arXiv subjects

Michael Chary

Publications and source records attributed to Michael Chary.

4 recordsLinked to original sources

arXiv2026

Is Telehealth Better Used to Treat Patients or Help Other Physicians Treat Patients? An Agent-Based Modeling Study of Healthcare Provision

Telehealth, the delivery of medical care remotely, is hoped to increase access to specialty services or decrease health care utilization. Physicians can provide telehealth to each other or to patients. Specialists often treat complex patients who can be adequately cared for only in academic hospitals, suggesting that providing specialty services via telehealth will reallocate rather than reduce system utilization. Here I use agent-based modeling to investigate telehealth's effects on clinical outcomes and system utilization in medical toxicology. I found that physician-physician telehealth increased patient health but system utilization did not change. The effects were more pronounced as clinical complexity increased. Physician-patient telehealth increased cost and system utilization but not clinical outcomes. Within the limitations of our approach, these results suggest that telehealth is more cost-effective for improving generalist access to specialist knowledge than in providing care to the public.

cs.MA↗

arXiv2026

Curation and Extraction of Drug-Related Entities from Reddit Platform

Physicians learn primarily about illicit drugs from clinical overdose cases, limiting their understanding of real-world usage. Meanwhile, drug users share first-hand experiences online, offering insights into dosage and effects of drugs. To bridge this gap, we introduce ReDose (REddit Drug DOSe and Effect), a dataset of 6,435 Reddit posts on substance use. A board-certified toxicologist primarily annotated both the training and test sets, while two medical science students contributed to the test set, labeling DRUG, DOSE, and EFFECT entities. We benchmarked 6,267 annotations using BERT-based, large language model (LLM)-based, and Retrieval-Augmented Generation (RAG) models. BiomedBERT achieved an F1-score of 0.843 for DRUG, while Llama-3 70B outperformed GPT-4 (F1 = 0.79 vs. 0.72). EFFECT extraction remains challenging, with GPT-4 achieving a recall of 0.41. ReDose captures patient-curated narratives to advance medical data extraction from social media.

cs.CL↗

arXiv2021

Diagnosis of Acute Poisoning Using Explainable Artificial Intelligence

Medical toxicology is the clinical specialty that treats the toxic effects of substances, be it an overdose, a medication error, or a scorpion sting. The volume of toxicological knowledge and research has, as with other medical specialties, outstripped the ability of the individual clinician to entirely master and stay current with it. The application of machine learning techniques to medical toxicology is challenging because initial treatment decisions are often based on a few pieces of textual data and rely heavily on prior knowledge. ML techniques often do not represent knowledge in a way that is transparent for the physician, raising barriers to usability. Rule-based systems and decision tree learning are more transparent approaches, but often generalize poorly and require expert curation to implement and maintain. Here, we construct a probabilistic logic network to represent a portion of the knowledge base of a medical toxicologist. Our approach transparently mimics the knowledge representation and clinical decision-making of practicing clinicians. The software, dubbed Tak, performs comparably to humans on straightforward cases and intermediate difficulty cases, but is outperformed by humans on challenging clinical cases. Tak outperforms a decision tree classifier at all levels of difficulty. Probabilistic logic provides one form of explainable artificial intelligence that may be more acceptable for use in healthcare, if it can achieve acceptable levels of performance.

cs.AI↗

arXiv2012

A Computational Model of the Effects of Drug Addiction on Neural Population Dynamics

Reward processing and derangements thereof, such as drug addiction, involve the coordinated activity of many brain areas. Prior work has identified many behavioral, molecular biological and single neuron changes throughout the mesocorticolimbic system that reflect and drive addictive behavior. Subpopulations in the ventral tegemental area (VTA) encode positive reward prediction error, negative reward prediction error, and the magnitude of the reward. Phasic activity in VTA dopaminergic neurons correlates with hedonic value. Tonic activity of groups in the dorsomedial prefrontal cortex (dmPFC) can encode antidepressant states. However, little is known about how drug addiction might affect population encoding across larger brain regions. Here, we compare the information content associated with network patterns in naive, acutely intoxicated and chronically addicted states in a plastic attractor network. We found that addiction decreases the network's ability to store and discriminate among patterns of activity. Altered dopaminergic tone flattens the energy landscape and decreases the entropy associated with each network pattern. Altered dmPFC activity produces signal-to-noise deficits similar to computational models of schizophrenia. Our results provide a conceptual framework for interpreting altered neural population dynamics in psychopathological states based on information theory. They also suggest a view of the subtypes of depression as on a continuum of combinations of cortical and subcortical dysfunction. This suggests that patients who suffer from depression with psychotic features will have more cortical than mesolimbic dysfunction. Furthermore, our framework can be applied to other psychiatric illnesses and so may help us, in general, quantitatively understand psychiatric illnesses as disorders in the representation and processing of information by distributed brain networks.

q-bio.NC↗