Publications by authors named "Daniel J Lizotte"

21 Publications

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Primary Care Informatics Response to Covid-19 Pandemic: Adaptation, Progress, and Lessons from Four Countries with High ICT Development.

Yearb Med Inform 2021 Apr 21. Epub 2021 Apr 21.

Nuffield Department of Primary Care Health Sciences, University of Oxford, UK.

Objective: Internationally, primary care practice had to transform in response to the COVID pandemic. Informatics issues included access, privacy, and security, as well as patient concerns of equity, safety, quality, and trust. This paper describes progress and lessons learned.

Methods: IMIA Primary Care Informatics Working Group members from Australia, Canada, United Kingdom and United States developed a standardised template for collection of information. The template guided a rapid literature review. We also included experiential learning from primary care and public health perspectives.

Results: All countries responded rapidly. Common themes included rapid reductions then transformation to virtual visits, pausing of non-COVID related informatics projects, all against a background of non-standardized digital development and disparate territory or state regulations and guidance. Common barriers in these four and in less-resourced countries included disparities in internet access and availability including bandwidth limitations when internet access was available, initial lack of coding standards, and fears of primary care clinicians that patients were delaying care despite the availability of televisits.

Conclusions: Primary care clinicians were able to respond to the COVID crisis through telehealth and electronic record enabled change. However, the lack of coordinated national strategies and regulation, assurance of financial viability, and working in silos remained limitations. The potential for primary care informatics to transform current practice was highlighted. More research is needed to confirm preliminary observations and trends noted.
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http://dx.doi.org/10.1055/s-0041-1726489DOI Listing
April 2021

Artificial Intelligence, Intersectionality, and the Future of Public Health.

Am J Public Health 2021 01;111(1):98-100

Greta R. Bauer is with the departments of Epidemiology and Biostatistics and Gender, Sexuality and Women's Studies, Western University, London, ON, Canada. Daniel J. Lizotte is with the departments of Epidemiology and Biostatistics and Computer Science, and the Schulich Interfaculty Program in Public Health, Western University.

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http://dx.doi.org/10.2105/AJPH.2020.306006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7750598PMC
January 2021

Patterns of Primary Care Use Prior to a First Diagnosis of Nonaffective Psychotic Disorder in Ontario, Canada: Modèles d'utilisation des soins de première ligne avant un premier diagnostic de trouble psychotique non affectif en Ontario, Canada.

Can J Psychiatry 2021 Apr 5;66(4):406-417. Epub 2020 Oct 5.

Department of Epidemiology and Biostatistics, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario, Canada.

Background: Many people experience early signs and symptoms before the onset of psychotic disorder, suggesting that there may be help-seeking prior to first diagnosis. The family physician has been found to play a key role in pathways to care. This study examined patterns of primary care use preceding a first diagnosis of psychotic disorder.

Methods: We used health administrative data from Ontario (Canada) to construct a population-based retrospective cohort. We investigated patterns of primary care use, including frequency and timing of contacts, in the 6 years prior to a first diagnosis of psychosis, relative to a general population comparison group matched on age, sex, geographic area, and index date. We used latent class growth modeling to identify distinct trajectories of primary care service use, and associated factors, preceding the first diagnosis.

Results: People with early psychosis contacted primary care over twice as frequently in the 6 years preceding first diagnosis (RR = 2.22; 95% CI, = 2.19 to 2.25), relative to the general population, with a sharp increase in contacts 10 months prior to diagnosis. They had higher contact frequency across nearly all diagnostic codes, including mental health, physical health, and preventative health. We identified 3 distinct service use trajectories: low-, medium-, and high-increasing usage.

Discussion: We found elevated patterns of primary care service use prior to first diagnosis of psychotic disorder, suggesting that initiatives to support family physicians in their role on the pathway to care are warranted. Earlier intervention has implications for improved social, educational, and professional development in young people with first-episode psychosis.
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http://dx.doi.org/10.1177/0706743720961732DOI Listing
April 2021

Cognition and motor function: The gait and cognition pooled index.

PLoS One 2020 11;15(9):e0238690. Epub 2020 Sep 11.

Department of Epidemiology & Biostatistics, Schulich School of Medicine & Dentistry, University of Western Ontario, London, Ontario, Canada.

Background: There is a need for outcome measures with improved responsiveness to changes in pre-dementia populations. Both cognitive and motor function play important roles in neurodegeneration; motor function decline is detectable at early stages of cognitive decline. This proof of principle study used a Pooled Index approach to evaluate improved responsiveness of the predominant outcome measure (ADAS-Cog: Alzheimer's Disease Assessment Scale-Cognitive Subscale) when assessment of motor function is added.

Methods: Candidate Pooled Index variables were selected based on theoretical importance and pairwise correlation coefficients. Kruskal-Wallis and Mann-Whitney U tests assessed baseline discrimination. Standardized response means assessed responsiveness to longitudinal change.

Results: Final selected variables for the Pooled Index include gait velocity, dual-task cost of gait velocity, and an ADAS-Cog-Proxy (statistical approximation of the ADAS-Cog using similar cognitive tests). The Pooled Index and ADAS-Cog-Proxy scores had similar ability to discriminate between pre-dementia syndromes. The Pooled Index demonstrated trends of similar or greater responsiveness to longitudinal decline than ADAS-Cog-Proxy scores.

Conclusion: Adding motor function assessments to the ADAS-Cog may improve responsiveness in pre-dementia populations.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0238690PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7485843PMC
October 2020

Development and evaluation of an osteoarthritis risk model for integration into primary care health information technology.

Int J Med Inform 2020 09 1;141:104160. Epub 2020 May 1.

Department of Computer Science, Department of Epidemiology & Biostatistics, Schulich Interfaculty Program in Public Health, Department of Statistical and Actuarial Sciences, 1151 Richmond Street, Western University, London, Ontario, N6A 3K7, Canada. Electronic address:

Background: We developed and evaluated a prognostic prediction model that estimates osteoarthritis risk for use by patients and practitioners that is designed to be appropriate for integration into primary care health information technology systems. Osteoarthritis, a joint disorder characterized by pain and stiffness, causes significant morbidity among older Canadians. Because our prognostic prediction model for osteoarthritis risk uses data that are readily available in primary care settings, it supports targeting of interventions delivered as part of clinical practice that are aimed at risk reduction.

Methods: We used the CPCSSN (Canadian Primary Sentinel Surveillance Network) database, which contains aggregated electronic health information from a cohort of primary care practices, to develop and evaluate a prognostic prediction model to estimate 5-year osteoarthritis risk, addressing contextual challenges of data availability and missingness. We constructed a retrospective cohort of 383,117 eligible primary care patients who were included in the cohort if they had an encounter with their primary care practitioner between 1 January 2009 and 31 December 2010. Patients were excluded if they had a diagnosis of osteoarthritis prior to their first visit in this time period. Incident cases of osteoarthritis were observed. The model was constructed to predict incident osteoarthritis based on age, sex, BMI, previous leg injury, and osteoporosis. Evaluation of the model used internal 10-fold cross-validation; we argue that internal validation is particularly appropriate for a model that is to be integrated into the same context from which the data were derived.

Results: The resulting prediction model for 5-year risk of osteoarthritis diagnosis demonstrated state-of-the-art discrimination (estimated AUROC 0.84) and good calibration (assessed visually.) The model relies only on information that is readily available in Canadian primary care settings, and hence is appropriate for integration into Canadian primary care health information technology.

Conclusions: If the contextual challenges arising when using primary care electronic medical record data are appropriately addressed, highly discriminative models for osteoarthritis risk may be constructed using only data commonly available in primary care. Because the models are constructed from data in the same setting where the model is to be applied, internal validation provides strong evidence that the resulting model will perform well in its intended application.
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http://dx.doi.org/10.1016/j.ijmedinf.2020.104160DOI Listing
September 2020

Artificial Intelligence and Primary Care Research: A Scoping Review.

Ann Fam Med 2020 05;18(3):250-258

Departments of Epidemiology & Biostatistics, Computer Science, Schulich Interfaculty Program in Public Health, Statistical & Actuarial Sciences, Western University, London, Ontario, Canada.

Purpose: Rapid increases in technology and data motivate the application of artificial intelligence (AI) to primary care, but no comprehensive review exists to guide these efforts. Our objective was to assess the nature and extent of the body of research on AI for primary care.

Methods: We performed a scoping review, searching 11 published or gray literature databases with terms pertaining to AI (eg, machine learning, bayes* network) and primary care (eg, general pract*, nurse). We performed title and abstract and then full-text screening using Covidence. Studies had to involve research, include both AI and primary care, and be published in Eng-lish. We extracted data and summarized studies by 7 attributes: purpose(s); author appointment(s); primary care function(s); intended end user(s); health condition(s); geographic location of data source; and AI subfield(s).

Results: Of 5,515 unique documents, 405 met eligibility criteria. The body of research focused on developing or modifying AI methods (66.7%) to support physician diagnostic or treatment recommendations (36.5% and 13.8%), for chronic conditions, using data from higher-income countries. Few studies (14.1%) had even a single author with a primary care appointment. The predominant AI subfields were supervised machine learning (40.0%) and expert systems (22.2%).

Conclusions: Research on AI for primary care is at an early stage of maturity. For the field to progress, more interdisciplinary research teams with end-user engagement and evaluation studies are needed.
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http://dx.doi.org/10.1370/afm.2518DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7213996PMC
May 2020

Estimating patient-reported outcomes for glaucoma management: A cross-sectional study.

J Evid Based Med 2020 Feb 17;13(1):8-16. Epub 2020 Jan 17.

Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Canada.

Aim: To identify important explanatory variables of four patient-reported outcomes (PROs): vision-related quality of life (VRQoL), preference-based health-related quality of life (HRQoL), social support and community integration and depressive symptoms.

Methods: Cross-sectional study conducted at one ophthalmic practice in a hospital setting. Patients with a diagnosis of glaucoma or glaucoma suspect (n = 250) were sequentially recruited. Patients with language restrictions were excluded. Data were collected through medical chart reviews and face-to-face interviews. The PROs were measured using validated tools. Candidate models for predicting PROs from explanatory variables were constructed using linear and logistic regression, as well as classification and regression trees. Through leave-one-out cross-validation, the performance of each model was assessed in terms of mean absolute error.

Results: Use of mobility aids, best corrected visual acuity (BCVA), income, and living arrangements were most predictive of VRQoL, social support, and community integration. Use of mobility aids was also most predictive of the presence of depressive symptoms, and BCVA with preference-based HRQoL.

Conclusion: Although promising associations were discovered, the models based on commonly collected clinical variables had limited ability to accurately predict individual patient PROs. Thus, although this study identifies clinical and demographic variables that are most predictive of PROs, routine collection of PROs in clinical practice may be necessary to obtain a complete picture of the quality of life of glaucoma patients.
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http://dx.doi.org/10.1111/jebm.12369DOI Listing
February 2020

Math versus meaning in MAIHDA: A commentary on multilevel statistical models for quantitative intersectionality.

Soc Sci Med 2020 01 24;245:112500. Epub 2019 Aug 24.

Department of Epidemiology & Biostatistics, Schulich School of Medicine & Dentistry, The University of Western Ontario, Canada. Electronic address:

Rationale: Intersectionality has been increasingly adopted as a theoretical framework within quantitative research, raising questions about the congruence between theory and statistical methodology. Which methods best map onto intersectionality theory, with regard to their assumptions and the results they produce? Which methods are best positioned to provide information on health inequalities and direction for their remediation? One method, multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA), has been argued to provide statistical efficiency for high-dimensional intersectional analysis along with valid intersection-specific predictions and tests of interactions. However, the method has not been thoroughly tested in scenarios where ground truth is known.

Method: We perform a simulation analysis using plausible data generating scenarios where intersectional effects are present. We apply variants of MAIHDA and ordinary least squares regression to each, and we observe how the effects are reflected in the estimates that the methods produce.

Results: The first-order fixed effects estimated by MAIHDA can be interpreted neither as effects on mean outcome when interacting variables are set to zero (as in a correctly-specified linear regression model), nor as effects on mean outcome averaged over the individuals in the population (as in a misspecified linear regression model), but rather as effects on mean outcome averaged over an artificial population where all intersections are of equal size. Furthermore, the values of the random effects do not reflect advantage or disadvantage of different intersectional groups.

Conclusions: Because first-order fixed effects estimates are the reference point for interpreting random effects as intersectional effects in MAIHDA analyses, the random effects alone do not provide meaningful estimates of intersectional advantage or disadvantage. Rather, the fixed and random parts of the model must be combined for their estimates to be meaningful. We therefore advise caution when interpreting the results of MAIHDA in quantitative intersectional analyses.
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http://dx.doi.org/10.1016/j.socscimed.2019.112500DOI Listing
January 2020

Development of an EMG-Based Muscle Health Model for Elbow Trauma Patients.

Sensors (Basel) 2019 Jul 27;19(15). Epub 2019 Jul 27.

Department of Electrical and Computer Engineering, Western University, London, ON N6A 5B9, Canada.

Wearable robotic braces have the potential to improve rehabilitative therapies for patients suffering from musculoskeletal (MSK) conditions. Ideally, a quantitative assessment of health would be incorporated into rehabilitative devices to monitor patient recovery. The purpose of this work is to develop a model to distinguish between the healthy and injured arms of elbow trauma patients based on electromyography (EMG) data. Surface EMG recordings were collected from the healthy and injured limbs of 30 elbow trauma patients while performing 10 upper-limb motions. Forty-two features and five feature sets were extracted from the data. Feature selection was performed to improve the class separation and to reduce the computational complexity of the feature sets. The following classifiers were tested: linear discriminant analysis (LDA), support vector machine (SVM), and random forest (RF). The classifiers were used to distinguish between two levels of health: healthy and injured (50% baseline accuracy rate). Maximum fractal length (MFL), myopulse percentage rate (MYOP), power spectrum ratio (PSR) and spike shape analysis features were identified as the best features for classifying elbow muscle health. A majority vote of the LDA classification models provided a cross-validation accuracy of 82.1%. The work described in this paper indicates that it is possible to discern between healthy and injured limbs of patients with MSK elbow injuries. Further assessment and optimization could improve the consistency and accuracy of the classification models. This work is the first of its kind to identify EMG metrics for muscle health assessment by wearable rehabilitative devices.
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http://dx.doi.org/10.3390/s19153309DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6695912PMC
July 2019

Predicting responses to platin chemotherapy agents with biochemically-inspired machine learning.

Signal Transduct Target Ther 2019;4. Epub 2019 Jan 11.

1Department of Biochemistry, Schulich School of Medicine and Dentistry, Western University, London, ON N6A 2C1 Canada.

The selection of effective genes that accurately predict chemotherapy responses might improve cancer outcomes. We compare optimized gene signatures for cisplatin, carboplatin, and oxaliplatin responses in the same cell lines and validate each signature using data from patients with cancer. Supervised support vector machine learning is used to derive gene sets whose expression is related to the cell line GI values by backwards feature selection with cross-validation. Specific genes and functional pathways distinguishing sensitive from resistant cell lines are identified by contrasting signatures obtained at extreme and median GI thresholds. Ensembles of gene signatures at different thresholds are combined to reduce the dependence on specific GI values for predicting drug responses. The most accurate gene signatures for each platin are: cisplatin: , , , , , , , , , , , , , , and ; carboplatin: , , , , , , , , , , , , , , and and oxaliplatin: , , , , , , , , , , and . Data from The Cancer Genome Atlas (TCGA) patients with bladder, ovarian, and colorectal cancer were used to test the cisplatin, carboplatin, and oxaliplatin signatures, resulting in 71.0%, 60.2%, and 54.5% accuracies in predicting disease recurrence and 59%, 61%, and 72% accuracies in predicting remission, respectively. One cisplatin signature predicted 100% of recurrence in non-smoking patients with bladder cancer (57% disease-free;  = 19), and 79% recurrence in smokers (62% disease-free;  = 35). This approach should be adaptable to other studies of chemotherapy responses, regardless of the drug or cancer types.
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http://dx.doi.org/10.1038/s41392-018-0034-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6329797PMC
February 2021

A generalized shapelet-based method for analysis of nanostructured surface imaging.

Nanotechnology 2019 Feb 23;30(7):075703. Epub 2018 Nov 23.

Department of Chemical Engineering, University of Waterloo, Waterloo, Ontario, Canada.

The determination of quantitative structure-property relations is a vital but challenging task for nanostructured materials research due to the presence of large-scale spatially varying patterns resulting from nanoscale processes such as self-assembly and nano-lithography. Focusing on nanostructured surfaces, recent advances have been made in automated quantification methods for orientational and translational order using shapelet functions, originally developed for analysis of images of galaxies, as a reduced-basis for surface pattern structure. In this work, a method combining shapelet functions and machine learning is developed and applied to a representative set of images of self-assembled surfaces from experimental characterization techniques including scanning electron miscroscopy, atomic force microscopy and transmission electron microscopy. The method is shown to be computationally efficient and able to quantify salient pattern features including deformation, defects, and grain boundaries from a broad range of patterns typical of self-assembly processes.
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http://dx.doi.org/10.1088/1361-6528/aaf353DOI Listing
February 2019

CYP2D6 genotype and endoxifen plasma concentration do not predict hot flash severity during tamoxifen therapy.

Breast Cancer Res Treat 2018 Oct 6;171(3):701-708. Epub 2018 Jul 6.

Department of Medicine, Division of Clinical Pharmacology, Western University, London, ON, N6A 5K5, Canada.

Purpose: Tamoxifen is frequently prescribed to prevent breast cancer recurrence. Tamoxifen is a prodrug and requires bioactivation by CYP2D6. Tamoxifen use is often limited by adverse effects including severe hot flashes. There is paucity of prospectively collected data in terms of CYP2D6 genotype and measured tamoxifen, 4-hydroxytamoxifen and endoxifen concentrations in relation to hot flash severity during tamoxifen therapy.

Methods: We conducted a longitudinal prospective study of breast cancer patients on tamoxifen (n = 410). At each visit, blood samples were collected, and patients completed a standardized hot flash survey (n = 1144) that reflected hot flash severity during the 7 days prior to the visit. Plasma concentrations of tamoxifen, 4-hydroxytamoxifen, and endoxifen were measured using liquid chromatography-tandem mass spectrometry and genotyping was carried out for CYP2D6. A linear mixed-effects regression analysis assessed the association of covariates in relation to the hot flash severity score (HFSS).

Results: Median age at first assessment was 50 years with 61.9% of patients considered peri-menopausal. Most patients (92.2%) experienced hot flash symptoms with 51.0% having low HFSS (0-4) and 7.32% experiencing HFSS > 25. Age was significantly associated with hot flash severity, with patients aged 45-59 more likely to have higher HFSS. Neither duration of tamoxifen therapy nor observed tamoxifen, endoxifen and 4-hydroxy tamoxifen plasma concentration predicted hot flash severity. Genetic variation in CYP2D6 or CYP3A4 was not predictive of hot flash severity.

Conclusions: Hot flash severity during tamoxifen therapy can not be accounted for by CYP2D6 genotype or observed plasma concentration of tamoxifen, 4-hydroxytamoxifen, or endoxifen.
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http://dx.doi.org/10.1007/s10549-018-4876-xDOI Listing
October 2018

Interpatient Variation in Rivaroxaban and Apixaban Plasma Concentrations in Routine Care.

Can J Cardiol 2017 08 24;33(8):1036-1043. Epub 2017 Apr 24.

Division of Clinical Pharmacology, Department of Medicine, London Health Sciences Centre, Western University, London, Ontario, Canada; Department of Physiology and Pharmacology, Western University, London, Ontario, Canada. Electronic address:

Background: Direct-acting oral anticoagulants (DOACs) are widely prescribed for stroke prevention in patients with atrial fibrillation (AF). An important advantage of DOACs is that routine monitoring of an anticoagulation response is not necessary. Nevertheless, because of their mechanism of action, a DOAC anticoagulation effect can be inferred based on the observed plasma concentration. However, there is a paucity of data relating to observed interpatient variation in DOAC plasma concentrations in the postmarket clinical setting.

Methods: We determined rivaroxaban and apixaban plasma concentrations in patients with AF during routine clinic visits.

Results: Among 243 patients (rivaroxaban, n = 94; apixaban, n = 149) enrolled in this study, a 60- and 50-fold interpatient variation in plasma concentration was observed for rivaroxaban and apixaban, respectively. Approximately 12% of patients receiving rivaroxaban and 13% of patients receiving apixaban exceeded the 95th percentile for predicted maximum plasma concentration observed in clinical trials.

Conclusions: In this routine-care setting, rivaroxaban and apixaban plasma concentrations tended to be more variable than those observed in clinical trials. Identification of additional clinical and molecular determinants that more fully predict patients at risk for excessively high or low DOAC concentrations may enable a more precise DOAC dosing regimen for the individual patient.
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http://dx.doi.org/10.1016/j.cjca.2017.04.008DOI Listing
August 2017

Prediction and tolerance intervals for dynamic treatment regimes.

Stat Methods Med Res 2017 Aug 11;26(4):1611-1629. Epub 2017 Jul 11.

Departments of Computer Science and Epidemiology & Biostatistics, The University of Western Ontario, London, Ontario, Canada.

We develop and evaluate tolerance interval methods for dynamic treatment regimes (DTRs) that can provide more detailed prognostic information to patients who will follow an estimated optimal regime. Although the problem of constructing confidence intervals for DTRs has been extensively studied, prediction and tolerance intervals have received little attention. We begin by reviewing in detail different interval estimation and prediction methods and then adapting them to the DTR setting. We illustrate some of the challenges associated with tolerance interval estimation stemming from the fact that we do not typically have data that were generated from the estimated optimal regime. We give an extensive empirical evaluation of the methods and discussed several practical aspects of method choice, and we present an example application using data from a clinical trial. Finally, we discuss future directions within this important emerging area of DTR research.
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http://dx.doi.org/10.1177/0962280217708662DOI Listing
August 2017

Multi-Objective Markov Decision Processes for Data-Driven Decision Support.

J Mach Learn Res 2016 1;17. Epub 2016 Dec 1.

Department of Statistics, North Carolina State University, Raliegh, NC 27695, USA.

We present new methodology based on Multi-Objective Markov Decision Processes for developing sequential decision support systems from data. Our approach uses sequential decision-making data to provide support that is useful to many different decision-makers, each with different, potentially time-varying preference. To accomplish this, we develop an extension of fitted- iteration for multiple objectives that computes policies for all scalarization functions, i.e. preference functions, simultaneously from continuous-state, finite-horizon data. We identify and address several conceptual and computational challenges along the way, and we introduce a new solution concept that is appropriate when different actions have similar expected outcomes. Finally, we demonstrate an application of our method using data from the Clinical Antipsychotic Trials of Intervention Effectiveness and show that our approach offers decision-makers increased choice by a larger class of optimal policies.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5179144PMC
December 2016

Theory and application of shapelets to the analysis of surface self-assembly imaging.

Phys Rev E Stat Nonlin Soft Matter Phys 2015 Mar 25;91(3):033307. Epub 2015 Mar 25.

Department of Chemical Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada and Waterloo Institute for Nanotechnology, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.

A method for quantitative analysis of local pattern strength and defects in surface self-assembly imaging is presented and applied to images of stripe and hexagonal ordered domains. The presented method uses "shapelet" functions which were originally developed for quantitative analysis of images of galaxies (∝10(20)m). In this work, they are used instead to quantify the presence of translational order in surface self-assembled films (∝10(-9)m) through reformulation into "steerable" filters. The resulting method is computationally efficient (with respect to the number of filter evaluations), robust to variation in pattern feature shape, and, unlike previous approaches, is applicable to a wide variety of pattern types. An application of the method is presented which uses a nearest-neighbor analysis to distinguish between uniform (defect-free) and nonuniform (strained, defect-containing) regions within imaged self-assembled domains, both with striped and hexagonal patterns.
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http://dx.doi.org/10.1103/PhysRevE.91.033307DOI Listing
March 2015

Dynamic treatment regimes: technical challenges and applications.

Electron J Stat 2014 ;8(1):1225-1272

University of Michigan, Ann Arbor, MI 48106-1248.

Dynamic treatment regimes are of growing interest across the clinical sciences because these regimes provide one way to operationalize and thus inform sequential personalized clinical decision making. Formally, a dynamic treatment regime is a sequence of decision rules, one per stage of clinical intervention. Each decision rule maps up-to-date patient information to a recommended treatment. We briefly review a variety of approaches for using data to construct the decision rules. We then review a critical inferential challenge that results from nonregularity, which often arises in this area. In particular, nonregularity arises in inference for parameters in the optimal dynamic treatment regime; the asymptotic, limiting, distribution of estimators are sensitive to local perturbations. We propose and evaluate a locally consistent Adaptive Confidence Interval (ACI) for the parameters of the optimal dynamic treatment regime. We use data from the Adaptive Pharmacological and Behavioral Treatments for Children with ADHD Trial as an illustrative example. We conclude by highlighting and discussing emerging theoretical problems in this area.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4209714PMC
http://dx.doi.org/10.1214/14-ejs920DOI Listing
January 2014

Set-valued dynamic treatment regimes for competing outcomes.

Biometrics 2014 Mar 8;70(1):53-61. Epub 2014 Jan 8.

Department of Statistics, NC State University, Raleigh, North Carolina 27695, U.S.A.

Dynamic treatment regimes (DTRs) operationalize the clinical decision process as a sequence of functions, one for each clinical decision, where each function maps up-to-date patient information to a single recommended treatment. Current methods for estimating optimal DTRs, for example Q-learning, require the specification of a single outcome by which the "goodness" of competing dynamic treatment regimes is measured. However, this is an over-simplification of the goal of clinical decision making, which aims to balance several potentially competing outcomes, for example, symptom relief and side-effect burden. When there are competing outcomes and patients do not know or cannot communicate their preferences, formation of a single composite outcome that correctly balances the competing outcomes is not possible. This problem also occurs when patient preferences evolve over time. We propose a method for constructing DTRs that accommodates competing outcomes by recommending sets of treatments at each decision point. Formally, we construct a sequence of set-valued functions that take as input up-to-date patient information and give as output a recommended subset of the possible treatments. For a given patient history, the recommended set of treatments contains all treatments that produce non-inferior outcome vectors. Constructing these set-valued functions requires solving a non-trivial enumeration problem. We offer an exact enumeration algorithm by recasting the problem as a linear mixed integer program. The proposed methods are illustrated using data from the CATIE schizophrenia study.
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http://dx.doi.org/10.1111/biom.12132DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3954452PMC
March 2014

Linear Fitted-Q Iteration with Multiple Reward Functions.

J Mach Learn Res 2012 Nov;13(Nov):3253-3295

David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada,

We present a general and detailed development of an algorithm for finite-horizon fitted-Q iteration with an arbitrary number of reward signals and linear value function approximation using an arbitrary number of state features. This includes a detailed treatment of the 3-reward function case using triangulation primitives from computational geometry and a method for identifying globally dominated actions. We also present an example of how our methods can be used to construct a real-world decision aid by considering symptom reduction, weight gain, and quality of life in sequential treatments for schizophrenia. Finally, we discuss future directions in which to take this work that will further enable our methods to make a positive impact on the field of evidence-based clinical decision support.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3670261PMC
November 2012

Informing sequential clinical decision-making through reinforcement learning: an empirical study.

Mach Learn 2011 Jul;84(1-2):109-136

School of Computer Science, McGill University, Montreal, QC, Canada H3A 2T5.

This paper highlights the role that reinforcement learning can play in the optimization of treatment policies for chronic illnesses. Before applying any off-the-shelf reinforcement learning methods in this setting, we must first tackle a number of challenges. We outline some of these challenges and present methods for overcoming them. First, we describe a multiple imputation approach to overcome the problem of missing data. Second, we discuss the use of function approximation in the context of a highly variable observation set. Finally, we discuss approaches to summarizing the evidence in the data for recommending a particular action and quantifying the uncertainty around the Q-function of the recommended policy. We present the results of applying these methods to real clinical trial data of patients with schizophrenia.
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http://dx.doi.org/10.1007/s10994-010-5229-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3143507PMC
July 2011