Computational neuroimaging strategies for single patient predictions

AbstractNeuroimaging increasingly exploits machine learning techniques in an attempt to achieve clinically relevant single-subject predictions. An alternative to machine learning, which tries to establish predictive links between features of the observed data and clinical variables, is the deployment of computational models for inferring on the (patho)physiological and cognitive mechanisms that generate behavioural and neuroimaging responses. This paper discusses the rationale behind a computational approach to neuroimaging-based single-subject inference, focusing on its potential for characterising disease mechanisms in individual subjects and mapping these characterisations to clinical predictions. Following an overview of two main approaches – Bayesian model selection and generative embedding – which can link computational models to individual predictions, we review how these methods accommodate heterogeneity in psychiatric and neurological spectrum disorders, help avoid erroneous interpretations of neuroimaging data, and establish a link between a mechanistic, model-based approach and the statistical perspectives afforded by machine learning

Asymmetric inner wedge group sequential tests with applications to verifying whether effective drug concentrations are similar in adults and children

Extrapolating from information available on one patient group to support conclusions about another is common in clinical research. For example, the findings of clinical trials, often conducted in highly selective patient cohorts, are routinely extrapolated to wider populations by policy makers. Meanwhile, the results of adult trials may be used to support conclusions about the effects of a medicine in children. For example, if the effective concentration of a drug can be assumed to be similar in adults and children, an appropriate paediatric dosing rule may be found by ‘bridging’, that is, by matching the adult effective concentration. However, this strategy may result in children receiving an ineffective or hazardous dose if, in fact, effective concentrations differ between adults and children. When there is uncertainty about the equality of effective concentrations, some pharmacokinetic–pharmacodynamic data may be needed in children to verify that differences are small. In this paper, we derive optimal group sequential tests that can be used to verify this assumption efficiently. Asymmetric inner wedge tests are constructed that permit early stopping to accept or reject an assumption of similar effective drug concentrations in adults and children. Asymmetry arises because the consequences of under- and over-dosing may differ. We show how confidence intervals can be obtained on termination of these tests and illustrate the small sample operating characteristics of designs using simulation

Improving Reinforcement Learning Techniques for Medical Decision Making

Reinforcement learning (RL) is a powerful tool for developing personalized treatment regimens from healthcare data. In RL, an agent samples experiences from an environment (such as a model of patient health) to learn a policy that maximizes long-term reward. This dissertation proposes methodological and practical developments in the application of RL to treatment planning problems. First, we develop a novel time series model for simulating patient health states from observed clinical data. We use a generative neural network architecture that learns a direct mapping between distributions over clinical measurements at adjacent time points. We show that this model produces realistic patient trajectories and can be paired with on-policy RL to learn effective treatment policies. Second, we develop a novel extension of hidden Markov models, which are commonly used to model and predict patient health states. Specifically, we develop a special case of recurrent neural networks with the same likelihood function as a corresponding discrete-observation hidden Markov model. We demonstrate how combining our model with other predictive neural networks improves disease forecasting and offers novel clinical interpretations compared with a standard hidden Markov model. Third, we develop a method for selecting high-performing reinforcement learning-based treatment policies for underrepresented patient subpopulations using limited observations. Our method learns a probability distribution over treatment policies from a reference patient group, then adapts its recommendations using limited data from an underrepresented patient group. We show that our method outperforms state-of-the-art benchmarks in selecting effective treatment policies for patients with non-typical clinical characteristics, and predicting these patients\u27 outcomes under its policies. Finally, we use RL to optimize medication regimens for Parkinson\u27s disease patients using high-frequency wearable sensor data. We build an environment model of how patients\u27 symptoms respond to medication, then use RL to recommend optimal medication types, timing, and dosages for each patient. We show that these patient-specific RL-prescribed medication regimens outperform physician-prescribed regimens and provide clinically defensible treatment strategies. Our framework also enables physicians to identify patients who could could switch to lower-frequency regimens for improved adherence, and to identify patients who may be candidates for advanced therapies

SEMIPARAMETRIC METHODS TO IMPROVE RISK ASSESSMENT AND DYNAMIC PREDICTION

Incorporating promising biomarkers to improve risk assessment and prediction is the central goal in many biomedical studies. Cost-effective designs and longitudinal designs are often utilized for measuring biomarker information, but they pose challenges to the data analyses. Statistical analyses for these kinds of data are routinely performed using parametric models. When the model assumptions are violated, parametric models may lead to substantial bias in parameter estimation, risk evaluation and prediction. In this dissertation, we will develop robust, exible statistical methods for risk assessment for matched case-control, nested case-control, and case-cohort designs, as well as a dynamic prediction tool for longitudinal data. In the first aim, we will develop a distribution-free method for identifying an optimal combination of biomarkers to differentiate cases and controls in matched case-control data. In the second aim, we will develop a semiparametric regression model with minimal assumptions on the link function for data from two-phase sampling designs with binary outcomes. In the third aim, we will develop a model-free dynamic prediction method for a survival outcome that provides dynamically updated risk scores using longitudinal biomarker(s)

Clinical risk modelling with machine learning: adverse outcomes of pregnancy

As a complex biological process, there are various health issues that are related to pregnancy. Prenatal care, a type of preventative healthcare at different points in gestation is comprised of management, treatment, and mitigation of such issues. This also includes risk prediction for adverse pregnancy outcomes, where probabilistic modelling is used to calculate individual’s risk at the early stages of pregnancy. This type of modelling can have a definite clinical scope such as in prenatal screening, and an educational aim where awareness of a healthy lifestyle is promoted, such as in health education. Currently, the most used models are based on traditional statistical approaches, as they provide sufficient predictive power and are easily interpreted by clinicians. Machine learning, a subfield of data science, contains methods for building probabilistic models with multidimensional data. Compared to existing prediction models related to prenatal care, machine learning models can provide better results by fitting more intricate nonlinear decision boundary areas, improve data-driven model fitting by generating synthetic data, and by providing more automation for routine model adjustment processes. This thesis presents the evaluation of machine learning methods to prenatal screening and health education prediction problems, along with novel methods for generating synthetic rare disorder data to be used for modelling, and an adaptive system for continuously adjusting a prediction model to the changing patient population. This way the thesis addresses all the four main entities related to predicting adverse outcomes of pregnancy: the mother or patient, the clinician, the screening laboratory and the developer or manufacturer of screening materials and systems.Kliinisen riskin mallinnus koneoppimismenetelmin: raskaudelle haitalliset lopputulemat Raskaus on kompleksinen biologinen prosessi, jonka etenemiseen liittyy useita terveysongelmia. Äitiyshoito voidaan kuvata ennalta ehkäiseväksi terveydenhuolloksi, jossa pyritään käsittelemään, hoitamaan ja lievittämään kyseisiä ongelmia. Tähän hoitoon sisältyy myös raskauden haitallisten lopputulemien riskilaskenta, missä probabilistista mallinnusta hyödynnetään määrittämään yksilön riski raskauden varhaisissa vaiheissa. Tällä mallinnuksella voi olla selkeä kliininen tarkoitus kuten prenataaliseulonta, tai terveyssivistyksellinen tarkoitus missä odottavalle äidille esitellään raskauden kannalta terveellisiä elämäntapoja. Tällä hetkellä eniten käytössä olevat ennustemallit perustuvat perinteiseen tilastolliseen mallinnukseen, sille ne tarjoavat riittävän ennustetehokkuuden ja ovat helposti tulkittavissa. Koneoppiminen on datatieteen osa-alue, joka pitää sisällään menetelmiä millä voidaan mallintaa moniulotteista dataa ennustekäyttöön. Verrattuna olemassa oleviin äitiyshoidon ennustemalleihin, koneoppiminen mahdollistaa parempien ennustetulosten tuottamisen sovittamalla hienojakoisempia epälineaarisia päätösalueita, tehostamalla datakeskeisten mallien sovitusta luomalla synteettisiä havaintoja ja tarjoamalla enemmän automaatiota rutiininomaiseen mallien hienosäätöön. Tämä väitös esittelee koneoppimismenetelmien evaluaation prenataaliseulonta-ja terveyssivistysongelmiin, ja uusia menetelmiä harvinaisten sairauksien datan luomiseen mallinnustarkoituksiin ja jatkuvan ennustemallin hienosäätämisen järjestelmän muuttuvia potilaspopulaatiota varten. Näin väitös käy läpi kaikki neljä asianomaista jotka liittyvät haitallisten lopputulemien ennustamiseen: odottava äiti eli potilas, kliinikko, seulontalaboratorio ja seulonnassa käytettävien materiaalien ja järjestelmien kehittäjä tai valmistaja

Healthcare seeking behaviour as a link between tuberculosis and socioeconomic factors

Socioeconomic barriers to tuberculosis care-seeking and costs due to care-seeking lead to unfavourable treatment, epidemiological and economic outcomes. Especially in the post-2015 era, socioeconomic interventions for tuberculosis control are receiving increasing attention. In Taiwan, the National Health Insurance programme minimises out-of-pocket expenses for patients, but important delays to tuberculosis treatment still exist. Based on the population and tuberculosis epidemiology in Taiwan, I develop an analysis for profiling the efficacy of tuberculosis care provision and patients' care-seeking pathways. The results highlight that the interrupted tuberculosis evaluation processes and low diagnostic capacity in small local hospitals stands as key causes of extended delays to treatment, unfavourable outcomes, and costs. I analyse socioeconomic status (SES) of employment, vulnerability, and residential contexts, to identify risk factors for different aspects of care-seeking. To link the care-seeking pathways to the nationwide tuberculosis epidemiology, I develop a data-driven hybrid simulation model. The model integrates the advantages of agent-based approaches in representing detail, and equation-based approaches in simplicity and low computational cost. This approach makes feasible Monte-Carlo experiments for robust inferences without over-simplifying the care-seeking details of interest. By comparing the hybrid model simulations with a corresponding equation-based comparator, I confirm its validity. I considered interventions to improve universal health coverage by decentralising tuberculosis diagnostic capacity. I modelled specific interventions increasing the coverage of tuberculosis diagnostic capacity using various SES-targeted scale-up strategies. These show potential benefits in terms of reducing dropouts and reducing the tuberculosis burden, without significant increases in the inequality of care-seeking costs. I suggest considering additional SES variables such as education, health illiteracy, and social segregation to find other care-seeking barriers. Further investigations of SES-related interventions against tuberculosis, including formal impact and health economic evaluation, should be pursued in collaboration with policymakers able to advise on feasibility and patients able to advise on acceptability