AI in Healthcare

Enhance clinical decision-making, improve patient outcomes, and optimize system efficiency

My research centers on applying artificial intelligence to solve critical challenges in healthcare (Ahmad et al., 2018) (Lammers et al., 2021) (Mahajan et al., 2013). I have worked on disease prediction, patient flow optimization, LLMs, and the use of AI in decision support systems in clinical settings. I have worked on predictive models for disease onset and progression e.g., pressure injury (Ahmad et al., 2021), diabetes (Lim et al., 2021). I have worked on fraud and waste analysis (Liu et al., 2018) in healthcare systems, where AI tools can identify inefficiencies and reduce unnecessary costs. My research on patient flow includes analyzing care deferral (Ahmad et al., 2022), risk of readmission (Eckert et al., 2019) pediatric intensive care units (Ahmad et al., 2021), optimization in emergency departments (Padthe et al., 2021), and other hospital environments (Eckert et al., 2018). These efforts aim to improve resource allocation, reduce patient wait times, and enhance overall care delivery across healthcare systems.

More recently, I have been working on the application of large language models in electronic health records (EHRs) to enhance insight generation, improve clinical outcomes, and improve mental health (Jaiswal et al., 2024) . Furthermore, I focus on predicting end-of-life scenarios (Ahmad et al., 2018) to better inform palliative care and patient management strategies. Another area of exploration is algorithmic nudging, where I investigate how subtle AI suggestions can improve health outcomes at scale for millions of patients. Another recent project is the use of combining machine learning and simulation modeing to build digital twins of hospital systems at scale (Ahmad et al., 2023).

References

2024

ACM BCB
Building Personality-Adaptive Conversational AI for Mental Health Therapy

Sugam Jaiswal, Joyce Lee, Joe Berria, and 4 more authors

In Proceedings of the 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, 2024

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Many people with mental health problems cannot get professional help for various reasons such as lack of awareness, unavailability, unaffordability, etc. A virtual conversational agent can offer an alternative to deliver mental health care that is accessible, affordable, and scalable. However, building such agents using a one-size-fits-all approach may not be effective for everyone, as different individuals have different personality types that dictate how they communicate with chatbots. Therefore, developing therapy chatbots that can adjust to the user’s personality is important. In this work, we present the important role of personality-adaptive conversational agents (PACAs) in the context of mental healthcare. We designed an architecture around traditional machine learning (ML) models and open-source large language models (LLMs) to build a PACA for mental health therapy, developed a working prototype based on it, and conducted a user study to conclude that personality-adaptiveness is indeed an important feature for mental health chatbots.
@inproceedings{jaiswal2024building, title = {Building Personality-Adaptive Conversational AI for Mental Health Therapy}, author = {Jaiswal, Sugam and Lee, Joyce and Berria, Joe and Tanikella, Raviteja and Zolyomi, Annuska and Ahmad, Muhammad Aurangzeb and Si, Dong}, booktitle = {Proceedings of the 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics}, pages = {1--1}, year = {2024}, url = {https://dl.acm.org/doi/abs/10.1145/3698587.3701489}, }

2023

ICHI
Validation of a Hospital Digital Twin with Machine Learning

Muhammad Aurangzeb Ahmad, Vijay Chickarmane, Farinaz Sabz Ali Pour, and 2 more authors

2023

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Recently there has been a surge of interest in developing Digital Twins of process flows in healthcare to better understand bottlenecks and areas of improvement. A key challenge is in the validation process. We describe a work in progress for a digital twin using an agent based simulation model for determining bed turnaround time for patients in hospitals. We employ a strategy using machine learning for validating the model and implementing sensitivity analysis.
@article{aurangzeb2023validation, title = {Validation of a Hospital Digital Twin with Machine Learning}, author = {Ahmad, Muhammad Aurangzeb and Chickarmane, Vijay and Pour, Farinaz Sabz Ali and Shariari, Nima and Dutta Roy, Taposh}, booktitle = {2023 IEEE 11th International Conference on Healthcare Informatics (ICHI)}, pages = {465--469}, year = {2023}, organization = {IEEE}, url = {https://arxiv.org/pdf/2303.04117}, }

2022

ArXiV
Machine Learning for Deferral of Care Prediction

Muhammad Aurangzeb Ahmad, Raafia Ahmed, Dr Steve Overman, and 3 more authors

arXiv preprint arXiv:2207.01485, 2022

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Care deferral is the phenomenon where patients defer or are unable to receive healthcare services, such as seeing doctors, medications or planned surgery. Care deferral can be the result of patient decisions, service availability, service limitations, or restrictions due to cost. Continual care deferral in populations may lead to a decline in population health and compound health issues leading to higher social and financial costs in the long term. [1]. Consequently, identification of patients who may be at risk of deferring care is important towards improving population health and reducing care total costs. Additionally, minority and vulnerable populations are at a greater risk of care deferral due to socioeconomic factors. In this paper, we (a) address the problem of predicting care deferral for well-care visits; (b) observe that social determinants of health are relevant explanatory factors towards predicting care deferral, and (c) compute how fair the models are with respect to demographics, socioeconomic factors and selected comorbidities. Many health systems currently use rules-based techniques to retroactively identify patients who previously deferred care. The objective of this model is to identify patients at risk of deferring care and allow the health system to prevent care deferrals through direct outreach or social determinant mediation.
@article{ahmad2022machine, title = {Machine Learning for Deferral of Care Prediction}, author = {Ahmad, Muhammad Aurangzeb and Ahmed, Raafia and Overman, Dr Steve and Campbell, Patrick and Stroum, Corinne and Karunakaran, Bipin}, journal = {arXiv preprint arXiv:2207.01485}, year = {2022}, }

2021

Annals of Surgery
A surgeon’s guide to machine learning

Daniel T Lammers, Carly M Eckert, Muhammad A Ahmad, and 2 more authors

Annals of Surgery Open, 2021

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Machine learning (ML) represents a collection of advanced data modeling techniques beyond the traditional statistical models and tests with which most clinicians are familiar. While a subset of artificial intelligence, ML is far from the science fiction impression frequently associated with AI. At its most basic, ML is about pattern finding, sometimes with complex algorithms. The advanced mathematical modeling of ML is seeing expanding use throughout healthcare and increasingly in the day-to-day practice of surgeons. As with any new technique or technology, a basic understanding of principles, applications, and limitations are essential for appropriate implementation. This primer is intended to provide the surgical reader an accelerated introduction to applied ML and considerations in potential research applications or the review of publications, including ML techniques.
@article{lammers2021surgeon, title = {A surgeon’s guide to machine learning}, author = {Lammers, Daniel T and Eckert, Carly M and Ahmad, Muhammad A and Bingham, Jason R and Eckert, Matthew J}, journal = {Annals of Surgery Open}, volume = {2}, number = {3}, pages = {e091}, year = {2021}, publisher = {LWW}, url = {https://journals.lww.com/aosopen/abstract/2021/09000/a_surgeon_s_guide_to_machine_learning.21.aspx}, }
IEEE ICHI
Machine learning approaches for pressure injury prediction

Muhammad Aurangzeb Ahmad, Barrett Larson, Steve Overman, and 5 more authors

In 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), 2021

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Pressure Injuries are localized damages to the skin caused by sustained pressure. It is a common yet preventable disease affecting millions of patients. While there are multiple scales to determine if a patient has pressure injury, these methods suffer from high inter-rater subjectivity. To address this problem we create predictive models for pressure injury using Centers for Medicare Medicaid Services claims data. The models show relatively good predictive performance, we also explore aspects of the model where they will be deployed in a real world clinical settings.
@inproceedings{ahmad2021machine, title = {Machine learning approaches for pressure injury prediction}, author = {Ahmad, Muhammad Aurangzeb and Larson, Barrett and Overman, Steve and Kumar, Vikas and Xie, Jing and Rossington, Alan and Patel, Ankur and Teredesai, Ankur}, booktitle = {2021 IEEE 9th International Conference on Healthcare Informatics (ICHI)}, pages = {427--431}, year = {2021}, organization = {IEEE}, url = {https://ieeexplore.ieee.org/abstract/document/9565729}, }
ArXiV
Machine Learning Approaches for Type 2 Diabetes Prediction and Care Management

Aloysius Lim, Ashish Singh, Jody Chiam, and 4 more authors

arXiv preprint arXiv:2104.07820, 2021

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Prediction of diabetes and its various complications has been studied in a number of settings, but a comprehensive overview of problem setting for diabetes prediction and care management has not been addressed in the literature. In this document we seek to remedy this omission in literature with an encompassing overview of diabetes complication prediction as well as situating this problem in the context of real world healthcare management. We illustrate various problems encountered in real world clinical scenarios via our own experience with building and deploying such models. In this manuscript we illustrate a Machine Learning (ML) framework for addressing the problem of predicting Type 2 Diabetes Mellitus (T2DM) together with a solution for risk stratification, intervention and management. These ML models align with how physicians think about disease management and mitigation, which comprises these four steps: Identify, Stratify, Engage, Measure.
@article{lim2021machine, title = {Machine Learning Approaches for Type 2 Diabetes Prediction and Care Management}, author = {Lim, Aloysius and Singh, Ashish and Chiam, Jody and Eckert, Carly and Kumar, Vikas and Ahmad, Muhammad Aurangzeb and Teredesai, Ankur}, journal = {arXiv preprint arXiv:2104.07820}, year = {2021}, url = {https://arxiv.org/pdf/2104.07820}, }
IEEE ICHI
Machine learning approaches for patient state prediction in pediatric ICUs

Muhammad Aurangzeb Ahmad, Eduardo Antonio Trujillo Rivera, Pollack MD Murray, and 3 more authors

In 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), 2021

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We consider the problem of characterizing and predicting the condition of pediatric patients in intensive care units (ICUs). This population is often typified by rapid changes in patient conditions which necessitate predictions that can capture transition in patient states. While the assessment of patient’s condition is currently usually done using domain based scoring systems, we employ machine learning models for predicting the state of the pediatric patient. Additionally, we explore how model explainability could affect the usage of predictive models in a real world settings.
@inproceedings{ahmad2021pediatric, title = {Machine learning approaches for patient state prediction in pediatric ICUs}, author = {Ahmad, Muhammad Aurangzeb and Rivera, Eduardo Antonio Trujillo and Murray, Pollack MD and Carly, Eckert MD and Anita, Patel MD and Teredesai, Ankur}, booktitle = {2021 IEEE 9th International Conference on Healthcare Informatics (ICHI)}, pages = {422--426}, year = {2021}, organization = {IEEE}, url = {https://www.computer.org/csdl/proceedings-article/ichi/2021/013200a422/1xIOO2BcHcY}, }
ArXiV
Emergency Department Optimization and Load Prediction in Hospitals

Karthik K Padthe, Vikas Kumar, Carly M Eckert, and 4 more authors

arXiv preprint arXiv:2102.03672, 2021

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Over the past several years, across the globe, there has been an increase in people seeking care in emergency departments (EDs). ED resources, including nurse staffing, are strained by such increases in patient volume. Accurate forecasting of incoming patient volume in emergency departments (ED) is crucial for efficient utilization and allocation of ED resources. Working with a suburban ED in the Pacific Northwest, we developed a tool powered by machine learning models, to forecast ED arrivals and ED patient volume to assist end-users, such as ED nurses, in resource allocation. In this paper, we discuss the results from our predictive models, the challenges, and the learnings from users’ experiences with the tool in active clinical deployment in a real world setting.
@article{padthe2021emergency, title = {Emergency Department Optimization and Load Prediction in Hospitals}, author = {Padthe, Karthik K and Kumar, Vikas and Eckert, Carly M and Mark, Nicholas M and Zahid, Anam and Ahmad, Muhammad Aurangzeb and Teredesai, Ankur}, journal = {arXiv preprint arXiv:2102.03672}, year = {2021}, url = {https://arxiv.org/pdf/2102.03672}, }

2019

ACI
Development and prospective validation of a machine learning-based risk of readmission model in a large military hospital

Carly Eckert, Neris Nieves-Robbins, Elena Spieker, and 8 more authors

Applied clinical informatics, 2019

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Thirty-day hospital readmissions are a quality metric for health care systems. Predictive models aim to identify patients likely to readmit to more effectively target preventive strategies. Many risk of readmission models have been developed on retrospective data, but prospective validation of readmission models is rare. To the best of our knowledge, none of these developed models have been evaluated or prospectively validated in a military hospital. The objectives of this study are to demonstrate the development and prospective validation of machine learning (ML) risk of readmission models to be utilized by clinical staff at a military medical facility and demonstrate the collaboration between the U.S. Department of Defense’s integrated health care system and a private company. We evaluated multiple ML algorithms to develop a predictive model for 30-day readmissions using data from a retrospective cohort of all-cause inpatient readmissions at Madigan Army Medical Center (MAMC).
@article{eckert2019development, title = {Development and prospective validation of a machine learning-based risk of readmission model in a large military hospital}, author = {Eckert, Carly and Nieves-Robbins, Neris and Spieker, Elena and Louwers, Tom and Hazel, David and Marquardt, James and Solveson, Keith and Zahid, Anam and Ahmad, Muhammad and Barnhill, Richard and others}, journal = {Applied clinical informatics}, volume = {10}, number = {02}, pages = {316--325}, year = {2019}, publisher = {Georg Thieme Verlag KG}, url = {https://www.thieme-connect.com/products/ejournals/html/10.1055/s-0039-1688553}, }

2018

ACM-BCB
Interpretable machine learning in healthcare

Muhammad Aurangzeb Ahmad, Carly Eckert, and Ankur Teredesai

Proceedings of the 2018 ACM international conference on bioinformatics, computational biology, and health informatics, 2018

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This tutorial extensively covers the definitions, nuances, challenges, and requirements for the design of interpretable and explainable machine learning models and systems in healthcare. We discuss many uses in which interpretable machine learning models are needed in healthcare and how they should be deployed. Additionally, we explore the landscape of recent advances to address the challenges model interpretability in healthcare and also describe how one would go about choosing the right interpretable machine learnig algorithm for a given problem in healthcare.
@article{ahmad2018interpretable, title = {Interpretable machine learning in healthcare}, author = {Ahmad, Muhammad Aurangzeb and Eckert, Carly and Teredesai, Ankur}, year = {2018}, journal = {Proceedings of the 2018 ACM international conference on bioinformatics, computational biology, and health informatics}, pages = {559--560}, url = {https://dl.acm.org/doi/abs/10.1145/3233547.3233667}, }
SIAM
Automatic detection of excess healthcare spending and cost variation in ACOs

Eric Liu, Muhammad A Ahmad, Carly Eckert, and 5 more authors

2018

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There are more than nine hundred Accountable Care Organizations (ACOs) in the United States, both in the public and private sector, serving millions of patients across the country in a process to transition from fee-for-service to a value-based-care model for healthcare delivery in an effort to contain expenditures. Identifying fraud, waste, and abuse resulting in superfluous expenditures associated with care delivery is central to the success of ACOs and for making the cost of healthcare sustainable. In theory, such expenditures should be easily identifiable with large amounts of historical data. However, to the best of our knowledge there is no data mining framework that systematically addresses the problem of identifying unwarranted variation in expenditures on high dimensional claims data using unsupervised machine learning techniques. In this paper we propose methods to uncover unwarranted variation in healthcare spending by automatically extracting reference groups of peerproviders from the data and then detecting high cost outliers within these groups. We demonstrate the utility of our proposed framework on datasets from a large ACO in the United States to successfully identify unwarranted variation in therapeutic procedures even in low cost claims that had previously gone unnoticed.
@misc{liu2018automatic, title = {Automatic detection of excess healthcare spending and cost variation in ACOs}, author = {Liu, Eric and Ahmad, Muhammad A and Eckert, Carly and Nascimento, Anderson and De Cock, Martine and Padthe, Karthik and Teredesai, Ankur and McKelvey, Greg}, year = {2018}, publisher = {SIAM}, }
Thorax
S45 predicting likelihood of emergency department admission prior to triage: utilising machine learning within a COPD cohort

C Eckert, M Ahmad, K Zolfaghar, and 3 more authors

2018

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Acute exacerbation of COPD is one of the commonest reasons for emergency department (ED) attendance and admission. Optimising ED patient flow requires early identification of patients needing inpatient care to initiate the admissions process, improve bed management and reduce ED length of stay. Stratifying COPD patients by likelihood of admission and length of stay at triage would potentially facilitate these and other operational efficiencies, such as targeting early supported discharge team review and COPD care bundle. We propose a machine learning (ML) based approach to predicting the need for admission from ED amongst a cohort of COPD patients.
@misc{eckert2018s45, title = {S45 predicting likelihood of emergency department admission prior to triage: utilising machine learning within a COPD cohort}, author = {Eckert, C and Ahmad, M and Zolfaghar, K and McKelvey, G and Carlin, C and Lowe, D}, year = {2018}, publisher = {BMJ Publishing Group Ltd}, url = {https://thorax.bmj.com/content/73/Suppl_4/A28.1}, }
IAAI
Death vs. data science: predicting end of life

Muhammad Ahmad, Carly Eckert, Greg McKelvey, and 3 more authors

In Proceedings of the AAAI Conference on Artificial Intelligence, 2018

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Death is an inevitable part of life and while it cannot be delayed indefinitely it is possible to predict with some certainty when the health of a person is going to deteriorate. In this paper, we predict risk of mortality for patients from two large hospital systems in the Pacific Northwest. Using medical claims and electronic medical records (EMR) data we greatly improve prediction for risk of mortality and explore machine learning models with explanations for end of life predictions. The insights that are derived from the predictions can then be used to improve the quality of patient care towards the end of life.
@inproceedings{ahmad2018death, title = {Death vs. data science: predicting end of life}, author = {Ahmad, Muhammad and Eckert, Carly and McKelvey, Greg and Zolfagar, Kiyana and Zahid, Anam and Teredesai, Ankur}, booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence}, volume = {32}, number = {1}, year = {2018}, }

2013

Patent
Systems and methods for programming implantable medical devices

Deepa Mahajan, Yanting Dong, and Muhammad A Ahmad

2013

US Patent 8,346,369

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Embodiments of the invention are directed to systems and methods for programming implantable medical devices, amongst other things. In an embodiment, the invention includes a method of programming an implantable medical device. The method can include gathering parameter data representing a set of previously programmed parameter values from a plurality of implanted medical devices. The method can further include performing association analysis on the parameter data to form a set of association rules. The method can further include suggesting parameter choices to a system user regarding a specific patient based on the set of association rules. In an embodiment, the invention can include a medical system including a server configured to perform association analysis on a set of data representing previously programmed parameter values from a plurality of implanted medical devices to derive a set of association rules. Other embodiments are also included herein.
@misc{mahajan2013systems, title = {Systems and methods for programming implantable medical devices}, author = {Mahajan, Deepa and Dong, Yanting and Ahmad, Muhammad A}, year = {2013}, publisher = {United States Patents}, note = {US Patent 8,346,369}, }