Explanipedia

Towards responsible artificial intelligence in healthcare—getting real about real-world data and evidence Open

Eileen Koski, Amar K. Das, Pei-Yun Hsueh, Anthony Solomonides, Amanda L. Joseph , et al. · 2025

Background The use of real-world data (RWD) in artificial intelligence (AI) applications for healthcare offers unique opportunities but also poses complex challenges related to interpretability, transparency, safety, efficacy, bias, equity…

A Proposed Statistical Approach for Conducting a Longitudinal Assessment of Circulating Tumor DNA Open

Christopher R. Pretz, Jiemin Liao, Caroline Weipert, Leslie Bucheit, Leylah Drusbosky , et al. · 2025

As circulating tumor DNA (ctDNA) may reflect cancer progression, understanding its temporal evolution can inform clinical decision-making in precision oncology. Temporal changes in ctDNA often exhibit complex patterns, varying significantl…

Biochemistry a non-diseases with environment editing model Open

Debabrata Das, Amar K. Das, Pamela Das, Saibal Kumar Das, Ranabir Das · 2024

Population health outcomes of blood-based screening for colorectal cancer in comparison to current screening modalities: insights from a discrete-event simulation model incorporating longitudinal adherence Open

Shaun P. Forbes, Elifnur Yay Donderici, Nicole Zhang, Behnam Sharif, Gabriel Tremblay , et al. · 2024

The CAN-SCREEN model demonstrates that amongst non-invasive CRC screening strategies, those with higher adherence yield more favorable health outcomes as measured by CRC deaths averted, CRC cases averted, and LYG.

Hospitalization Among Patients Treated With Molnupiravir: A Retrospective Study of Administrative Data Open

Girish Prajapati, Amar K. Das, Yezhou Sun, Eileen Fonseca · 2023

The majority of patients treated with molnupiravir in early 2022 had at least one risk factor for severe COVID-19 and had comedications that could require treatment modification or monitoring if given a ritonavir-containing regimen. Hospit…

Semantically enabling clinical decision support recommendations Open

Oshani Seneviratne, Amar K. Das, Shruthi Chari, Nkechinyere N. Agu, Sabbir M. Rashid , et al. · 2023

Background Clinical decision support systems have been widely deployed to guide healthcare decisions on patient diagnosis, treatment choices, and patient management through evidence-based recommendations. These recommendations are typicall…

Predicting Measles Outbreaks in the United States: Evaluation of Machine Learning Approaches Open

Boshu Ru, Stephanie Kujawski, Nelson Lee Afanador, Richard Baumgartner, Manjiri Pawaskar , et al. · 2023

Background Measles, a highly contagious viral infection, is resurging in the United States, driven by international importation and declining domestic vaccination coverage. Despite this resurgence, measles outbreaks are still rare events t…

Prediction of persistent chronic cough in patients with chronic cough using machine learning Open

Wansu Chen, Michael Schatz, Yichen Zhou, Fagen Xie, Vishal Bali , et al. · 2023

Introduction The aim of this study was to develop and validate prediction models for risk of persistent chronic cough (PCC) in patients with chronic cough (CC). This was a retrospective cohort study. Methods Two retrospective cohorts of pa…

Comorbidity Burden in Patients with Covid-19 Treated with Molnupiravir in the United States Open

Girish Prajapati, Amar K. Das, Yezhou Sun, Eileen Fonseca · 2022

Supporting diabetic self-management behaviors during the COVID-19 pandemic Open

Neha Upadhya, Amar K. Das, Morgan A. Foreman, Daniel Gruen, Jon Harris , et al. · 2022

This study provides a qualitative analysis of the effects of the COVID-19 pandemic on the nutritional and physical activity self-management of adults with Type 2 diabetes (T2D). We conducted semi-structured interviews with 21 adults with T…

Planning and Monitoring Equitable Clinical Trial Enrollment Using Goal Programming Open

Miao Qi, Amar K. Das, Kristin P. Bennett · 2022

Randomized clinical trial (RCT) studies are the gold standard for scientific evidence on treatment benefits to patients. RCT outcomes may not be generalizable to clinical practice if the trial population is not representative of the patien…

Predicting Measles Outbreaks in the United States: Evaluation of Machine Learning Approaches (Preprint) Open

Boshu Ru, Stephanie Kujawski, Nelson Lee Afanador, Richard Baumgartner, Manjiri Pawaskar , et al. · 2022

BACKGROUND Measles, a highly contagious viral infection, is resurging in the United States, driven by international importation and declining domestic vaccination coverage. Despite this resurgence, measles outbreaks are still rare events …

Computer vision syndrome, musculoskeletal, and stress-related problems among visual display terminal users in Nepal Open

Amar K. Das, Sangam Shah, Tara Ballav Adhikari, Basanta Sharma Paudel, Sanjit Sah , et al. · 2022

Background The use of computers and other Visual Display Terminal (VDT) screens is increasing in Nepal. However, there is a paucity of evidence on the prevalence of Computer Vision Syndrome (CVS) and other occupational health concerns amon…

Risk Factors for Persistent Chronic Cough During Consecutive Years: A Retrospective Database Analysis Open

Robert S. Zeiger, Michael Schatz, Yichen Zhou, Fagen Xie, Vishal Bali , et al. · 2022

Knowledge of the independent risk factors associated with PCC should aid clinicians in identifying such patients and improve their management.

Assessing the Association Between Network-based Provider Communities and Patient Mortality in the Medicare Population with Multiple Chronic Conditions. Open

Yoonyoung Park, Panagiotis D. Karampourniotis, Issa Sylla, Gigi Yuen-Reed, Amar K. Das · 2022

Understanding the complexity of care delivery and care coordination for patients with multiple chronic conditions is challenging. Network analysis can model the relationship between providers and patients to find factors associated with pa…

1344. Predicting Measles Outbreaks in the United States: Application of Different Modeling Approaches Open

Stephanie Kujawski, Boshu Ru, Amar K. Das, Nelson Lee Afanador, Richard Baumgartner , et al. · 2021

Background Although measles is still rare in the United States (U.S.), there have been recent resurgent outbreaks in the U.S. To improve the accuracy of prediction given the rarity of measles events, we used machine learning (ML) algorithm…

Quantifying representativeness in randomized clinical trials using machine learning fairness metrics Open

Miao Qi, Owen Cahan, Morgan A. Foreman, Daniel M. Gruen, Amar K. Das , et al. · 2021

Objective We help identify subpopulations underrepresented in randomized clinical trials (RCTs) cohorts with respect to national, community-based or health system target populations by formulating population representativeness of RCTs as a…

Quantifying representativeness in randomized clinical trials using machine learning fairness metrics Open

Miao Qi, Owen Cahan, Morgan A. Foreman, Daniel M. Gruen, Amar K. Das , et al. · 2021

Objective We formulate population representativeness of randomized clinical trials (RCTs) as a machine learning (ML) fairness problem, derive new representation metrics, and deploy them in visualization tools which help users identify subp…

An Audio Personal Health Library of Clinic Visit Recordings for Patients and Their Caregivers (HealthPAL): User-Centered Design Approach Open

Paul Barr, William Haslett, Michelle D Dannenberg, Lisa Oh, Glyn Elwyn , et al. · 2021

Background Providing digital recordings of clinic visits to patients has emerged as a strategy to promote patient and family engagement in care. With advances in natural language processing, an opportunity exists to maximize the value of v…

Comparison of Methods to Reduce Bias From Clinical Prediction Models of Postpartum Depression Open

Yoonyoung Park, Jianying Hu, Moninder Singh, Issa Sylla, Irene Dankwa‐Mullan , et al. · 2021

Clinical prediction models trained on potentially biased data may produce unfair outcomes on the basis of the chosen metrics. This study's results suggest that the performance varied depending on the model, outcome label, and method for re…

Natural Language Processing for Automated Annotation of Medication Mentions in Primary Care Visit Conversations Open

Craig H. Ganoe, Weiyi Wu, Paul Barr, William Haslett, Michelle D Dannenberg , et al. · 2021

Objectives The objective of this study is to build and evaluate a natural language processing approach to identify medication mentions in primary care visit conversations between patients and physicians. Materials and Methods Eight clinici…

An Audio Personal Health Library of Clinic Visit Recordings for Patients and Their Caregivers (HealthPAL): User-Centered Design Approach (Preprint) Open

Paul Barr, William Haslett, Michelle D Dannenberg, Lisa Oh, Glyn Elwyn , et al. · 2020

BACKGROUND Providing digital recordings of clinic visits to patients has emerged as a strategy to promote patient and family engagement in care. With advances in natural language processing, an opportunity exists to maximize the value of …

Explanation Ontology in Action: A Clinical Use-Case Open

Shruthi Chari, Oshani Seneviratne, Daniel M. Gruen, Morgan A. Foreman, Amar K. Das , et al. · 2020

We addressed the problem of a lack of semantic representation for user-centric explanations and different explanation types in our Explanation Ontology (https://purl.org/heals/eo). Such a representation is increasingly necessary as explain…

Evaluating artificial intelligence in medicine: phases of clinical research Open

Yoonyoung Park, Gretchen Purcell Jackson, Morgan A. Foreman, Daniel M. Gruen, Jianying Hu , et al. · 2020

Increased scrutiny of artificial intelligence (AI) applications in healthcare highlights the need for real-world evaluations for effectiveness and unintended consequences. The complexity of healthcare, compounded by the user- and context-d…

The Semantic Data Dictionary – An Approach for Describing and Annotating Data Open

Sabbir M. Rashid, Jamie P. McCusker, Paulo Pinheiro, Marcello Peixoto Bax, Henrique Santos , et al. · 2020

It is common practice for data providers to include text descriptions for each column when publishing data sets in the form of data dictionaries. While these documents are useful in helping an end-user properly interpret the meaning of a c…

Anonymizing Data for Privacy-Preserving Federated Learning Open

Olivia Choudhury, Aris Gkoulalas-Divanis, Theodoros Salonidis, Issa Sylla, Yoonyoung Park , et al. · 2020

Federated learning enables training a global machine learning model from data distributed across multiple sites, without having to move the data. This is particularly relevant in healthcare applications, where data is rife with personal, h…

Explanation Ontology: A Model of Explanations for User-Centered AI Open

Shruthi Chari, Oshani Seneviratne, Daniel M. Gruen, Morgan A. Foreman, Amar K. Das , et al. · 2020

Knowledge Extraction of Cohort Characteristics in Research Publications. Open

Jay D S Franklin, Shruthi Chari, Morgan A. Foreman, Oshani Seneviratne, Daniel M. Gruen , et al. · 2020

When healthcare providers review the results of a clinical trial study to understand its applicability to their practice, they typically analyze how well the characteristics of the study cohort correspond to those of the patients they see.…

Differential Privacy-enabled Federated Learning for Sensitive Health Data Open

Olivia Choudhury, Aris Gkoulalas-Divanis, Theodoros Salonidis, Issa Sylla, Yoonyoung Park , et al. · 2019

Leveraging real-world health data for machine learning tasks requires addressing many practical challenges, such as distributed data silos, privacy concerns with creating a centralized database from person-specific sensitive data, resource…

Hierarchical patient-centric caregiver network method for clinical outcomes study Open

Yoonyoung Park, Panagiotis D. Karampourniotis, Issa Sylla, Amar K. Das · 2019

In clinical outcome studies, analysis has traditionally been performed using patient-level factors, with minor attention given to provider-level features. However, the nature of care coordination and collaboration between caregivers (provi…

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