Towards generalizable machine learning models for computer-aided diagnosis in medicine

Hidden stratification represents a phenomenon in which a training dataset contains unlabeled (hidden) subsets of cases that may affect machine learning model performance. Machine learning models that ignore the hidden stratification phenomenon--despite promising overall performance measured as accuracy and sensitivity--often fail at predicting the low prevalence cases, but those cases remain important. In the medical domain, patients with diseases are often less common than healthy patients, and a misdiagnosis of a patient with a disease can have significant clinical impacts. Therefore, to build a robust and trustworthy CAD system and a reliable treatment effect prediction model, we cannot only pursue machine learning models with high overall accuracy, but we also need to discover any hidden stratification in the data and evaluate the proposing machine learning models with respect to both overall performance and the performance on certain subsets (groups) of the data, such as the ‘worst group’. In this study, I investigated three approaches for data stratification: a novel algorithmic deep learning (DL) approach that learns similarities among cases and two schema completion approaches that utilize domain expert knowledge. I further proposed an innovative way to integrate the discovered latent groups into the loss functions of DL models to allow for better model generalizability under the domain shift scenario caused by the data heterogeneity. My results on lung nodule Computed Tomography (CT) images and breast cancer histopathology images demonstrate that learning homogeneous groups within heterogeneous data significantly improves the performance of the computer-aided diagnosis (CAD) system, particularly for low-prevalence or worst-performing cases. This study emphasizes the importance of discovering and learning the latent stratification within the data, as it is a critical step towards building ML models that are generalizable and reliable. Ultimately, this discovery can have a profound impact on clinical decision-making, particularly for low-prevalence cases

The influence of medical personnel's career calling on organizational citizenship behavior: an empirical study in Zhejiang province, China

The medical profession is inherently driven by a sense of duty to protect others’ lives and well-being. These, so called, vocational professions, are characterized by a strong bond with the people they serve, as seen in the Doctor-Patient Relationship. This is a critical factor for the clinical and therapeutic success as well as for the organizational performance. However, the Doctor-Patient Relationship has been facing many challenges due to the changes in the social environment and the deepening of medical system reform. Acknowledging the importance of Doctor-Patient Relationship, it is relevant to identify existing resources that contribute to protect and improve it, namely two features of vocational professions: Career Calling and Organizational Citizenship Behavior. However, empirical research involving these constructs is lacking. The empirical research on the Career Calling rarely involves the medical industry, and most of the researches on the Career Calling and Organizational Citizenship Behavior are conducted in the context of Western culture. Research conducted in China on the Career Calling of medical personnel is more on the level of moral didacticism and lacks quantitative empirical research. The study of medical personnel's Career Calling provides a new approach for hospital organizational behavior and hospital human resource management, and provides a new perspective for hospital managers. Therefore, it is of great theoretical and practical significance to study the combination of medical personnel's Career Calling, Organizational Citizenship Behavior and Doctor-Patient Relationship. Based on a review of the previous studies on the Career Calling, this research further puts forward two empirical studies. The first study adopted a mixed-methods approach to identify the structural dimensions of Career Calling of medical personnel in China. It integrated in-depth interviews with open questionnaires, to produce a scale that was found to have good psychometric qualities, namely, good reliability and validity. The scale comprises three-dimensions, divided each in two subdimensions, all parsimoniously measured with 23 items. A second study, of a quantitative nature, via structural equations modelling, tests the conceptual model that took both Job Engagement and Organizational Citizenship Behavior as the sequential mediators between Career Calling and Doctor-Patient Relationship. Likewise, the model previews a parallel path via Organizational Commitment. With a sample of 767 medical personnel in 10 3A hospitals in Hangzhou, China, results show Career Calling affects Organizational Citizenship Behavior through Job Engagement or organizational commitment, which then is positively associated to Doctor-Patient Relationship. Several paths are uncovered that show a complex network of plausible relations helpful in promoting positive Doctor-Patient Relationship. These findings are analyzed in the light of the theory and can be taken as psychological assets contributive to leveraging Doctor-Patient Relationship with the well-known positive implications for clinical and hospital success.A profissão médica é inerentemente motivada por um sentido de dever de proteção da vida e bem-estar de outros. Estas chamadas profissões vocacionais são caracterizadas por um elo forte com as pessoas que servem, tal como expresso na relação médico-doente. Esta é um fator crítico para o sucesso clínico e terapêutico bem como para o desempenho organizacional. Porém, a relação médico-doente tem enfrentado muitos desafios decorrentes das mudanças no ambiente social e do aprofundamento da reforma do sistema médico. Reconhecendo a importância desta relação, é relevante identificar os recursos existentes que contribuem para a proteger e melhorar, nomeadamente duas características das profissões vocacionais: o sentido de missão profissional e o comportamento de cidadania organizacional. Contudo, a investigação empírica em torno destes constructos está em falta e raramente envolve o setor médico para além de que muita da investigação sobre este constructo e sobre a cidadania organizacional é realizada no contexto cultural ocidental. A investigação realizada na China sobre o sentido de missão profissional dos médicos queda-se pelo nível de pregação moral e carece de investigação empírica quantitativa. O estudo do sentido de missão profissional do pessoal médico oferece, no contexto hospitalar, uma nova abordagem ao comportamento organizacional e à Gestão de Recursos Humanos e faculta uma nova perspetiva aos gestores hospitalares. Assim, estudar a combinação do sentido de missão profissional dos médicos, com os comportamentos de cidadania organizacional e a relação médico-doente tem significado teórico e valor aplicado. Partindo de uma revisão de literatura sobre o sentido de missão profissional, esta pesquisa desenvolve-se dois estudos empíricos. O primeiro adotou uma abordagem mista para identificar as dimensões estruturais do sentido de missão profissional do pessoal médico na China. Integrou entrevistas em profundidade com questionários abertos, para produzir uma escala com boas propriedades psicométricas, nomeadamente validade e fiabilidade. A escala compreende três dimensões, divididas cada em duas sub-dimensões, parcimoniosamente medidas com 23 itens. Um segundo estudo, de natureza quantitativa por via da modelação de equações estruturais, testou o modelo conceptual que tomou a dedicação no trabalho e o comportamento de cidadania organizacional como mediadores sequenciais entre o sentido de missão profissional e a relação médico-doente. Do mesmo modo, o modelo previa um caminho paralelo por via do compromisso organizacional. Com uma amostra de 767 médicos de dez hospitais de nível 3A em Hangzhou, na China, os resultados mostraram que o sentido de missão profissional afeta os comportamentos de cidadania organizacional através da dedicação no trabalho ou do compromisso organizacional, que por sua vez estão associados positivamente à relação médico-paciente. Descobriram-se vários caminhos que evidenciam uma rede complexa de relações plausíveis contributivas para uma relação médico-doente positiva. Estes resultados são analisados à luz da teoria e podem ser tidos como ativos psicológicos que contribuem para alavancar a relação médico-doente e, consequentemente, a sua implicação positiva para o sucesso clínico e hospitalar

Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree Search

Neural Architecture Search (NAS) has shown great success in automating the design of neural networks, but the prohibitive amount of computations behind current NAS methods requires further investigations in improving the sample efficiency and the network evaluation cost to get better results in a shorter time. In this paper, we present a novel scalable Monte Carlo Tree Search (MCTS) based NAS agent, named AlphaX, to tackle these two aspects. AlphaX improves the search efficiency by adaptively balancing the exploration and exploitation at the state level, and by a Meta-Deep Neural Network (DNN) to predict network accuracies for biasing the search toward a promising region. To amortize the network evaluation cost, AlphaX accelerates MCTS rollouts with a distributed design and reduces the number of epochs in evaluating a network by transfer learning, which is guided with the tree structure in MCTS. In 12 GPU days and 1000 samples, AlphaX found an architecture that reaches 97.84\% top-1 accuracy on CIFAR-10, and 75.5\% top-1 accuracy on ImageNet, exceeding SOTA NAS methods in both the accuracy and sampling efficiency. Particularly, we also evaluate AlphaX on NASBench-101, a large scale NAS dataset; AlphaX is 3x and 2.8x more sample efficient than Random Search and Regularized Evolution in finding the global optimum. Finally, we show the searched architecture improves a variety of vision applications from Neural Style Transfer, to Image Captioning and Object Detection.Comment: To appear in the Thirty-Fourth AAAI conference on Artificial Intelligence (AAAI-2020