360 research outputs found

    Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines

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    The dearth of prescribing guidelines for physicians is one key driver of the current opioid epidemic in the United States. In this work, we analyze medical and pharmaceutical claims data to draw insights on characteristics of patients who are more prone to adverse outcomes after an initial synthetic opioid prescription. Toward this end, we propose a generative model that allows discovery from observational data of subgroups that demonstrate an enhanced or diminished causal effect due to treatment. Our approach models these sub-populations as a mixture distribution, using sparsity to enhance interpretability, while jointly learning nonlinear predictors of the potential outcomes to better adjust for confounding. The approach leads to human-interpretable insights on discovered subgroups, improving the practical utility for decision suppor

    Confident interpretation of Bayesian decision tree ensembles for clinical applications

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    Copyright © 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.Bayesian averaging (BA) over ensembles of decision models allows evaluation of the uncertainty of decisions that is of crucial importance for safety-critical applications such as medical diagnostics. The interpretability of the ensemble can also give useful information for experts responsible for making reliable decisions. For this reason, decision trees (DTs) are attractive decision models for experts. However, BA over such models makes an ensemble of DTs uninterpretable. In this paper, we present a new approach to probabilistic interpretation of Bayesian DT ensembles. This approach is based on the quantitative evaluation of uncertainty of the DTs, and allows experts to find a DT that provides a high predictive accuracy and confident outcomes. To make the BA over DTs feasible in our experiments, we use a Markov Chain Monte Carlo technique with a reversible jump extension. The results obtained from clinical data show that in terms of predictive accuracy, the proposed method outperforms the maximum a posteriori (MAP) method that has been suggested for interpretation of DT ensembles

    A decision support system to follow up and diagnose primary headache patients using semantically enriched data

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    Abstract Background Headache disorders are an important health burden, having a large health-economic impact worldwide. Current treatment & follow-up processes are often archaic, creating opportunities for computer-aided and decision support systems to increase their efficiency. Existing systems are mostly completely data-driven, and the underlying models are a black-box, deteriorating interpretability and transparency, which are key factors in order to be deployed in a clinical setting. Methods In this paper, a decision support system is proposed, composed of three components: (i) a cross-platform mobile application to capture the required data from patients to formulate a diagnosis, (ii) an automated diagnosis support module that generates an interpretable decision tree, based on data semantically annotated with expert knowledge, in order to support physicians in formulating the correct diagnosis and (iii) a web application such that the physician can efficiently interpret captured data and learned insights by means of visualizations. Results We show that decision tree induction techniques achieve competitive accuracy rates, compared to other black- and white-box techniques, on a publicly available dataset, referred to as migbase. Migbase contains aggregated information of headache attacks from 849 patients. Each sample is labeled with one of three possible primary headache disorders. We demonstrate that we are able to reduce the classification error, statistically significant (ρ≤0.05), with more than 10% by balancing the dataset using prior expert knowledge. Furthermore, we achieve high accuracy rates by using features extracted using the Weisfeiler-Lehman kernel, which is completely unsupervised. This makes it an ideal approach to solve a potential cold start problem. Conclusion Decision trees are the perfect candidate for the automated diagnosis support module. They achieve predictive performances competitive to other techniques on the migbase dataset and are, foremost, completely interpretable. Moreover, the incorporation of prior knowledge increases both predictive performance as well as transparency of the resulting predictive model on the studied dataset

    Modern Views of Machine Learning for Precision Psychiatry

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    In light of the NIMH's Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of the ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. Additionally, we review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We further discuss explainable AI (XAI) and causality testing in a closed-human-in-the-loop manner, and highlight the ML potential in multimedia information extraction and multimodal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research

    Towards Interpretable Machine Learning in Medical Image Analysis

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    Over the past few years, ML has demonstrated human expert level performance in many medical image analysis tasks. However, due to the black-box nature of classic deep ML models, translating these models from the bench to the bedside to support the corresponding stakeholders in the desired tasks brings substantial challenges. One solution is interpretable ML, which attempts to reveal the working mechanisms of complex models. From a human-centered design perspective, interpretability is not a property of the ML model but an affordance, i.e., a relationship between algorithm and user. Thus, prototyping and user evaluations are critical to attaining solutions that afford interpretability. Following human-centered design principles in highly specialized and high stakes domains, such as medical image analysis, is challenging due to the limited access to end users. This dilemma is further exacerbated by the high knowledge imbalance between ML designers and end users. To overcome the predicament, we first define 4 levels of clinical evidence that can be used to justify the interpretability to design ML models. We state that designing ML models with 2 levels of clinical evidence: 1) commonly used clinical evidence, such as clinical guidelines, and 2) iteratively developed clinical evidence with end users are more likely to design models that are indeed interpretable to end users. In this dissertation, we first address how to design interpretable ML in medical image analysis that affords interpretability with these two different levels of clinical evidence. We further highly recommend formative user research as the first step of the interpretable model design to understand user needs and domain requirements. We also indicate the importance of empirical user evaluation to support transparent ML design choices to facilitate the adoption of human-centered design principles. All these aspects in this dissertation increase the likelihood that the algorithms afford interpretability and enable stakeholders to capitalize on the benefits of interpretable ML. In detail, we first propose neural symbolic reasoning to implement public clinical evidence into the designed models for various routinely performed clinical tasks. We utilize the routinely applied clinical taxonomy for abnormality classification in chest x-rays. We also establish a spleen injury grading system by strictly following the clinical guidelines for symbolic reasoning with the detected and segmented salient clinical features. Then, we propose the entire interpretable pipeline for UM prognostication with cytopathology images. We first perform formative user research and found that pathologists believe cell composition is informative for UM prognostication. Thus, we build a model to analyze cell composition directly. Finally, we conduct a comprehensive user study to assess the human factors of human-machine teaming with the designed model, e.g., whether the proposed model indeed affords interpretability to pathologists. The human-centered design process is proven to be truly interpretable to pathologists for UM prognostication. All in all, this dissertation introduces a comprehensive human-centered design for interpretable ML solutions in medical image analysis that affords interpretability to end users

    Elicitation of relevant information from medical databases: application to the encoding of secondary diagnoses

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    Dans cette thèse, nous nous concentrons sur le codage du séjour d'hospitalisation en codes standards. Ce codage est une tâche médicale hautement sensible dans les hôpitaux français, nécessitant des détails minutieux et une haute précision, car le revenu de l'hôpital en dépend directement. L'encodage du séjour d'hospitalisation comprend l'encodage du diagnostic principal qui motive le séjour d'hospitalisation et d'autres diagnostics secondaires qui surviennent pendant le séjour. Nous proposons une analyse rétrospective mettant en oeuvre des méthodes d'apprentissage, sur la tâche d'encodage de certains diagnostics secondaires sélectionnés. Par conséquent, la base de données PMSI, une grande base de données médicales qui documente toutes les informations sur les séjours d'hospitalisation en France.} est analysée afin d'extraire à partir de séjours de patients hospitalisés antérieurement, des variables décisives (Features). Identifier ces variables permet de pronostiquer le codage d'un diagnostic secondaire difficile qui a eu lieu avec un diagnostic principal fréquent. Ainsi, à la fin d'une session de codage, nous proposons une aide pour les codeurs en proposant une liste des encodages pertinents ainsi que des variables utilisées pour prédire ces encodages. Les défis nécessitent une connaissance métier dans le domaine médical et une méthodologie d'exploitation efficace de la base de données médicales par les méthodes d'apprentissage automatique. En ce qui concerne le défi lié à la connaissance du domaine médical, nous collaborons avec des codeurs experts dans un hôpital local afin de fournir un aperçu expert sur certains diagnostics secondaires difficiles à coder et afin d'évaluer les résultats de la méthodologie proposée. En ce qui concerne le défi lié à l'exploitation des bases de données médicales par des méthodes d'apprentissage automatique, plus spécifiquement par des méthodes de "Feature Selection" (FS), nous nous concentrons sur la résolution de certains points : le format des bases de données médicales, le nombre de variables dans les bases de données médicales et les variables instables extraites des bases de données médicales. Nous proposons une série de transformations afin de rendre le format de la base de données médicales, en général sous forme de bases de données relationnelles, exploitable par toutes les méthodes de type FS. Pour limiter l'explosion du nombre de variables représentées dans la base de données médicales, généralement motivée par la quantité de diagnostics et d'actes médicaux, nous analysons l'impact d'un regroupement de ces variables dans un niveau de représentation approprié et nous choisissons le meilleur niveau de représentation. Enfin, les bases de données médicales sont souvent déséquilibrées à cause de la répartition inégale des exemples positifs et négatifs. Cette répartition inégale cause des instabilités de variables extraites par des méthodes de FS. Pour résoudre ce problème, nous proposons une méthodologie d'extraction des variables stables en échantillonnant plusieurs fois l'ensemble de données et en extrayant les variables pertinentes de chaque ensemble de données échantillonné. Nous évaluons la méthodologie en établissant un modèle de classification qui prédit les diagnostics étudiés à partir des variables extraites. La performance du modèle de classification indique la qualité des variables extraites, car les variables de bonne qualité produisent un bon modèle de classification. Deux échelles de base de données PMSI sont utilisées: échelle locale et régionale. Le modèle de classification est construit en utilisant l'échelle locale de PMSI et testé en utilisant des échelles locales et régionales. Les évaluations ont montré que les variables extraites sont de bonnes variables pour coder des diagnostics secondaires. Par conséquent, nous proposons d'appliquer notre méthodologie pour éviter de manquer des encodages importants qui affectent le budget de l'hôpital en fournissant aux codeurs les encodages potentiels des diagnostics secondaires ainsi que les variables qui conduisent à ce codage.In the thesis we focus on encoding inpatient episode into standard codes, a highly sensitive medical task in French hospitals, requiring minute detail and accuracy, since the hospital's income directly depends on it. Encoding inpatient episode includes encoding the primary diagnosis that motivates the hospitalisation stay and other secondary diagnoses that occur during the stay. Unlike primary diagnosis, encoding secondary diagnoses is prone to human error, due to the difficulty of collecting relevant data from different medical sources, or to the outright absence of relevant data that helps encoding the diagnosis. We propose a retrospective analysis on the encoding task of some selected secondary diagnoses. Hence, the PMSI database is analysed in order to extract, from previously encoded inpatient episodes, the decisive features to encode a difficult secondary diagnosis occurred with frequent primary diagnosis. Consequently, at the end of an encoding session, once all the features are available, we propose to help the coders by proposing a list of relevant encodings as well as the features used to predict these encodings. Nonetheless, a set of challenges need to be addressed for the development of an efficient encoding help system. The challenges include, an expert knowledge in the medical domain and an efficient exploitation methodology of the medical database by Machine Learning methods. With respect to the medical domain knowledge challenge, we collaborate with expert coders in a local hospital in order to provide expert insight on some difficult secondary diagnoses to encode and in order to evaluate the results of the proposed methodology. With respect to the medical databases exploitation challenge, we use ML methods such as Feature Selection (FS), focusing on resolving several issues such as the incompatible format of the medical databases, the excessive number features of the medical databases in addition to the unstable features extracted from the medical databases. Regarding to issue of the incompatible format of the medical databases caused by relational databases, we propose a series of transformation in order to make the database and its features more exploitable by any FS methods. To limit the effect of the excessive number of features in the medical database, usually motivated by the amount of the diagnoses and the medical procedures, we propose to group the excessive number features into a proper representation level and to study the best representation level. Regarding to issue of unstable features extracted from medical databases, as the dataset linked with diagnoses are highly imbalanced due to classification categories that are unequally represented, most existing FS methods tend not to perform well on them even if sampling strategies are used. We propose a methodology to extract stable features by sampling the dataset multiple times and extracting the relevant features from each sampled dataset. Thus, we propose a methodology that resolves these issues and extracts stable set of features from medical database regardless to the sampling method and the FS method used in the methodology. Lastly, we evaluate the methodology by building a classification model that predicts the studied diagnoses out of the extracted features. The performance of the classification model indicates the quality of the extracted features, since good quality features produces good classification model. Two scales of PMSI database are used: local and regional scales. The classification model is built using the local scale of PMSI and tested out using both local and regional scales. Hence, we propose applying our methodology to increase the integrity of the encoded diagnoses and to prevent missing important encodings. We propose modifying the encoding process and providing the coders with the potential encodings of the secondary diagnoses as well as the features that lead to this encoding
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