32 research outputs found
A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units
The management of invasive mechanical ventilation, and the regulation of
sedation and analgesia during ventilation, constitutes a major part of the care
of patients admitted to intensive care units. Both prolonged dependence on
mechanical ventilation and premature extubation are associated with increased
risk of complications and higher hospital costs, but clinical opinion on the
best protocol for weaning patients off of a ventilator varies. This work aims
to develop a decision support tool that uses available patient information to
predict time-to-extubation readiness and to recommend a personalized regime of
sedation dosage and ventilator support. To this end, we use off-policy
reinforcement learning algorithms to determine the best action at a given
patient state from sub-optimal historical ICU data. We compare treatment
policies from fitted Q-iteration with extremely randomized trees and with
feedforward neural networks, and demonstrate that the policies learnt show
promise in recommending weaning protocols with improved outcomes, in terms of
minimizing rates of reintubation and regulating physiological stability
Using Whole Slide Image Representations from Self-Supervised Contrastive Learning for Melanoma Concordance Regression
Although melanoma occurs more rarely than several other skin cancers,
patients' long term survival rate is extremely low if the diagnosis is missed.
Diagnosis is complicated by a high discordance rate among pathologists when
distinguishing between melanoma and benign melanocytic lesions. A tool that
provides potential concordance information to healthcare providers could help
inform diagnostic, prognostic, and therapeutic decision-making for challenging
melanoma cases. We present a melanoma concordance regression deep learning
model capable of predicting the concordance rate of invasive melanoma or
melanoma in-situ from digitized Whole Slide Images (WSIs). The salient features
corresponding to melanoma concordance were learned in a self-supervised manner
with the contrastive learning method, SimCLR. We trained a SimCLR feature
extractor with 83,356 WSI tiles randomly sampled from 10,895 specimens
originating from four distinct pathology labs. We trained a separate melanoma
concordance regression model on 990 specimens with available concordance ground
truth annotations from three pathology labs and tested the model on 211
specimens. We achieved a Root Mean Squared Error (RMSE) of 0.28 +/- 0.01 on the
test set. We also investigated the performance of using the predicted
concordance rate as a malignancy classifier, and achieved a precision and
recall of 0.85 +/- 0.05 and 0.61 +/- 0.06, respectively, on the test set. These
results are an important first step for building an artificial intelligence
(AI) system capable of predicting the results of consulting a panel of experts
and delivering a score based on the degree to which the experts would agree on
a particular diagnosis. Such a system could be used to suggest additional
testing or other action such as ordering additional stains or genetic tests.Comment: Accepted at ECCV 2022 AIMIA Workshop. arXiv admin note: text overlap
with arXiv:2109.0755
Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes
Multi-output Gaussian processes (GPs) are a flexible Bayesian nonparametric
framework that has proven useful in jointly modeling the physiological states
of patients in medical time series data. However, capturing the short-term
effects of drugs and therapeutic interventions on patient physiological state
remains challenging. We propose a novel approach that models the effect of
interventions as a hybrid Gaussian process composed of a GP capturing patient
physiology convolved with a latent force model capturing effects of treatments
on specific physiological features. This convolution of a multi-output GP with
a GP including a causal time-marked kernel leads to a well-characterized model
of the patients' physiological state responding to interventions. We show that
our model leads to analytically tractable cross-covariance functions, allowing
scalable inference. Our hierarchical model includes estimates of
patient-specific effects but allows sharing of support across patients. Our
approach achieves competitive predictive performance on challenging hospital
data, where we recover patient-specific response to the administration of three
common drugs: one antihypertensive drug and two anticoagulants
Economic Impacts of Non-Native Forest Insects in the Continental United States
Reliable estimates of the impacts and costs of biological invasions are critical to developing credible management, trade and regulatory policies. Worldwide, forests and urban trees provide important ecosystem services as well as economic and social benefits, but are threatened by non-native insects. More than 450 non-native forest insects are established in the United States but estimates of broad-scale economic impacts associated with these species are largely unavailable. We developed a novel modeling approach that maximizes the use of available data, accounts for multiple sources of uncertainty, and provides cost estimates for three major feeding guilds of non-native forest insects. For each guild, we calculated the economic damages for five cost categories and we estimated the probability of future introductions of damaging pests. We found that costs are largely borne by homeowners and municipal governments. Wood- and phloem-boring insects are anticipated to cause the largest economic impacts by annually inducing nearly 830 million in lost residential property values. Given observations of new species, there is a 32% chance that another highly destructive borer species will invade the U.S. in the next 10 years. Our damage estimates provide a crucial but previously missing component of cost-benefit analyses to evaluate policies and management options intended to reduce species introductions. The modeling approach we developed is highly flexible and could be similarly employed to estimate damages in other countries or natural resource sectors
Economic Impacts of Non-Native Forest Insects in the Continental United States
Reliable estimates of the impacts and costs of biological invasions are critical to developing credible management, trade and regulatory policies. Worldwide, forests and urban trees provide important ecosystem services as well as economic and social benefits, but are threatened by non-native insects. More than 450 non-native forest insects are established in the United States but estimates of broad-scale economic impacts associated with these species are largely unavailable. We developed a novel modeling approach that maximizes the use of available data, accounts for multiple sources of uncertainty, and provides cost estimates for three major feeding guilds of non-native forest insects. For each guild, we calculated the economic damages for five cost categories and we estimated the probability of future introductions of damaging pests. We found that costs are largely borne by homeowners and municipal governments. Wood- and phloem-boring insects are anticipated to cause the largest economic impacts by annually inducing nearly 830 million in lost residential property values. Given observations of new species, there is a 32% chance that another highly destructive borer species will invade the U.S. in the next 10 years. Our damage estimates provide a crucial but previously missing component of cost-benefit analyses to evaluate policies and management options intended to reduce species introductions. The modeling approach we developed is highly flexible and could be similarly employed to estimate damages in other countries or natural resource sectors
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Behavioral economic implementation strategies to improve serious illness communication between clinicians and high-risk patients with cancer: protocol for a cluster randomized pragmatic trial
Background
Serious illness conversations (SICs) are an evidence-based approach to eliciting patients’ values, goals, and care preferences that improve patient outcomes. However, most patients with cancer die without a documented SIC. Clinician-directed implementation strategies informed by behavioral economics (“nudges”) that identify high-risk patients have shown promise in increasing SIC documentation among clinicians. It is unknown whether patient-directed nudges that normalize and prime patients towards SIC completion—either alone or in combination with clinician nudges that additionally compare performance relative to peers—may improve on this approach. Our objective is to test the effect of clinician- and patient-directed nudges as implementation strategies for increasing SIC completion among patients with cancer.
Methods
We will conduct a 2 × 2 factorial, cluster randomized pragmatic trial to test the effect of nudges to clinicians, patients, or both, compared to usual care, on SIC completion. Participants will include 166 medical and gynecologic oncology clinicians practicing at ten sites within a large academic health system and their approximately 5500 patients at high risk of predicted 6-month mortality based on a validated machine-learning prognostic algorithm. Data will be obtained via the electronic medical record, clinician survey, and semi-structured interviews with clinicians and patients. The primary outcome will be time to SIC documentation among high-risk patients. Secondary outcomes will include time to SIC documentation among all patients (assessing spillover effects), palliative care referral among high-risk patients, and aggressive end-of-life care utilization (composite of chemotherapy within 14 days before death, hospitalization within 30 days before death, or admission to hospice within 3 days before death) among high-risk decedents. We will assess moderators of the effect of implementation strategies and conduct semi-structured interviews with a subset of clinicians and patients to assess contextual factors that shape the effectiveness of nudges with an eye towards health equity.
Discussion
This will be the first pragmatic trial to evaluate clinician- and patient-directed nudges to promote SIC completion for patients with cancer. We expect the study to yield insights into the effectiveness of clinician and patient nudges as implementation strategies to improve SIC rates, and to uncover multilevel contextual factors that drive response to these strategies.
Trial registration
ClinicalTrials.gov
,
NCT04867850
. Registered on April 30, 2021.
Funding
National Cancer Institute P50CA24469
Predictive invasion ecology and decisions under uncertainty
This thesis focuses on the development of methods for forecasting and managing the spread of non-native species. By combining statistical modelling and computational simulations with both biological and sociological data, this research aims to provide decision support tools to resource managers and policy makers. With an emphasis on the quantification and propagation of uncertainty through the construction of both classical and Bayesian models, I analyse the implications of various human and biological factors on forecasting the spread of fresh water invasive species. These include: 1) dispersal network structure, 2) population dynamics, 3) environmental suitability, and 4) human behavioural feedbacks to policy interventions.The first section compares two current approaches to predicting the secondary spread of aquatic invasive species and introduces a novel methodology for the quantitative validation of such predictions. Chapter 2 compares alternative models of human-mediated dispersal and assesses the consequences of the resulting dispersal network structures for predictions of invasion risk at both the local and landscape level. A new approach to validating the predictions made by models of spreading species is developed in Chapter 3. The new approach accommodates both stochastic and epistemic prediction uncertainty and I demonstrate that it has both the appropriate expected error rates as well as increased power compared to existing methods. Application to a published forecast model of Bythotrephes longimanus in central Ontario confirms the predicted invasion pattern.The second section deals with the development and application of new forecasting and management models which are applicable in common situations of limited data availability and limited management resources. Chapter 4 solves problems posed by presence-only data by extending current approaches to species distribution modelling using an observation model of the detection process. Application of this approach to 10 aquatic invasive species in Ontario revealed that the number of sites at which species are detected is not alone predictive of their current and potential range. By quantifying between-species differences in prevalence and detectability, this approach can provide guidance for sampling efforts and management interventions. Finally, Chapter 5 addresses the predicted efficacy of specific management interventions by modelling the human behavioural responses to such interventions. By integrating behavioural responses into a gravity model formulation, the predicted consequences of various policy scenarios on the future spread of aquatic invasives in Ontario is compared. Together, this research provides novel insights into both ecological processes and environmental policy.Cette thèse repose sur le développement de méthodes visant à prédire et gérer la propagation des espèces non-indigènes. Combinant modélisation statistique et simulations informatiques sur des données biologiques et sociologiques, cette recherche vise à fournir une aide à la décision aux décideurs et gestionnaires de ressources. En mettant l'accent sur la quantification et la propagation de l'incertitude par la construction de deux modèles classique et bayésien, j'analyse les implications de différents facteurs humains et biologiques pour la prédiction de la propagation des espèces envahissantes d'eau douce. Il s'agit notamment de: 1) la structure du réseau de dispersion, 2) les dynamiques de population, 3) la qualité de l'environnement, et 4) la réponse aux interventions politiques.La première section compare deux approches actuellement employées pour prédire la propagation secondaire d'espèces aquatiques envahissantes et introduit une nouvelle méthodologie de validation quantitative de ces prédictions. Le chapitre 2 compare d'autres modèles de dispersion par médiation humaine et évalue les conséquences des structures de réseau de dispersion obtenues pour les prédictions de risques d'invasion, à la fois au niveau local et à l'échelle du paysage. Le chapitre 3 développe une nouvelle approche de validation des prédictions issues de modèles de propagation. Cette nouvelle approche intègre les incertitudes de prédiction stochastique et épistémique et je démontre qu'elle conduit aux taux d'erreur attendus et est plus puissante que les méthodes existantes. Appliquée à un modèle de prévision publié de Bythoterphes longimanus dans le Centre de l'Ontario, cette approche confirme le schéma d'invasion prédit. La deuxième section porte sur le développement et l'application de nouveaux modèles de prévision et de gestion utilisables dans les limites ordinaires de disponibilité de données et de ressources de gestion. Le chapitre 4 résout les problèmes associés aux données signalant seulement la présence d'une espèce et élargit le champ des approches actuelles de modélisation de distribution d'espèces par l'utilisation d'un modèle d'observation du processus de détection. L'application de cette approche sur 10 espèces aquatiques envahissantes en Ontario révèle que l'usage seul du nombre de sites où les espèces sont détectées ne permet pas de prédire leur distribution actuelle et potentielle. En quantifiant les différences inter-spécifiques de prévalence et de détection, cette approche peut aider au développement de méthodes d'échantillonnage et à la mise en place d'interventions de gestion. En dernier lieu, le chapitre 5 traite de l'efficacité prédite d'interventions de gestion spécifiques en modélisant les réponses comportementales des individus à ces interventions. Intégrant les réponses comportementales dans une formule de modèle de gravité, je compare les effets prédits de différents scenarios d'intervention sur la future propagation des espèces envahissantes aquatiques en Ontario. Cette recherche ouvre de nouvelles perspectives aussi bien sur les processus écologiques que les politiques de gestion de l'environnement
Rising complexity and falling explanatory power in ecology
Analyses of published research can provide a realistic perspective on the progress of science. By analyzing more than 18 000 articles published by the preeminent ecological societies, we found that (1) ecological research is becoming increasingly statistically complex, reporting a growing number of P values per article and (2) the value of reported coefficient of determination (R2) has been falling steadily, suggesting a decrease in the marginal explanatory power of ecology. These trends may be due to changes in the way ecology is studied or in the way the findings of investigations are reported. Determining the reason for increasing complexity and declining marginal explanatory power would require a critical review of the scientific process in ecology, from research design to dissemination, and could influence the public interpretation and policy implications of ecological findings