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200 papers
Acceptability of pictographs as a novel patient identifier to improve patient safety in the neonatal intensive care unit.
Salmasian H, Erdei C, Applebaum JR … +10 more , Sharon D, Hannon K, Cuddyer D, Sawyer M, Steele T, Sheldon Y, Lehman IS, Schwartz JE, Chen A, Adelman J
AMIA Annu Symp Proc · 2024 · PMID 40417562
As part of a randomized controlled trial on the use of pictographs (images used in lieu of a patient photo) embedded in the electronic health record to reduce wrong-patient errors in the neonatal intensive care unit (NIC...
As part of a randomized controlled trial on the use of pictographs (images used in lieu of a patient photo) embedded in the electronic health record to reduce wrong-patient errors in the neonatal intensive care unit (NICU), we conducted a series of surveys of parents, providers and nurses in the NICU. Data from survey responses were thematically analyzed and categorized. We found that in all groups, there was very high awareness of the intended purpose of the pictographs; however, the perception of providers and nurses about the effectiveness of pictographs was not as strong. While several providers and nurses acknowledged that pictographs can or have helped them avoid wrong-patient errors when caring for multiple birth infants (such as twins), many nurses believed that their current practice of the use of two patient identifiers was sufficient, and pictographs were not useful. Parents reported that pictographs improved their experience of care.
Leveraging Cluster Causal Diagrams for Determining Causal Effects in Medicine.
Anand TV, Hripcsak G
AMIA Annu Symp Proc · 2024 · PMID 40417561
Causal inference, or the task of estimating the causal effect of an exposure or interventional variable on an outcome from an observational dataset, requires precise and rigorous methods, based on assumptions about the s...
Causal inference, or the task of estimating the causal effect of an exposure or interventional variable on an outcome from an observational dataset, requires precise and rigorous methods, based on assumptions about the system under study. Such assumptions can be articulated as a causal diagram, however use of this technique in medicine is uncommon due to challenges in causal diagram construction in high-dimensional settings. Recent introduction of cluster causal diagrams or C-DAGs promise to ease the task of diagram construction by allowing for the representation of some unknown or partially defined relationships. We evaluate the practical application of C-DAGs in simulated medical contexts. We estimate causal effects under varying sets of assumptions, determined by both causal diagrams and C-DAGs and compare our results. Our findings show empirically similar results, with little discrepancy between causal effect sizes or variance across experimental runs, although estimation and efficiency challenges remain to be explored.
Exploring the use of Artificial Genomes for Genome-wide Association Studies through the lens of Utility and Privacy.
Wang X, Min S, Vaidya J
AMIA Annu Symp Proc · 2024 · PMID 40417560
Collaborative Genome-wide association studies (GWAS) have the potential to uncover rare genetic variant-trait associations by leveraging larger datasets and diverse population samples. Despite this potential, privacy con...
Collaborative Genome-wide association studies (GWAS) have the potential to uncover rare genetic variant-trait associations by leveraging larger datasets and diverse population samples. Despite this potential, privacy concerns and cumbersome review processes for data validation and collaborator selection hinder their broader implementation. Advances in generative models present a possible solution by generating synthetic datasets that closely resemble real genomic data, thus enhancing privacy and expediting the review process. This study assesses the capability of deep generative models to produce artificial genomic data for GWAS applications. We evaluate two state-of-the-art models on real-world datasets, identifying significant limitations in their ability to generate high-quality artificial genomes. Furthermore, we demonstrate that prevailing privacy measures, mainly based on membership inference attacks, are inadequate for providing insightful privacy evaluations. Our findings highlight the critical challenges and suggest future directions for the effective use of artificial genomes in GWAS.
Derivation and Experimental Performance of Standard and Novel Uncertainty Calibration Techniques.
Brown KE, Talbert S, Talbert DA
AMIA Annu Symp Proc · 2024 · PMID 40417559
To aid in the transparency of state-of-the-art machine learning models, there has been considerable research performed in uncertainty quantification (UQ). UQ aims to quantify what a model does not know by measuring varia...
To aid in the transparency of state-of-the-art machine learning models, there has been considerable research performed in uncertainty quantification (UQ). UQ aims to quantify what a model does not know by measuring variation of the model under stochastic conditions and has been demonstrated to be a potentially powerful tool for medical AI. Evaluation of UQ, however, is largely constrained to visual analysis. In this work, we expand upon the Rejection Classification Index (RC-Index) and introduce the relative RC-Index as measures of uncertainty based on rejection classification curves. We hypothesize that rejection classification curves can be used as a basis to derive a metric of how well a given arbitrary uncertainty quantification metric can identify potentially incorrect predictions by an ML model. We compare RC-Index and rRC-Index to established measures based on lift curves.
Neural Mosaics: Detecting Aberrant Brain Interactions using Algebraic Topology and Generative Artificial Intelligence.
Prantzalos K, Upadhyaya D, Golnari P … +9 more , Fernandez-BacaVaca G, Aispuro GP, Salehizadeh S, Thyagaraj S, Gurski N, Yoshimoto K, Sivagnanam S, Majumdar A, Sahoo SS
AMIA Annu Symp Proc · 2024 · PMID 40417558
Epilepsy affects over 50 million persons worldwide, with less than 50% achieving long-term success following surgery. Traditional electrophysiology signal-based seizure detection methods are resource-intensive, laborious...
Epilepsy affects over 50 million persons worldwide, with less than 50% achieving long-term success following surgery. Traditional electrophysiology signal-based seizure detection methods are resource-intensive, laborious, and overlook multifocal brain interactions. Algebraic topology methods, particularly persistent homology, offer robust representations of complex brain interaction patterns. Leveraging persistent homology and the Google Gemini Pro Vision 1.0 large language model (LLM), we present a novel prompting template to classify topological structures computed from intracranial electroencephalography (iEEG) recordings from refractory epilepsy patients. This study marks the first use of persistence diagrams as input to a LLM for analyzing brain interaction dynamics. Our results indicate that simply prompting LLMs with persistence diagrams is insufficient for accurate seizure detection. Nonetheless, unlike traditional approaches using machine learning algorithms for EEG classification, our approach does not require large volumes of representative training data or brittle hyperparameter tuning, which highlights the promise of more scalable analyses in the future.
Towards Optimizing LLM Use in Healthcare: Identifying Patient Questions in MyChart Messages.
Chekuri A, Johal AS, Allen MR … +3 more , Ayers JW, Hogarth M, Farcas E
AMIA Annu Symp Proc · 2024 · PMID 40417557
The volume of patient-provider messages is on the rise, and Large Language Models (LLMs) can potentially streamline the clinical messaging process, but their success hinges on triaging messages they can optimally address...
The volume of patient-provider messages is on the rise, and Large Language Models (LLMs) can potentially streamline the clinical messaging process, but their success hinges on triaging messages they can optimally address. In this study, we analyzed Electronic Health Records with over 4 million messages exchanged between patients and providers to characterize the utility of using LLMs for messages containing knowledge questions. We implemented a rule-based Syntactic Question Detector as a triage tool, and we evaluated it on 500 messages. The interrater reliability metrics and comparison with LLMs show the difficulty of detecting questions due to the informal text and implicit requests. Our results show that 25% of MyChart messages with questions do not have a response from the clinical team. This paper provides insights into the challenges of real-world data, highlights the importance and non-triviality of detecting questions, and suggests a pipeline for LLM use in healthcare.
Generative AI Demonstrated Difficulty Reasoning on Nursing Flowsheet Data.
Diamond CJ, Thate J, Withall JB … +3 more , Lee RY, Cato K, Rossetti SC
AMIA Annu Symp Proc · 2024 · PMID 40417556
Excessive documentation burden is linked to clinician burnout, thus motivating efforts to reduce burden. Generative artificial intelligence (AI) poses opportunities for burden reduction but requires rigorous assessment....
Excessive documentation burden is linked to clinician burnout, thus motivating efforts to reduce burden. Generative artificial intelligence (AI) poses opportunities for burden reduction but requires rigorous assessment. We evaluated the ability of a large language model (LLM) (OpenAI's GPT-4) to interpret various intervention-response relationships presented on nursing flowsheets, assessing performance using MUC-5 evaluation metrics, and compared its assessments to those of nurse expert evaluators. ChatGPT correctly assessed 3 of 14 clinical scenarios, and partially correctly assessed 6 of 14, frequently omitting data from its reasoning. Nurse expert evaluators correctly assessed all relationships and provided additional language reflective of standard nursing practice beyond the intervention-response relationships evidenced in nursing flowsheets. Future work should ensure the training data used for electronic health record (EHR)-integrated LLMs includes all types of narrative nursing documentation that reflect nurses' clinical reasoning, and verification of LLM-based information summarization does not burden end-users.
BadCLM: Backdoor Attack in Clinical Language Models for Electronic Health Records.
Lyu W, Bi Z, Wang F … +1 more , Chen C
AMIA Annu Symp Proc · 2024 · PMID 40417555
The advent of clinical language models integrated into electronic health records (EHR) for clinical decision support has marked a significant advancement, leveraging the depth of clinical notes for improved decision-maki...
The advent of clinical language models integrated into electronic health records (EHR) for clinical decision support has marked a significant advancement, leveraging the depth of clinical notes for improved decision-making. Despite their success, the potential vulnerabilities of these models remain largely unexplored. This paper delves into the realm of backdoor attacks on clinical language models, introducing an innovative attention-based backdoor attack method, BadCLM (Bad Clinical Language Models). This technique clandestinely embeds a backdoor within the models, causing them to produce incorrect predictions when a pre-defined trigger is present in inputs, while functioning accurately otherwise. We demonstrate the efficacy of BadCLM through an in-hospital mortality prediction task with MIMIC III dataset, showcasing its potential to compromise model integrity. Our findings illuminate a significant security risk in clinical decision support systems and pave the way for future endeavors in fortifying clinical language models against such vulnerabilities.
Health Related Social Needs Screening and Referral Fulfillment: Toward a Complex Model.
Sockolow P
AMIA Annu Symp Proc · 2024 · PMID 40417554
Health Related Social Needs (HRSN) is an important driver of patient health outcomes. Healthcare organizations address patient HRSN with screening and community resource referral fulfillment (S&RF) processes for which th...
Health Related Social Needs (HRSN) is an important driver of patient health outcomes. Healthcare organizations address patient HRSN with screening and community resource referral fulfillment (S&RF) processes for which they lack patient retention data, due to information silos. The process is complex and not fully represented in available conceptual models nor adequately assessed for effectiveness. The objective was to develop an evidence-based HRSN S&RF complex model and identify patient retention parameters. Model development drew from the literature and expert input to create a complex S&RF model, and identify parameters for model stages and factors. Studies (50) involved manual S&RF processes in small, specialized populations. The model organized 88 factors among five S&RF stages. Half the studies reported parameters, for which stage and factor ranges were wide and indicated reduced patient retention along the process. Needed is data from routine care in which HRSN platforms are used, and information silos overcome.
Optimizing Large Language Models for Discharge Prediction: Best Practices in Leveraging Electronic Health Record Audit Logs.
Zhang X, Yan C, Yang Y … +4 more , Li Z, Feng Y, Malin BA, Chen Y
AMIA Annu Symp Proc · 2024 · PMID 40417553
Electronic Health Record (EHR) audit log data are increasingly utilized for clinical tasks, from workflow modeling to predictive analyses of discharge events, adverse kidney outcomes, and hospital readmissions. These dat...
Electronic Health Record (EHR) audit log data are increasingly utilized for clinical tasks, from workflow modeling to predictive analyses of discharge events, adverse kidney outcomes, and hospital readmissions. These data encapsulate user-EHR interactions, reflecting both healthcare professionals' behavior and patients' health statuses. To harness this temporal information effectively, this study explores the application of Large Language Models (LLMs) in leveraging audit log data for clinical prediction tasks, specifically focusing on discharge predictions. Utilizing a year's worth of EHR data from Vanderbilt University Medical Center, we fine-tuned LLMs with randomly selected 10,000 training examples. Our findings reveal that LLaMA-2 70B, with an AUROC of 0.80 [0.77-0.82], outperforms both GPT-4 128K in a zero-shot, with an AUROC of 0.68 [0.65-0.71], and DeBERTa, with an AUROC of 0.78 [0.75-0.82]. Among various serialization methods, the first-occurrence approach-wherein only the initial appearance of each event in a sequence is retained-shows superior performance. Furthermore, for the fine-tuned LLaMA-2 70B, logit outputs yield a higher AUROC of 0.80 [0.77-0.82] compared to text outputs, with an AUROC of 0.69 [0.67-0.72]. This study underscores the potential of fine-tuned LLMs, particularly when combined with strategic sequence serialization, in advancing clinical prediction tasks.
Federated Diabetes Prediction in Canadian Adults Using Real-world Cross-Province Primary Care Data.
Tang G, Black JE, Williamson TS … +1 more , Drew SH
AMIA Annu Symp Proc · 2024 · PMID 40417552
Integrating Electronic Health Records (EHR) and the application of machine learning present opportunities for enhancing the accuracy and accessibility of data-driven diabetes prediction. In particular, developing data-dr...
Integrating Electronic Health Records (EHR) and the application of machine learning present opportunities for enhancing the accuracy and accessibility of data-driven diabetes prediction. In particular, developing data-driven machine learning models can provide early identification of patients with high risk for diabetes, potentially leading to more effective therapeutic strategies and reduced healthcare costs. However, regulation restrictions create barriers to developing centralized predictive models. This paper addresses the challenges by introducing a federated learning approach, which amalgamates predictive models without centralized data storage and processing, thus avoiding privacy issues. This marks the first application of federated learning to predict diabetes using real clinical datasets in Canada extracted from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) without cross-province patient data sharing. We address class-imbalance issues through downsampling techniques and compare federated learning performance against province-based and centralized models. Experimental results show that the federated MLP model presents a similar or higher performance compared to the model trained with the centralized approach. However, the federated logistic regression model showed inferior performance compared to its centralized peer.
Lessons Learned from OpenEMR Implementation in Graduate Health Informatics Curriculum.
Sunchu K, Moncy MM, Purkayastha S … +1 more , Fulton CR
AMIA Annu Symp Proc · 2024 · PMID 40417551
This study examines the integration of OpenEMR, a Meaningful Use-certified open-source electronic health record (EHR) system, into a Health Informatics curriculum. The primary objective was to address the disparity betwe...
This study examines the integration of OpenEMR, a Meaningful Use-certified open-source electronic health record (EHR) system, into a Health Informatics curriculum. The primary objective was to address the disparity between theoretical knowledge and practical application in health informatics education. The implementation process revealed several significant challenges, including unintended system modifications that compromised functionality, data entry errors that impacted usability, and technical issues that impeded accessibility. To mitigate these challenges, a series of interventions were implemented. These included backend modifications to enhance data entry accuracy, usability improvements such as limiting open tabs to facilitate navigation, and the implementation ofproactive measures to expedite the resolution of technical issues. The experiences gained from this integration process highlight three critical aspects of health informatics education: the significance of practical proficiency in EHR systems, the necessity for user-centric interface design, and the importance of adaptability and problem-solving skills. The study proposes several future directions for research and practice. These include fostering global collaboration, developing standardized curricula for EHR education, and establishing robust mechanisms for continuous assessment and improvement. The findings underscore the pivotal role of integrating hands-on EHR experience into health informatics education, emphasizing its potential to equip students with the essential competencies required to navigate the complex and dynamic healthcare landscape.
Combining Rule-based NLP-lite with Rapid Iterative Chart Adjudication for Creation of a Large, Accurately Curated Cohort from EHR data: A Case Study in the Context of a Clinical Trial Emulation.
Mutalik P, Cheung KH, Green J … +12 more , Buelt-Gebhardt M, Anderson KF, Jeanpaul V, McDonald L, Wininger M, Li Y, Rajeevan N, Jessel PM, Moore H, Adabag S, Raitt MH, Aslan M
AMIA Annu Symp Proc · 2024 · PMID 40417550
The aim of this work was to create a gold-standard curated cohort of 10,000+ cases from the Veteran Affairs (VA) corporate data warehouse (CDW) for virtual emulation of a randomized clinical trial (CSP#592). The trial ha...
The aim of this work was to create a gold-standard curated cohort of 10,000+ cases from the Veteran Affairs (VA) corporate data warehouse (CDW) for virtual emulation of a randomized clinical trial (CSP#592). The trial had six inclusion/exclusion criteria lacking adequate structured data. We therefore used a hybrid computer/human approach to extract information from clinical notes. Rule-based NLP output was iteratively adjudicated by a panel of trained non-clinician content experts and non-experts using an easy-to-use spreadsheet-based rapid adjudication display. This group-adjudication process iteratively sharpened both the computer algorithm and clinical decision criteria, while simultaneously training the non-experts. The cohort was successfully created with each inclusion/exclusion decision backed by a source document. Less than 0.5% of cases required referral to specialist clinicians. It is likely that such curated datasets capturing specialist reasoning and using a process-supervised approach will acquire greater importance as training tools for future clinical AI applications.
Structured Knowledge Base Enhances Effective Use of Large Language Models for Metadata Curation.
Sundaram SS, Solomon B, Khatri A … +3 more , Laumas A, Khatri P, Musen MA
AMIA Annu Symp Proc · 2024 · PMID 40417549
field name-field value
field name-field value
RealMedQA: A pilot biomedical question answering dataset containing realistic clinical questions.
Kell G, Roberts A, Umansky S … +8 more , Khare Y, Ahmed N, Patel N, Simela C, Coumbe J, Rozario J, Griffiths RR, Marshall IJ
AMIA Annu Symp Proc · 2024 · PMID 40417548
Clinical question answering systems have the potential to provide clinicians with relevant and timely answers to their questions. Nonetheless, despite the advances that have been made, adoption of these systems in clinic...
Clinical question answering systems have the potential to provide clinicians with relevant and timely answers to their questions. Nonetheless, despite the advances that have been made, adoption of these systems in clinical settings has been slow. One issue is a lack of question-answering datasets which reflect the real-world needs of health professionals. In this work, we present RealMedQA, a dataset of realistic clinical questions generated by humans and an LLM. We describe the process for generating and verifying the QA pairs and assess several QA models on BioASQ and RealMedQA to assess the relative difficulty of matching answers to questions. We show that the LLM is more cost-efficient for generating "ideal" QA pairs. Additionally, we achieve a lower lexical similarity between questions and answers than BioASQ which provides an additional challenge to the top two QA models, as per the results. We release our code and our dataset publicly to encourage further research.
Investigating the effects of housing instability on depression, anxiety, and mental health treatment in childhood and adolescence.
Zehrung R, Hu D, Guo Y … +2 more , Zheng K, Chen Y
AMIA Annu Symp Proc · 2024 · PMID 40417547
Housing instability is a widespread phenomenon in the United States. In combination with other social determinants of health, housing instability affects children's overall health and development. Drawing on data from th...
Housing instability is a widespread phenomenon in the United States. In combination with other social determinants of health, housing instability affects children's overall health and development. Drawing on data from the 2022 National Survey of Children's Health, we employed multiple logistic regression models to understand how sociodemographic factors, especially housing instability, affect mental health outcomes and treatment access for youth aged 6-17 years. Our results show that youth facing housing instability have a higher likelihood of experiencing anxiety (OR: 1.42, p<0.001) and depression (OR: 1.57, p<0.001). Furthermore, youth experiencing both mental health conditions and housing instability are significantly less likely to receive mental health services in the past year, indicating the substantial barriers they face in accessing mental health care. Based on our findings, we highlight opportunities for digital mental health interventions to provide children experiencing housing instability with more accessible and consistent mental health services.
Adaptive Recruitment Resource Allocation to Improve Cohort Representativeness in Participatory Biomedical Datasets.
Borza VA, Estornell A, Clayton EW … +4 more , Ho CJ, Rothman RL, Vorobeychik Y, Malin BA
AMIA Annu Symp Proc · 2024 · PMID 40417546
Large participatory biomedical studies - studies that recruit individuals to join a dataset - are gaining popularity and investment, especially for analysis by modern AI methods. Because they purposively recruit particip...
Large participatory biomedical studies - studies that recruit individuals to join a dataset - are gaining popularity and investment, especially for analysis by modern AI methods. Because they purposively recruit participants, these studies are uniquely able to address a lack of historical representation, an issue that has affected many biomedical datasets. In this work, we define representativeness as the similarity to a target population distribution of a set of attributes and our goal is to mirror the U.S. population across distributions of age, gender, race, and ethnicity. Many participatory studies recruit at several institutions, so we introduce a computational approach to adaptively allocate recruitment resources among sites to improve representativeness. In simulated recruitment of 10,000-participant cohorts from medical centers in the STAR Clinical Research Network, we show that our approach yields a more representative cohort than existing baselines. Thus, we highlight the value of computational modeling in guiding recruitment efforts.
Ontology-based modeling, integration, and analysis of heterogeneous clinical, pathological, and molecular kidney data for precision medicine.
He YO, Barisoni L, Rosenberg AZ … +22 more , Robinson P, Diehl AD, Chen Y, Phuong J, Hansen J, Herr Ii BW, Börner K, Schaub J, Bonevich N, Arnous G, Boddapati S, Zheng J, Alakwaa F, Sardar P, Duncan WD, Liang C, Valerius MT, Jain S, Iyengar R, Himmelfarb J, Kretzler M, Kidney Precision Medicine Project
AMIA Annu Symp Proc · 2024 · PMID 40417545
Many data resources generate, process, store, or provide kidney related molecular, pathological, and clinical data. Reference ontologies offer an opportunity to support knowledge and data integration. The Kidney Precisio...
Many data resources generate, process, store, or provide kidney related molecular, pathological, and clinical data. Reference ontologies offer an opportunity to support knowledge and data integration. The Kidney Precision Medicine Project (KPMP) team contributed to the representation and addition of 329 kidney phenotype terms to the Human Phenotype Ontology (HPO), and identified many subcategories of acute kidney injury (AKI) or chronic kidney disease (CKD). The Kidney Tissue Atlas Ontology (KTAO) imports and integrates kidney-related terms from existing ontologies (e.g., HPO, CL, and Uberon) and represents 259 kidney-related biomarkers. We have also developed a precision medicine metadata ontology (PMMO) to integrate 50 variables from KPMP and CZ CellxGene data resources and applied PMMO for integrative kidney data analysis. The gene expression profiles of kidney gene biomarkers were specifically analyzed under healthy control or AKI/CKD disease states. This work demonstrates how ontology-based approaches support multi-domain data and knowledge integration in precision medicine.
Enhancing Semantic and Structure Modeling of Diseases for Diagnosis Prediction.
Lv H, Chen Z, Yang Y … +4 more , Pan S, Xiong B, Tan Y, Yang C
AMIA Annu Symp Proc · 2024 · PMID 40417544
Electronic Health Records (EHRs) are valuable healthcare data, aiding researchers and doctors in improving diagnosis accuracy. Researchers have developed several predictive models by learning disease representations to f...
Electronic Health Records (EHRs) are valuable healthcare data, aiding researchers and doctors in improving diagnosis accuracy. Researchers have developed several predictive models by learning disease representations to forecast the potential diagnosis that patients may receive. However, existing studies usually ignore the fine-grained semantic and structure information in EHRs (e.g., the hierarchical relations between diseases and ICD-9 codes), which fails to provide accurate disease representation towards effective diagnosis prediction. To this end, we propose to enhance diagnosis prediction through LabCare, a framework with improved semantic and structure modeling of diseases in EHR data. LabCare can simultaneously capture rich semantic and structural relations among diseases and ICD-9 codes, which is achieved by innovatively integrating language models and box embeddings. Extensive experiments on two EHR datasets show that LabCare surpasses competitors, consistently achieving a 4.29% average improvement in Recall and NDCG metrics.
A Personalized Dosing Strategy Optimization for Diabetes Management: Applications to Gestational Diabetes Mellitus.
Lee EK, Wei X, Wright MD … +1 more , Baker-Witt F
AMIA Annu Symp Proc · 2024 · PMID 40417543
Type II diabetes mellitus is a disorder that disrupts the way the body uses glucose. It is managed by close monitoring of blood glucose levels while the clinician experiments with a dosing strategy based on some clinical...
Type II diabetes mellitus is a disorder that disrupts the way the body uses glucose. It is managed by close monitoring of blood glucose levels while the clinician experiments with a dosing strategy based on some clinical guidelines and his/her own experience. In this study, we propose a treatment planning model that optimizes the dosing strategy for diabetes treatment. The model utilizes a patient's personalized characteristics of disease progression and dose-response to optimize the drug dosage. Such personalized evidence is estimated by a drug-dose-drug-effect predictive model using the daily blood glucose data recorded during the titration period. We apply these to a group of patients suffering from gestational diabetes. For each patient, drug-dose-drug-effect prediction was established based on the first four weeks of self-monitored blood glucose. The treatment model then individualizes and optimizes the dose regimen based on the patient's personalized drug-dose-drug-effect characteristics. Consistently, the optimized dose regimens use less amount of drug while achieving better glycemic control than the original regimens used to treat the patients. This results in the first mathematical model that is data-driven and evidence-based and that quantitatively optimizes dosage for the treatment of diabetes. The model can generate a personalized dose regimen that has a better treatment outcome and is more drug-efficient. Clinical trials must be conducted to gauge the overall effectiveness.
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