different ages on both a mass and area basis. Overall, area-based models outperformed mass-based models with a striking improvement in prediction observed for area-based leaf carbon (Carea) estimates. We observed trait-specific age effects in all mass-based models while area-based models displayed age effects in mixed-age leaf groups for Parea and Narea. Spectral coefficients and variable importance in projection (VIPs) also reflected age effects. Both mass- and area-based models for all five leaf traits displayed age/temporal sensitivity when we tested their ability to predict the traits of leaves of other age groups. Importantly, mass-based mature models displayed the worst overall performance when predicting the traits of leaves from other age groups. These results indicate that the widely adopted approach of using fully expanded mature leaves to calibrate models that estimate remotely-sensed tree canopy traits introduces error that can bias results depending on the phenological stage of canopy leaves. To achieve temporally stable models, spectroscopic studies should consider producing area-based estimates as well as calibrating models with leaves of different age groups as they present themselves through the growing season. We discuss the implications of this for surveys of canopies with synchronised and unsynchronised leaf phenology.

Machine-learning based identification of undiagnosed dementia in primary care: a feasibility study.

BACKGROUND: Up to half of patients with dementia may not receive a formal diagnosis, limiting access to appropriate services. It is hypothesised that it may be possible to identify undiagnosed dementia from a profile of symptoms recorded in routine clinical practice. AIM: The aim of this study is to develop a machine learning-based model that could be used in general practice to detect dementia from routinely collected NHS data. The model would be a useful tool for identifying people who may be living with dementia but have not been formally diagnosed. DESIGN & SETTING: The study involved a case-control design and analysis of primary care data routinely collected over a 2-year period. Dementia diagnosed during the study period was compared to no diagnosis of dementia during the same period using pseudonymised routinely collected primary care clinical data. METHOD: Routinely collected Read-encoded data were obtained from 18 consenting GP surgeries across Devon, for 26 483 patients aged >65 years. The authors determined Read codes assigned to patients that may contribute to dementia risk. These codes were used as features to train a machine-learning classification model to identify patients that may have underlying dementia. RESULTS: The model obtained sensitivity and specificity values of 84.47% and 86.67%, respectively. CONCLUSION: The results show that routinely collected primary care data may be used to identify undiagnosed dementia. The methodology is promising and, if successfully developed and deployed, may help to increase dementia diagnosis in primary care.

An Internet of Things platform architecture for supporting ambient assisted living environments.

Internet of Things (IoT) is the logical further development of today's Internet, enabling a huge amount of devices to communicate, compute, sense and act. IoT sensors placed in Ambient Assisted Living (AAL) environments, enable the context awareness and allow the support of the elderly in their daily routines, ultimately allowing an independent and safe lifestyle. The vast amount of data that are generated and exchanged between the IoT nodes require innovative context modeling approaches that go beyond currently used models. Current paper presents and evaluates an open interoperable platform architecture in order to utilize the technical characteristics of IoT and handle the large amount of generated data, as a solution to the technical requirements of AAL applications.

Post-processing for spectral coherence of magnetoencephalogram background activity: application to Alzheimer's disease.

Estimating the connectivity between magnetoencephalogram (MEG) signals provides an excellent opportunity to analyze whole brain functional integration across a spectrum of conditions from health to disease. For this purpose, spectral coherence has been used widely as an easy-to-interpret metric of signal coupling. However, a number of systematic effects may influence the estimations of spectral coherence and subsequent inferences about brain activity. In this pilot study, we focus on the potentially confounding effects of the field spread and the on-going dynamic temporal variability inherent in the signals. We propose two simple post-processing approaches to account for these: 1) a jack-knife procedure to account for the variance in the estimation of spectral coherence; and 2) a detrending technique to reduce its dependence on sensor proximity. We illustrate the effect of these techniques in the estimation of MEG spectral coherence in the α band for 36 patients with Alzheimer's disease and 26 control subjects.

Smart adaptable system for older adults' Daily Life Activities Management - The ABLE platform.

In this paper we propose a system (ABLE) that will act as the main platform for a number of low-cost, mature technologies that will be integrated in order to create a dynamically adaptive Daily Life Activities Management environment in order to facilitate the everyday life of senior (but not exclusively) citizens at home. While the main target group of ABLE's users is the ageing population its use can be extended to all people that are vulnerable or atypical in body, intellect or emotions and are categorized by society as disabled. The classes of assistive products that are well defined in the international standard, ISO9999 such as assistive products for personal medical treatment, personal care and protection, communication, information and reaction and for personal mobility, will be easily incorporated in our proposed platform. Furthermore, our platform could integrate and implement the above classes under several service models that will be analyzed further.