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 $1.7 billion in local government expenditures and approximately$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 $1.7 billion in local government expenditures and approximately$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

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

Guiding Efficient, Effective, and Patient-Oriented Electrolyte Replacement in Critical Care: An Artificial Intelligence Reinforcement Learning Approach

Both provider- and protocol-driven electrolyte replacement have been linked to the over-prescription of ubiquitous electrolytes. Here, we describe the development and retrospective validation of a data-driven clinical decision support tool that uses reinforcement learning (RL) algorithms to recommend patient-tailored electrolyte replacement policies for ICU patients. We used electronic health records (EHR) data that originated from two institutions (UPHS; MIMIC-IV). The tool uses a set of patient characteristics, such as their physiological and pharmacological state, a pre-defined set of possible repletion actions, and a set of clinical goals to present clinicians with a recommendation for the route and dose of an electrolyte. RL-driven electrolyte repletion substantially reduces the frequency of magnesium and potassium replacements (up to 60%), adjusts the timing of interventions in all three electrolytes considered (potassium, magnesium, and phosphate), and shifts them towards orally administered repletion over intravenous replacement. This shift in recommended treatment limits risk of the potentially harmful effects of over-repletion and implies monetary savings. Overall, the RL-driven electrolyte repletion recommendations reduce excess electrolyte replacements and improve the safety, precision, efficacy, and cost of each electrolyte repletion event, while showing robust performance across patient cohorts and hospital systems