External and service factors influencing outcomes in a model of stratified mental health services for Young People

Background: Stratified care aims at matching the intensity and setting of mental health interventions to the needs of help-seeking Young People. In Australia, a 5-tiered system of mental health services is in operation. To aid patient triage to the most appropriate tier, a Decision Support Tool (DST) has been developed and is being rolled out nationally Methods: We analysed outcome data pre-and post-enrolment of about 1500 Young People (aged 16-25) referred to a Youth Mental Health Service delivering medium- and high intensity psychological treatment programs (tiers 3 and 4). We compared outcomes in both tiers during three 12-month periods: (a) in the inaugural phase of tier 4, prior to service saturation and stringent triaging, and prior to the COVID-19 pandemic (2019);(b) during the COVID-19 pandemic when all services were delivered remotely over phone- and video facilities, and when DST triaging was introduced (2020);(c) following return of face-to-face consultations, in a situation of service saturation and stringent DST triaging (2021) Findings: About 22% of Young People in the tier 3 program experienced reliable improvement according to their Kessler-10 (K-10) scale ratings, regardless of changing circumstances. In contrast, 40% of people in the tier 4 program reliably improved during the inaugural phase When circumstances and service delivery changed (COVID-19 restrictions service saturation, DST triaging), the rate of reliable improvement halved to about 20% Conclusion(s): Access to higher intensity psychological programs improves treatment outcomes for help-seeking Young People. However high-intensity services are more sensitive to external and service factors than less intense treatment models.

A Hybrid Deep Fused Learning Approach to Segregate Infectious Diseases

Humankind is facing another deadliest pandemic of all times in history, caused by COVID-19. Apart from this challenging pandemic, World Health Organization (WHO) considers tuberculosis (TB) as a preeminent infectious disease due to its high infection rate. Generally, both TB and COVID-19 severely affect the lungs, thus hardening the job of medical practitioners who can often misidentify these diseases in the current situation. Therefore, the time of need calls for an immediate and meticulous automatic diagnostic tool that can accurately discriminate both diseases. As one of the preliminary smart health systems that examine three clinical states (COVID-19, TB, and normal cases), this study proposes an amalgam of image filtering, data-augmentation technique, transfer learning-based approach, and advanced deep-learning classifiers to effectively segregate these diseases. It first employed a generative adversarial network (GAN) and Crimmins speckle removal filter on X-ray images to overcome the issue of limited data and noise. Each pre-processed image is then converted into red, green, and blue (RGB) and Commission Internationale de l'Elcairage (CIE) color spaces from which deep fused features are formed by extracting relevant features using DenseNet121 and ResNet50. Each feature extractor extracts 1000 most useful features which are then fused and finally fed to two variants of recurrent neural network (RNN) classifiers for precise discrimination of three-clinical states. Comparative analysis showed that the proposed Bi-directional long-short-term-memory (Bi-LSTM) model dominated the long-short-term-memory (LSTM) network by attaining an overall accuracy of 98.22% for the three-class classification task, whereas LSTM hardly achieved 94.22% accuracy on the test dataset.

ASSOCIATION RULE MINING FOR PREDICTION OF COVID-19

COVID-19 is a raging pandemic that has created havoc with its impact ranging from loss of millions of human lives to social and economic disruptions of the entire world. Therefore, error-free prediction, quick diagnosis, disease identification, isolation and treatment of a COVID patient have become extremely important. Nowadays, mining knowledge and providing scientific decision making for diagnosis of diseases from clinical datasets has found wide-ranging applications in healthcare sector. In this direction, among different data mining tools, association rule mining has already emerged out as a popular technique to extract invaluable information and develop important knowledge-base to help in intelligent diagnosis of distinct diseases quickly and automatically. In this paper, based on 5434 records of COVID cases collected from a popular data science community and using Rapid Miner Studio software, an attempt is put forward to develop a predictive model based on frequent pattern growth algorithm of association rule mining to determine the likelihood of COVID-19 in a patient. It identifies breathing problem, fever, dry cough, sore throat, abroad travel and attended large gathering as the main indicators of COVID-19. Employing the same clinical dataset, a linear regression model is also proposed having a moderately high coefficient of determination of 0.739 in accurately predicting the occurrence of COVID-19. A decision support system can also be developed using the association rules to ease out and automate early detection of other diseases. © 2023 by the authors.

Epidemic Analytics for Decision Supports in COVID19 Crisis

Covid-19 has hit the world unprepared, as the deadliest pandemic of the century. Governments and authorities, as leaders and decision makers fighting against the virus, enormously tap on the power of AI and its data analytics models for urgent decision supports at the greatest efforts, ever seen from human history. This book showcases a collection of important data analytics models that were used during the epidemic, and discusses and compares their efficacy and limitations. Readers who from both healthcare industries and academia can gain unique insights on how data analytics models were designed and applied on epidemic data. Taking Covid-19 as a case study, readers especially those who are working in similar fields, would be better prepared in case a new wave of virus epidemic may arise again in the near future. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

COVID-19 Bed Management Using a Two-Step Process Mining and Discrete-Event Simulation Approach

The sudden admission of many patients with similar needs caused by the COVID-19 (SARS-CoV-2) pandemic forced health care centers to temporarily transform units to respond to the crisis. This process greatly impacted the daily activities of the hospitals. In this paper, we propose a two-step approach based on process mining and discrete-event simulation for sizing a recovery unit dedicated to COVID-19 patients inside a hospital. A decision aid framework is proposed to help hospital managers make crucial decisions, such as hospitalization cancellation and resource sizing, taking into account all units of the hospital. Three sources of patients are considered: (i) planned admissions, (ii) emergent admissions representing day-to-day activities, and (iii) COVID-19 admissions. Hospitalization pathways have been modeled using process mining based on synthetic medico-administrative data, and a generic model of bed transfers between units is proposed as a basis to evaluate the impact of those moves using discrete-event simulation. A practical case study in collaboration with a local hospital is presented to assess the robustness of the approach. Note to Practitioners—In this paper we develop and test a new decision-aid tool dedicated to bed management, taking into account exceptional hospitalization pathways such as COVID-19 patients. The tool enables the creation of a dedicated COVID-19 intensive care unit with specific management rules that are fine-tuned by considering the characteristics of the pandemic. Health practitioners can automatically use medico-administrative data extracted from the information system of the hospital to feed the model. Two execution modes are proposed: (i) fine-tuning of the staffed beds assignment policies through a design of experiment and (ii) simulation of user-defined scenarios. A practical case study in collaboration with a local hospital is presented. The results show that our model was able to find the strategy to minimize the number of transfers and the number of cancellations while maximizing the number of COVID-19 patients taken into care was to transfer beds to the COVID-19 ICU in batches of 12 and to cancel appointed patients using ICU when the department hit a 90% occupation rate. IEEE

Rapid protocols to support COVID-19 clinical diagnosis based on hematological parameters

Purpose: In December 2019, the Covid-19 pandemic began in the world. To reduce mortality, in addiction to mass vaccination, it is necessary to massify and accelerate clinical diagnosis, as well as creating new ways of monitoring patients that can help in the construction of specific treatments for the disease. Objective: In this work, we propose rapid protocols for clinical diagnosis of COVID-19 through the automatic analysis of hematological parameters using evolutionary computing and machine learning. These hematological parameters are obtained from blood tests common in clinical practice. Method: We investigated the best classifier architectures. Then, we applied the particle swarm optimization algorithm (PSO) to select the most relevant attributes: serum glucose, troponin, partial thromboplastin time, ferritin, D-dimer, lactic dehydrogenase, and indirect bilirubin. Then, we assessed again the best classifier architectures, but now using the reduced set of features. Finally, we used decision trees to build four rapid protocols for Covid-19 clinical diagnosis by assessing the impact of each selected feature. The proposed system was used to support clinical diagnosis and assessment of disease severity in patients admitted to intensive and semi-intensive care units as a case study in the city of Paudalho, Brazil. Results: We developed a web system for Covid-19 diagnosis support. Using a 100-tree random forest, we obtained results for accuracy, sensitivity, and specificity superior to 99%. After feature selection, results were similar. The four empirical clinical protocols returned accuracies, sensitivities and specificities superior to 98%. Conclusion: By using a reduced set of hematological parameters common in clinical practice, it was possible to achieve results of accuracy, sensitivity, and specificity comparable to those obtained with RT-PCR. It was also possible to automatically generate clinical decision protocols, allowing relatively accurate clinical diagnosis even without the aid of the web decision support system. © 2023, The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering.

Development of a model of care based on the chronic care and collaborative care models to improve cardiometabolic health in low-to- middle income countries

Background: Non-communicable diseases (NCDs) are rising in low middle income countries (LMICs) mainly driven by cardiometabolic disease (cardiovascular disease, diabetes, and hypertension). Aim(s): To develop a model of care, based on the chronic care model and collaborative care model, to improve care, outcomes and risk factor control for adults with cardio metabolic disease in LMICs in the Covid-19 era. This will contribute to the sustainable development goals of promoting good health, well-being and reducing inequalities. Method(s): Using an iterative consultative approach with healthcare workers, clients, and community leaders in Kenya, Ghana and Mozambique, we developed a model of care, which includes core features from chronic care models: self-management support;decision support;clinical information systems;delivery system design;and community linkages. Result(s): We produced a culturally adapted self-management education programme, a training package for educators delivering the programme, as well as a training package for community and healthcare professional leaders to increase awareness and self-care for cardiometabolic disease. Given the lack of a robust health information system, we are offering a global registry to provide real world data on patient management and quality of care for people with type 2 diabetes, hypertension, heart failure and chronic kidney disease. Conclusion(s): This intervention will be tested in a mixed-methods single-arm feasibility study in five sites across three African countries: Kenya, Ghana, Mozambique.

Impact of Provider Nudges on Herpes Zoster Vaccine Series Completion during the COVID-19 Pandemic in the United States

Objectives: To determine the impact of a pharmacy-based, clinical decision support (CDS) tool on herpes zoster (HZ) vaccine series completion during the initial months of the COVID-19 pandemic across the US. Method(s): In partnership with Kroger Health, a pharmacy CDS tool alerted staff of patients due for their second HZ vaccine dose, which had been accompanied previously by a timed text message. Once operations changed due to COVID-19, the system limited outreach to only patients visiting the pharmacy. Primary outcomes included the proportion of patients receiving both doses within a Kroger-owned pharmacy (n=2,293) and the number of days between doses, both within and across two 32-week periods before and after the pandemic hit the US. Generalized estimating equation-based (GEE) logistic and linear regression models determined differences in completion rates and time to completion. Result(s): During the observation period, 38,937 adults received at least one HZ vaccine dose, with 77.2% receiving both doses. Patients engaged by the CDS tool achieved 80.5% dose completion, versus 65.4% of those not intervened (p<0.0001), which was lower than in the period immediately before the pandemic (85.8%, p<0.0001). The dosing window averaged 119.4 days (SD: 26.91), which was the longest timeframe between doses since the HZ vaccine was launched and nearly one month longer than before the pandemic (93.0 days [SD: 28.02], p<0.0001). The odds of dose completion increased in areas of higher health literacy (OR: 1.01;95% CI: 1.007-1.014), but decreased in areas of higher poverty (OR: 0.992;95% CI: 0.988-0.995). Time to completion was slightly shorter (B=-0.04, p<0.05) in areas of higher health literacy. Conclusion(s): Despite changes in clinical processes, a nationwide community pharmacy was successful in completing HZ vaccine dose series for adults during the pandemic, suggesting that processes in community pharmacies can protect staff while remaining committed to providing preventive health services during public health crises.Copyright © 2023

REVIEW AND ANALYSIS OF EXISTING METHODS TO ASSESS THE INDOOR ENVIRONMENTAL QUALITY (IEQ) FOR OFFICE BUILDINGS

Population in developed countries spend most of their time indoors, whether in their homes, workplaces, stores or leisure areas. Due to the COVID-19 pandemic, this situation worsened and now, more than ever, the importance of a high Indoor Environmental Quality (IEQ) is highlighted. The IEQ is very important in building performance since it is directly related to its occupants' comfort, health, wellbeing, and productivity and the Sick Building Syndrome (SBS) concept. Therefore, it is essential to develop tools to support designers' decision-making in the materialization of indoor environments with higher quality. From the state-of-art analysis, it is possible to conclude that the methods to assess the overall building performance already consider the IEQ. Still, most use an approach that does not cover all relevant indicators. In this context, this paper presents the first milestone of a research work that aims to develop a new method to rate the overall IEQ of office buildings in Portugal. The main objective of the present study is to propose a list of IEQ indicators for office buildings, adapted to the Portuguese context, based on the analysis of existing rating methods for buildings and the recommendations of national and international standards. © 2022 The Author(s). Licensed under a CC-BY 4.0 licence.

AI and Dynamic Prediction of Deterioration in Covid-19

During the COVID-19 pandemic it became evident that outcome prediction of patients is crucial for triaging, when resources are limited and enable early start or increase of available therapeutic support. COVID-19 demographic risk factors for severe disease and death were rapidly established, including age and sex. Common Clinical Decision Support Systems (CDSS) and Early Warning Systems (EWS) have been used to triage based on demographics, vital signs and laboratory results. However, all of these have limitations, such as dependency of laboratory investigations or set threshold values, were derived from more or less specific cohort studies. Instead, individual illness dynamics and patterns of recovery might be essential characteristics in understanding the critical course of illness.The pandemic has been a game changer for data, and the concept of real-time massive health data has emerged as one of the important tools in battling the pandemic. We here describe the advantages and limitations of established risk scoring systems and show how artificial intelligence applied on dynamic vital parameter changes, may help to predict critical illness, adverse events and death in patients hospitalized with COVID-19.Machine learning assisted dynamic analysis can improve and give patient-specific prediction in Clinical Decision Support systems that have the potential of reducing both morbidity and mortality. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Outcomes of a Pilot Contribution Database for the ICU Pharmacy Team at the University Hospitals of Leicester NHS Trust

Introduction It has long been felt that many contributions made by the ICU Pharmacy team, are not well showcased by the yearly regional network multi-speciality contributions audit. Themes specific to ICU are diluted amongst Trust and region wide data, and valuable learning for the multi-disciplinary team (MDT) is subsequently overlooked. Objective(s): The aims of this project were to: * Develop and pilot a MicrosoftTM Access © database for the ICU pharmacy team to record significant contributions. * Enable the production of reports to the ICU Quality & Safety board, to raise awareness, disseminate concerns, and influence future quality improvement projects. * Provide examples to contribute to the training of the whole MDT. * Generate evidence of team effectiveness and encourage further investment. * Provide team members with a means to recall contributions, for revalidation, appraisal, prescribing re-affirmation and framework mapping. Method(s): * A database was built with a user-friendly data-entry form to prevent overwriting. Fields were agreed with peers who would be using the database. * The team were invited to voluntarily enter their contributions which they thought added value and provided useful learning. * The pilot phase ceased with the emergence of the Omicron SARS-CoV-2 variant, due to staffing pressures and surge planning. Result(s): * Between 12/07/2021 and 25/11/2021, a total of 211 contributions were recorded. * Pharmacists entered 88.6% and a single technician entered 11.4% of these. * Independent Prescribing was utilised in 52.13% of contributions, and deprescribing in 25.12%. * Figure 1 demonstrates the contributions by drug group * The top 5 drugs associated with contributions were: ? Dalteparin ? Vancomycin ? Voriconazole ? Meropenem ? Co-trimoxazole * Treatment optimisation was an outcome for 76.3% of all contributions. Figure 2 stratifies these by type. Contributions. * Drug suitability was a cause for intervention in 12.8% of all contributions, encompassing allergies, contraindications, cautions and interactions and routes. * Medicines reconciliation accounted for 17.54% of all contributions, which almost half were Technician led. Admission was the most common stage to intervene (81.08%), followed by transcription. * Of all contributions, 37.91% were classified as patient safety incidents. Reassuringly 76.25% of these were prevented by the Pharmacy team. Themes have been extracted from these incidents and are presented in Table 1. Conclusion(s): PROTECTED-UK1 demonstrated the value pharmacists contribute to the quality and safety of patient care on ICU. Studies of similar quality and scale including Pharmacy Technicians are lacking, but even in this pilot study, it is evident how important their input is. Independent prescribing is a fundamental and well utilised part of our ICU Pharmacist skillset, supporting the GPICS2 recommendation that ICU pharmacists should be encouraged to become prescribers. Compiling a team interventions database is a useful tool to highlight local priority areas for guideline development;training;and ensuring that appropriate decision support is built into electronic prescribing systems. To improve the usefulness of the data, further stratification of contributions according to the Eadon Criteria3 may be worthwhile, to expand its use as a medication safety thermometer for ICU.

Hybrid manufacturing-remanufacturing system of essential commodity in the context of COVID-19

PurposeThis paper aims to assess the feasibility of a hybrid manufacturing and remanufacturing system (HMRS) for essential commodities in the context of COVID-19. Specifically, it emphasises using HMRS based on costs associated with various manufacturing activities.Design/methodology/approachThe combination of mathematical model and system dynamics is used to model the HMRS system. The model was tried on sanitiser bottle manufacturing to generalise the result.FindingsThe remanufacturing cost is higher because of reverse logistics, inspection and holding costs. Ultimately remanufacturing costs turn out to be lesser than the original manufacturing the moment system attains stability.Practical implicationsThe study put forth the reason to encourage remanufacturing towards sustainability through government incentives.Originality/valueThe study put forth the feasibility of the HMRS system for an essential commodity in the context of a covid pandemic. The research implemented system dynamics for modelling and validation.

Artificial intelligence and machine learning in clinical trial design and application

In 2020, COVID-19 changed how health care was approached both in the United States and globally. In the early phases, the vast majority of energy and attention was devoted to containing the pandemic and treating the infected. Toward the end of 2020, that attention expanded to vaccinating people across the globe. What was not being considered at the time were challenges to future health and clinical trials that power new treatments for COVID-19 and non-COVID-19 treatments. © 2023 Elsevier Inc. All rights reserved.

Effective medical center finding during COVID-19 pandemic using a spatial DSS centered on ontology engineering.

The global spread of the coronavirus has generated one of the most critical circumstances forcing healthcare systems to deal with it everywhere in the world. The complexity of crisis management, particularly in Iran, the unfamiliarity of the disease, and a lack of expertise, provided the foundation for researchers and implementers to propose innovative solutions. One of the most important obstacles in COVID-19 crisis management is the lack of information and the need for immediate and real-time data on the situation and appropriate solutions. Such complex problems fall into the category of semi-structured problems. In this respect, decision support systems use people's mental resources with computer capabilities to improve the quality of decisions. In synergetic situations, for instance, healthcare domains cooperating with spatial solutions, coming to a decision needs logical reasoning and high-level analysis. Therefore, it is necessary to add rich semantics to different classes of involved data, find their relationships, and conceptualize the knowledge domain. For the COVID-19 case in this study, ontologies allow for querying over such established relationships to find related medical solutions based on description logic. Bringing such capabilities to a spatial decision support system (SDSS) can help with better control of the COVID-19 pandemic. Ontology-based SDSS solution has been developed in this study due to the complexity of information related to coronavirus and its geospatial aspect in the city of Tehran. According to the results, ontology can rationalize different classes and properties about the user's clinical information, various medical centers, and users' priority. Then, based on the user's requests in a web-based SDSS, the system focuses on the inference made, advises the users on choosing the most related medical center, and navigates the user on a map. The ontology's capacity for reasoning, overcoming knowledge gaps, and combining geographic and descriptive criteria to choose a medical center all contributed to promising outcomes and the satisfaction of the sample community of evaluators.

Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review.

During the early phase of the COVID-19 pandemic, reverse transcriptase-polymerase chain reaction (RT-PCR) testing faced limitations, prompting the exploration of machine learning (ML) alternatives for diagnosis and prognosis. Providing a comprehensive appraisal of such decision support systems and their use in COVID-19 management can aid the medical community in making informed decisions during the risk assessment of their patients, especially in low-resource settings. Therefore, the objective of this study was to systematically review the studies that predicted the diagnosis of COVID-19 or the severity of the disease using ML. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), we conducted a literature search of MEDLINE (OVID), Scopus, EMBASE, and IEEE Xplore from January 1 to June 31, 2020. The outcomes were COVID-19 diagnosis or prognostic measures such as death, need for mechanical ventilation, admission, and acute respiratory distress syndrome. We included peer-reviewed observational studies, clinical trials, research letters, case series, and reports. We extracted data about the study's country, setting, sample size, data source, dataset, diagnostic or prognostic outcomes, prediction measures, type of ML model, and measures of diagnostic accuracy. Bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO), with the number CRD42020197109. The final records included for data extraction were 66. Forty-three (64%) studies used secondary data. The majority of studies were from Chinese authors (30%). Most of the literature (79%) relied on chest imaging for prediction, while the remainder used various laboratory indicators, including hematological, biochemical, and immunological markers. Thirteen studies explored predicting COVID-19 severity, while the rest predicted diagnosis. Seventy percent of the articles used deep learning models, while 30% used traditional ML algorithms. Most studies reported high sensitivity, specificity, and accuracy for the ML models (exceeding 90%). The overall concern about the risk of bias was "unclear" in 56% of the studies. This was mainly due to concerns about selection bias. ML may help identify COVID-19 patients in the early phase of the pandemic, particularly in the context of chest imaging. Although these studies reflect that these ML models exhibit high accuracy, the novelty of these models and the biases in dataset selection make using them as a replacement for the clinicians' cognitive decision-making questionable. Continued research is needed to enhance the robustness and reliability of ML systems in COVID-19 diagnosis and prognosis.

Scheduling Model for a Trigeneration System With Energy Storage Unit: A Hospital Application

Over the last three COVID-19 effective years, it was evident that healthcare has been the most sensitive sector to electricity failures. Therefore, if well developed and implemented, a microgrid system with an integrated energy storage system (ESS) installed in hospitals has great potential to provide an uninterrupted and low-energy cost solution. In this article, we target to show the importance of the installed ESS against the problems that will arise from power outages and energy quality problems in hospitals. Besides, it aims to construct an energy management system (EMS) based on the scheduling model to meet the lowest cost of a system containing solar panels, microturbine, gas boiler, and energy storage units that are repurposed lithium-ion batteries from electric vehicles and thermal storage tank. EMS is a mixed-integer linear program to meet the hospital's electricity, heating, and cooling demands with the lowest cost for every hour. The established scheduling model is run for a hospital in Antioch, Turkiye, with 197 beds, 4 operating rooms, 2 resuscitation units, and 9 intensive care units for every hour based on the data in 2019. With the EMS, approximately 25% savings were achieved compared to the previous energy cost. Furthermore, as the result of the net present value calculation, the payback period of the proposed system is estimated to be approximately seven years.

Logistics for Diagnostic Testing: an Adaptive Decision-support Framework

Diagnostic testing is a fundamental component in effective outbreak containment during every phase of a pandemic. Test samples are collected at testing facilities and subsequently analyzed at specialized laboratories. In high-income countries where health care providers are often privately owned, the assignments of samples from testing facilities to laboratories are determined by individual stakeholders. While this decentralized system effectively matches supply and demand during normal times, dispersed outbreaks, e.g., as encountered during the COVID-19 pandemic, lead to imbalanced requests for diagnostic capacity. With no coordinating entity in place to match demands at testing facilities to laboratory capacities, local backlogs build up rapidly thus increasing waiting times for test results and thus impeding subsequent containment efforts. To ease the impact of erratic regional outbreaks through improved logistics activities, we develop a rolling horizon framework which repeatedly solves a mathematical programming snapshot problem based on the current number of test samples. The procedure dynamically adapts to requirements resulting from the pandemic activity and supports rather than replaces decentralized operations in order to match testing requests with available laboratory capacities. We present problem-specific performance indicators and assess the quality of our procedure in a case study based on the COVID-19 outbreak in 2020 in Germany. Experimental results demonstrate the potential of coordinating mechanisms to support the logistics related to diagnostic testing and hence to reduce waiting times for PCR test results. Significant improvements are achieved even when interventions in the decentralized assignment process only occur in response to increased pandemic activity.

Explainable artificial intelligence (XAI) in medical decision systems (MDSSs): healthcare systems perspective

The healthcare sector is very interested in machine learning (ML) and artificial intelligence (AI). Nevertheless, applying AI applications in scientific contexts is difficult due to explainability issues. Explainable AI (XAI) has been studied as a potential remedy for the problems with current AI methods. The usage of ML with XAI may be capable of both explaining models and making judgments, in contrast to AI techniques like deep learning. Computer applications called medical decision support systems (MDSS) affect the decisions doctors make regarding certain patients at a specific moment. MDSS has played a crucial role in systems' attempts to improve patient safety and the standard of care, particularly for non-communicable illnesses. They have moreover been a crucial prerequisite for effectively utilizing electronic healthcare (EHRs) data. This chapter offers a broad overview of the application of XAI in MDSS toward various infectious diseases, summarizes recent research on the use and effects of MDSS in healthcare with regard to non-communicable diseases, and offers suggestions for users to keep in mind as these systems are incorporated into healthcare systems and utilized outside of contexts for research and development.

Exploring the topic structure and evolutionary trends of health informatics research in library and information science

PurposeThis study aims to reveal the topic structure and evolutionary trends of health informatics research in library and information science.Design/methodology/approachUsing publications in Web of Science core collection, this study combines informetrics and content analysis to reveal the topic structure and evolutionary trends of health informatics research in library and information science. The analyses are conducted by Pajek, VOSviewer and Gephi.FindingsThe health informatics research in library and information science can be divided into five subcommunities: health information needs and seeking behavior, application of bibliometrics in medicine, health information literacy, health information in social media and electronic health records. Research on health information literacy and health information in social media is the core of research. Most topics had a clear and continuous evolutionary venation. In the future, health information literacy and health information in social media will tend to be the mainstream. There is room for systematic development of research on health information needs and seeking behavior.Originality/valueTo the best of the authors' knowledge, this is the first study to analyze the topic structure and evolutionary trends of health informatics research based on the perspective of library and information science. This study helps identify the concerns and contributions of library and information science to health informatics research and provides compelling evidence for researchers to understand the current state of research.

PDSS: A Pharmacological Decision Support System for Diabetics Patients with COVID-19.

With the advent of SARS-CoV-2, several studies have shown that there is a higher mortality rate in patients with diabetes and, in some cases, it is one of the side effects of overcoming the disease. However, there is no clinical decision support tool or specific treatment protocols for these patients. To tackle this issue, in this paper we present a Pharmacological Decision Support System (PDSS) providing intelligent decision support for COVID-19 diabetic patient treatment selection, based on an analysis of risk factors with data from electronic medical records using Cox regression. The goal of the system is to create real world evidence including the ability to continuously learn to improve clinical practice and outcomes of diabetic patients with COVID-19.