A novel hyperparameter search approach for accuracy and simplicity in disease prediction risk scoring.

OBJECTIVE: Develop a novel technique to identify an optimal number of regression units corresponding to a single risk point, while creating risk scoring systems from logistic regression-based disease predictive models. The optimal value of this hyperparameter balances simplicity and accuracy, yielding risk scores of small scale and high accuracy for patient risk stratification. MATERIALS AND METHODS: The proposed technique applies an adapted line search across all potential hyperparameter values. Additionally, DeLong test is integrated to ensure the selected value produces an accuracy insignificantly different from the best achievable risk score accuracy. We assessed the approach through two case studies predicting diabetic retinopathy (DR) within six months and hip fracture readmissions (HFR) within 30 days, involving cohorts of 90 400 diabetic patients and 18 065 hip fracture patients. RESULTS: Our scores achieve accuracies insignificantly different from those obtained by existing approaches, reaching AUROCs of 0.803 and 0.645 for DR and HFR predictions, respectively. Regarding the scale, our scores ranged 0-53 for DR and 0-15 for HFR, while scores produced by existing methods frequently spanned hundreds or thousands. DISCUSSION: According to the assessment, our risk scores offer simple and accurate predictions for diseases. Furthermore, our new DR score provides a competitive alternative to state-of-the-art risk scores for DR, while our HFR case study presents the first risk score for this condition. CONCLUSION: Our technique offers a generalizable framework for crafting precise risk scores of compact scales, addressing the demand for user-friendly and effective risk stratification tool in healthcare.

Evaluation of food waste treatment techniques using the complex q-rung orthopair fuzzy generalized TODIM method with weighted power geometric operator.

Food waste has received wide attention due to its hazardous environmental effects, such as soil, water, and air pollution. Evaluating food waste treatment techniques is imperative to realize environmental sustainability. This study proposes an integrated framework, the complex q-rung orthopair fuzzy-generalized TODIM (an acronym in Portuguese for interactive and multi-criteria decision-making) method with weighted power geometric operator, to assess the appropriate technique for food waste. The assessment of food waste treatment techniques can be divided into three phases: information processing, information fusion, and ranking alternatives. Firstly, the complex q-rung orthopair fuzzy set flexibly describes the information with periodic characteristics in the processing process with various parameters q. Then, the weighted power geometric operator is employed to calculate the weight of the expert and form the group evaluation matrix, in which the weight of each input rating depends upon the other input ratings. It can simulate the mutual support, multiplicative preferences, and interrelationship of experts. Next, the generalized TODIM method is employed to rank the food waste treatment techniques, considering experts' psychological characteristics and bounded behavior. Subsequently, a real-world application case examines the practicability of the proposed framework. Furthermore, the sensitivity analysis verifies the validity and stability of the presented framework. The comparative study highlights the effectiveness of this framework using the existing frameworks. According to the result, Anaerobic digestion (0.0043) has the highest priority among the considered alternatives, while Incineration (-0.0009) has the lowest.

A Bayesian belief network-based analytics methodology for early-stage risk detection of novel diseases.

During a pandemic, medical specialists have substantial challenges in discovering and validating new disease risk factors and designing effective treatment strategies. Traditionally, this approach entails several clinical studies and trials that might last several years, during which strict preventive measures are enforced to manage the outbreak and limit the death toll. Advanced data analytics technologies, on the other hand, could be utilized to monitor and expedite the procedure. This research integrates evolutionary search algorithms, Bayesian belief networks, and innovative interpretation techniques to provide a comprehensive exploratory-descriptive-explanatory machine learning methodology to assist clinical decision-makers in responding promptly to pandemic scenarios. The proposed approach is illustrated through a case study in which the survival of COVID-19 patients is determined using inpatient and emergency department (ED) encounters from a real-world electronic health record database. Following an exploratory phase in which genetic algorithms are used to identify a set of the most critical chronic risk factors and their validation using descriptive tools based on the concept of Bayesian Belief Nets, the framework develops and trains a probabilistic graphical model to explain and predict patient survival (with an AUC of 0.92). Finally, a publicly available online, probabilistic decision support inference simulator was constructed to facilitate what-if analysis and aid general users and healthcare professionals in interpreting model findings. The results widely corroborate intensive and expensive clinical trial research assessments.

Predicting and Mitigating Freshmen Student Attrition: A Local-Explainable Machine Learning Framework.

With the emergence of novel methods for improving machine learning (ML) transparency, traditional decision-support-focused information systems seem to need an upgrade in their approach toward providing more actionable insights for practitioners. Particularly, given the complex decision-making process of humans, using insights obtained from group-level interpretation of ML models for designing individual interventions may lead to mixed results. The present study proposes a hybrid ML framework by integrating established predictive and explainable ML approaches for decision support systems involving the prediction of human decisions and designing individualized interventions accordingly. The proposed framework is aimed at providing actionable insights for designing individualized interventions. It was showcased in the context of college students' attrition problem using a large and feature-rich integrated data set of freshman college students containing information about their demographics, educational, financial, and socioeconomic factors. A comparison of feature importance scores at the group- vs. individual-level revealed that while group-level insights might be useful for adjusting long-term strategies, using them as a one-size-fits-all strategy to design and implement individual interventions is subject to suboptimal outcomes.

An analytical approach to evaluate the impact of age demographics in a pandemic.

The time required to identify and confirm risk factors for new diseases and to design an appropriate treatment strategy is one of the most significant obstacles medical professionals face. Traditionally, this approach entails several clinical studies that may last several years, during which time strict preventative measures must be in place to contain the epidemic and limit the number of fatalities. Analytical tools may be used to direct and accelerate this process. This study introduces a six-state compartmental model to explain and assess the impact of age demographics by designing a dynamic, explainable analytics model of the SARS-CoV-2 coronavirus. An age-stratified mathematical model taking the form of a deterministic system of ordinary differential equations divides the population into different age groups to better understand and assess the impact of age on mortality. It also provides a more accurate and effective interpretation of the disease evolution, specifically in terms of the cumulative numbers of infected cases and deaths. The proposed Kermack-Mckendrick model is incorporated into a non-linear least-squares optimization curve-fitting problem whose optimized parameters are numerically obtained using the Levenberg-Marquard algorithm. The curve-fitting model's efficiency is proved by testing the age-stratified model's performance on three U.S. states: Connecticut, North Dakota, and South Dakota. Our results confirm that splitting the population into different age groups leads to better fitting and forecasting results overall as compared to those achieved by the traditional method, i.e., without age groups. By using comprehensive models that account for age, gender, and ethnicity, regional public health authorities may be able to avoid future epidemics from inflicting more fatalities and establish a public health policy that reduces the burden on the elderly population.

Relationship between electronic health records strategy and user satisfaction: a longitudinal study using clinicians' online reviews.

OBJECTIVE: We investigated how the electronic health records (EHRs) strategies concerning EHR sourcing and vendor switching impact user satisfaction over time. MATERIALS AND METHODS: This study used a novel longitudinal dataset created by scraping clinicians' Glassdoor.com reviews on 109 US health systems from 2012 to 2017 and combining it with the Healthcare Information and Management Systems Society (HIMSS) database. We performed sentiment analysis of clinician reviews to construct our main dependent variable, user satisfaction. Our main independent variables, EHR single sourcing and vendor switching, were constructed using the HIMSS database. RESULTS: Our fixed effects model showed that as health systems gain more experience with EHR, a single vendor sourcing strategy was associated with higher user satisfaction. Further, there was no significant impact of vendor switching on user satisfaction. CONCLUSION: This work adds to the current understanding of EHR-driven clinician burnout using a novel longitudinal dataset. We show how organizational-level EHR strategy can impact user satisfaction and that providers and EHR vendors can mine clinician reviews online to understand their evolving needs and sentiments.

An explanatory machine learning framework for studying pandemics: The case of COVID-19 emergency department readmissions.

One of the major challenges that confront medical experts during a pandemic is the time required to identify and validate the risk factors of the novel disease and to develop an effective treatment protocol. Traditionally, this process involves numerous clinical trials that may take up to several years, during which strict preventive measures must be in place to control the outbreak and reduce the deaths. Advanced data analytics techniques, however, can be leveraged to guide and speed up this process. In this study, we combine evolutionary search algorithms, deep learning, and advanced model interpretation methods to develop a holistic exploratory-predictive-explanatory machine learning framework that can assist clinical decision-makers in reacting to the challenges of a pandemic in a timely manner. The proposed framework is showcased in studying emergency department (ED) readmissions of COVID-19 patients using ED visits from a real-world electronic health records database. After an exploratory feature selection phase using genetic algorithm, we develop and train a deep artificial neural network to predict early (i.e., 7-day) readmissions (AUC = 0.883). Lastly, a SHAP model is formulated to estimate additive Shapley values (i.e., importance scores) of the features and to interpret the magnitude and direction of their effects. The findings are mostly in line with those reported by lengthy and expensive clinical trial studies.

An explanatory analytics framework for early detection of chronic risk factors in pandemics.

Timely decision-making in national and global health emergencies such as pandemics is critically important from various aspects. Especially, early identification of risk factors of contagious viral diseases can lead to efficient management of limited healthcare resources and saving lives by prioritizing at-risk patients. In this study, we propose a hybrid artificial intelligence (AI) framework to identify major chronic risk factors of novel, contagious diseases as early as possible at the time of pandemics. The proposed framework combines evolutionary search algorithms with machine learning and the novel explanatory AI (XAI) methods to detect the most critical risk factors, use them to predict patients at high risk of mortality, and analyze the risk factors at the individual level for each high-risk patient. The proposed framework was validated using data from a repository of electronic health records of early COVID-19 patients in the US. A chronological analysis of the chronic risk factors identified using our proposed approach revealed that those factors could have been identified months before they were determined by clinical studies and/or announced by the United States health officials.

A Machine Learning Framework for Assessing the Risk of Venous Thromboembolism in Patients Undergoing Hip or Knee Replacement.

Venous thromboembolism (VTE) is a well-recognized complication that is prevalent in patients undergoing major orthopedic surgery (e.g., total hip arthroplasty and total knee arthroplasty). For years, to identify patients at high risk of developing VTE, physicians have relied on traditional risk scoring systems, which are too simplistic to capture the risk level accurately. In this paper, we propose a data-driven machine learning framework to identify such high-risk patients before they undergo a major hip or knee surgery. Using electronic health records of more than 392,000 patients who undergone a major orthopedic surgery, and following a guided feature selection using the genetic algorithm, we trained a fully connected deep neural network model to predict high-risk patients for developing VTE. We identified several risk factors for VTE that were not previously recognized. The best FCDNN model trained using the selected features yielded an area under the ROC curve (AUC) of 0.873, which was remarkably higher than the best AUC obtained by including only risk factors previously known in the medical literature. Our findings suggest several interesting and important insights. The traditional risk scoring tables that are being widely used by physicians to identify high-risk patients are not considering a comprehensive set of risk factors, nor are they as powerful as cutting-edge machine learning methods in distinguishing low- from high-risk patients.

A critical analysis of COVID-19 research literature: Text mining approach.

OBJECTIVE: Among the stakeholders of COVID-19 research, clinicians particularly experience difficulty keeping up with the deluge of SARS-CoV-2 literature while performing their much needed clinical duties. By revealing major topics, this study proposes a text-mining approach as an alternative to navigating large volumes of COVID-19 literature. MATERIALS AND METHODS: We obtained 85,268 references from the NIH COVID-19 Portfolio as of November 21. After the exclusion based on inadequate abstracts, 65,262 articles remained in the final corpus. We utilized natural language processing to curate and generate the term list. We applied topic modeling analyses and multiple correspondence analyses to reveal the major topics and the associations among topics, journal countries, and publication sources. RESULTS: In our text mining analyses of NIH's COVID-19 Portfolio, we discovered two sets of eleven major research topics by analyzing abstracts and titles of the articles separately. The eleven major areas of COVID-19 research based on abstracts included the following topics: 1) Public Health, 2) Patient Care & Outcomes, 3) Epidemiologic Modeling, 4) Diagnosis and Complications, 5) Mechanism of Disease, 6) Health System Response, 7) Pandemic Control, 8) Protection/Prevention, 9) Mental/Behavioral Health, 10) Detection/Testing, 11) Treatment Options. Further analyses revealed that five (2,3,4,5, and 9) of the eleven abstract-based topics showed a significant correlation (ranked from moderate to weak) with title-based topics. CONCLUSION: By offering up the more dynamic, scalable, and responsive categorization of published literature, our study provides valuable insights to the stakeholders of COVID-19 research, particularly clinicians.

Derivation and Validation of Essential Predictors and Risk Index for Early Detection of Diabetic Retinopathy Using Electronic Health Records.

Diabetic retinopathy (DR) is a leading cause for blindness among working-aged adults. The growing prevalence of diabetes urges for cost-effective tools to improve the compliance of eye examinations for early detection of DR. The objective of this research is to identify essential predictors and develop predictive technologies for DR using electronic health records. We conducted a retrospective analysis on a derivation cohort with 3749 DR and 94,127 non-DR diabetic patients. In the analysis, an ensemble predictor selection method was employed to find essential predictors among 26 variables in demographics, duration of diabetes, complications and laboratory results. A predictive model and a risk index were built based on the selected, essential predictors, and then validated using another independent validation cohort with 869 DR and 6448 non-DR diabetic patients. Out of the 26 variables, 10 were identified to be essential for predicting DR. The predictive model achieved a 0.85 AUC on the derivation cohort and a 0.77 AUC on the validation cohort. For the risk index, the AUCs were 0.81 and 0.73 on the derivation and validation cohorts, respectively. The predictive technologies can provide an early warning sign that motivates patients to comply with eye examinations for early screening and potential treatments.

A novel diffusion-based model for estimating cases, and fatalities in epidemics: The case of COVID-19.

While the COVID-19 pandemic is still ongoing in a majority of countries, a wealth of literature published in reputable journals attempted to model the spread of the disease. A vast majority of these studies dealt with compartmental models such as susceptible-infected-recovered (SIR) model. Although these models are rather simple, intuitive, and insightful, we argue that they do not necessarily provide a good enough fit to the reported data, which are usually reported in the form of daily fatalities and cases during pandemics. This study proposes an alternative analytics approach that relies on diffusion models to predict the number of cases and fatalities in epidemics. After evaluating several of the well-known and widely used diffusion models in business literature, including ADBUDG, Gompertz, and Bass models, we developed and used a modified/improved version of the original Bass diffusion model to address the shortcomings of the ordinary compartmental models such as SIR and demonstrated its applicability on the portrayal of the COVID-19 pandemic incident data. The proposed model differentiates itself from other similar models by fitting the data without the need for preprocessing, requiring no initial conditions and assumptions, not involving in heavy parameterization, and also properly addressing the pressing issues such as undocumented cases, length of infectious or recovery periods.

No Place Like Home: Cross-National Data Analysis of the Efficacy of Social Distancing During the COVID-19 Pandemic.

BACKGROUND: In the absence of a cure in the time of a pandemic, social distancing measures seem to be the most effective intervention to slow the spread of disease. Various simulation-based studies have been conducted to investigate the effectiveness of these measures. While those studies unanimously confirm the mitigating effect of social distancing on disease spread, the reported effectiveness varies from 10% to more than 90% reduction in the number of infections. This level of uncertainty is mostly due to the complex dynamics of epidemics and their time-variant parameters. However, real transactional data can reduce uncertainty and provide a less noisy picture of the effectiveness of social distancing. OBJECTIVE: The aim of this paper was to integrate multiple transactional data sets (GPS mobility data from Google and Apple as well as disease statistics from the European Centre for Disease Prevention and Control) to study the role of social distancing policies in 26 countries and analyze the transmission rate of the coronavirus disease (COVID-19) pandemic over the course of 5 weeks. METHODS: Relying on the susceptible-infected-recovered (SIR) model and official COVID-19 reports, we first calculated the weekly transmission rate (ß) of COVID-19 in 26 countries for 5 consecutive weeks. Then, we integrated these data with the Google and Apple mobility data sets for the same time frame and used a machine learning approach to investigate the relationship between the mobility factors and ß values. RESULTS: Gradient boosted trees regression analysis showed that changes in mobility patterns resulting from social distancing policies explain approximately 47% of the variation in the disease transmission rates. CONCLUSIONS: Consistent with simulation-based studies, real cross-national transactional data confirms the effectiveness of social distancing interventions in slowing the spread of COVID-19. In addition to providing less noisy and more generalizable support for the idea of social distancing, we provide specific insights for public health policy makers regarding locations that should be given higher priority for enforcing social distancing measures.

Using predictive analytics to identify drug-resistant epilepsy patients.

Epilepsy is one of the most common brain disorders that greatly affects patients' quality of life and poses serious risks to their health. While the majority of the patients positively respond to the existing anti-epilepsy drugs, others who developed the refractory type of epilepsy show resistance against drug therapy and need to undergo advance treatments such as surgery. Given that identifying such patients is not a straightforward process and requires long courses of trial and error with anti-epilepsy drugs, this study aims at predicting those at-risk patients using clinical and demographic data obtained from electronic medical records. Specifically, the study employs several predictive analytics machine-learning methods, equipped with a novel approach for data balancing, to identify drug-resistant patients using their comorbidities and demographic information along with the initial epilepsy-related diagnosis made by their physician. The promising results we obtained highlight the potential use of machine-learning techniques in facilitating medical decisions and suggest the possibility of extending the proposed approach for developing a clinical decision support system for medical professionals.

Research Themes and Trends in Ten Top-Ranked Nephrology Journals: A Text Mining Analysis.

BACKGROUND: Nephrology research is expanding, and harnessing the much-needed information and data for the practice of evidence-based medicine is becoming more challenging. In this study, we used the natural language processing and text mining approach to mitigate some of these challenges. METHODS: We analyzed 17,412 abstracts from the top-10 nephrology journals over 10 years (2007-2017) by using latent semantic analysis and topic analysis. RESULTS: The analyses revealed 10 distinct topics (T) for nephrology research ranging from basic science studies, using animal modeling (T-1), to dialysis vascular access-related issues -(T-10). The trend analyses indicated that while the majority of topics stayed relatively stable, some of the research topics experienced increasing popularity over time such as studies focusing on mortality and survival (T-4) and Patient-related Outcomes and Perspectives of Clinicians (T-5). However, some research topics such as studies focusing on animal modeling (T-1), predictors of acute kidney injury, and dialysis access (T-10) exhibited a downward trend. CONCLUSION: Stakeholders of nephrology research may use these trends further to develop priorities and enrich the research agenda for the future.

Post-acute care referral in United States of America: a multiregional study of factors associated with referral destination in a cohort of patients with coronary artery bypass graft or valve replacement.

BACKGROUND: The use of post-acute care (PAC) for cardiovascular conditions is highly variable across geographical regions. Although PAC benefits include lower readmission rates, better clinical outcomes, and lower mortality, referral patterns vary widely, raising concerns about substandard care and inflated costs. The objective of this study is to identify factors associated with PAC referral decisions at acute care discharge. METHODS: This study is a retrospective Electronic Health Records (EHR) based review of a cohort of patients with coronary artery bypass graft (CABG) and valve replacement (VR). EHR records were extracted from the Cerner Health-Facts Data warehouse and covered 49 hospitals in the United States of America (U.S.) from January 2010 to December 2015. Multinomial logistic regression was used to identify associations of 29 variables comprising patient characteristics, hospital profiles, and patient conditions at discharge. RESULTS: The cohort had 14,224 patients with mean age 63.5 years, with 10,234 (71.9%) male and 11,946 (84%) Caucasian, with 5827 (40.96%) being discharged to home without additional care (Home), 5226 (36.74%) to home health care (HHC), 1721 (12.10%) to skilled nursing facilities (SNF), 1168 (8.22%) to inpatient rehabilitation facilities (IRF), 164 (1.15%) to long term care hospitals (LTCH), and 118 (0.83%) to other locations. Census division, hospital size, teaching hospital status, gender, age, marital status, length of stay, and Charlson comorbidity index were identified as highly significant variables (p- values < 0.001) that influence the PAC referral decision. Overall model accuracy was 62.6%, and multiclass Area Under the Curve (AUC) values were for Home: 0.72; HHC: 0.72; SNF: 0.58; IRF: 0.53; LTCH: 0.52, and others: 0.46. CONCLUSIONS: Census location of the acute care hospital was highly associated with PAC referral practices, as was hospital capacity, with larger hospitals referring patients to PAC at a greater rate than smaller hospitals. Race and gender were also statistically significant, with Asians, Hispanics, and Native Americans being less likely to be referred to PAC compared to Caucasians, and female patients being more likely to be referred than males. Additional analysis indicated that PAC referral practices are also influenced by the mix of PAC services offered in each region.

Examining the effect of prescription sequence on developing adverse drug reactions: The case of renal failure in diabetic patients.

OBJECTIVES: While the effect of medications in development of Adverse Drug Reactions (ADRs) have been widely studied in the past, the literature lacks sufficient coverage in investigating whether the sequence in which [ADR-prone] drugs are prescribed (and administered) can increase the chances of ADR development. The present study investigates this potential effect by applying emergent sequential pattern mining techniques to electronic health records. MATERIALS AND METHODS: Using longitudinal medication and diagnosis records from more than 377,000 diabetic patients, in this study, we assessed the possible effect of prescription sequences in developing acute renal failure as a prevalent ADR among this group of patients. Relying on emergent sequential pattern mining, two statistical case-control approaches were designed and employed for this purpose. RESULTS: The results taken from the two employed approaches (i.e. 76.7% total agreement and 68.4% agreement on the existence of some significant effect) provide evidence for the potential effect of prescription sequence on ADRs development evidenced by the discovery that certain sequential patterns occurred more frequently in one group of patients than the other. CONCLUSION: Given the significant effects shown by our data analyses, we believe that design and implementation of automated clinical decision support systems to constantly monitor patients' medication transactions (and the sequence in which they are administered) and make appropriate alerts to prevent certain possible ADRs, may decrease ADR occurrences and save lives and money.

Predicting and explaining inflammation in Crohn's disease patients using predictive analytics methods and electronic medical record data.

Crohn's disease is among the chronic inflammatory bowel diseases that impact the gastrointestinal tract. Understanding and predicting the severity of inflammation in real-time settings is critical to disease management. Extant literature has primarily focused on studies that are conducted in clinical trial settings to investigate the impact of a drug treatment on the remission status of the disease. This research proposes an analytics methodology where three different types of prediction models are developed to predict and to explain the severity of inflammation in patients diagnosed with Crohn's disease. The results show that machine-learning-based analytic methods such as gradient boosting machines can predict the inflammation severity with a very high accuracy (area under the curve = 92.82%), followed by regularized regression and logistic regression. According to the findings, a combination of baseline laboratory parameters, patient demographic characteristics, and disease location are among the strongest predictors of inflammation severity in Crohn's disease patients.

Medical informatics research trend analysis: A text mining approach.

The objective of this research is to identify major subject areas of medical informatics and explore the time-variant changes therein. As such it can inform the field about where medical informatics research has been and where it is heading. Furthermore, by identifying subject areas, this study identifies the development trends and the boundaries of medical informatics as an academic field. To conduct the study, first we identified 26,307 articles in PubMed archives which were published in the top medical informatics journals within the timeframe of 2002 to 2013. And then, employing a text mining -based semi-automated analytic approach, we clustered major research topics by analyzing the most frequently appearing subject terms extracted from the abstracts of these articles. The results indicated that some subject areas, such as biomedical, are declining, while other research areas such as health information technology (HIT), Internet-enabled research, and electronic medical/health records (EMR/EHR), are growing. The changes within the research subject areas can largely be attributed to the increasing capabilities and use of HIT. The Internet, for example, has changed the way medical research is conducted in the health care field. While discovering new medical knowledge through clinical and biological experiments is important, the utilization of EMR/EHR enabled the researchers to discover novel medical insight buried deep inside massive data sets, and hence, data analytics research has become a common complement in the medical field, rapidly growing in popularity.