Artificial Intelligence and Patient-Centered Decision-Making

Advanced AI systems are rapidly making their way into medical research and practice, and, arguably, it is only a matter of time before they will surpass human practitioners in terms of accuracy, reliability, and knowledge. If this is true, practitioners will have a prima facie epistemic and professional obligation to align their medical verdicts with those of advanced AI systems. However, in light of their complexity, these AI systems will often function as black boxes: the details of their contents, calculations, and procedures cannot be meaningfully understood by human practitioners. When AI systems reach this level of complexity, we can also speak of black-box medicine. In this paper, we want to argue that black-box medicine conflicts with core ideals of patient-centered medicine. In particular, we claim, black-box medicine is not conducive for supporting informed decision-making based on shared information, shared deliberation, and shared mind between practitioner and patient

CausaLM: Causal Model Explanation Through Counterfactual Language Models

Understanding predictions made by deep neural networks is notoriously difficult, but also crucial to their dissemination. As all ML-based methods, they are as good as their training data, and can also capture unwanted biases. While there are tools that can help understand whether such biases exist, they do not distinguish between correlation and causation, and might be ill-suited for text-based models and for reasoning about high level language concepts. A key problem of estimating the causal effect of a concept of interest on a given model is that this estimation requires the generation of counterfactual examples, which is challenging with existing generation technology. To bridge that gap, we propose CausaLM, a framework for producing causal model explanations using counterfactual language representation models. Our approach is based on fine-tuning of deep contextualized embedding models with auxiliary adversarial tasks derived from the causal graph of the problem. Concretely, we show that by carefully choosing auxiliary adversarial pre-training tasks, language representation models such as BERT can effectively learn a counterfactual representation for a given concept of interest, and be used to estimate its true causal effect on model performance. A byproduct of our method is a language representation model that is unaffected by the tested concept, which can be useful in mitigating unwanted bias ingrained in the data.Comment: Our code and data are available at: https://amirfeder.github.io/CausaLM/ Under review for the Computational Linguistics journa

Data Mining Application for Healthcare Sector: Predictive Analysis of Heart Attacks

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceCardiovascular diseases are the main cause of the number of deaths in the world, being the heart disease the most killing one affecting more than 75% of individuals living in countries of low and middle earnings. Considering all the consequences, firstly for the individual’s health, but also for the health system and the cost of healthcare (for instance, treatments and medication), specifically for cardiovascular diseases treatment, it has become extremely important the provision of quality services by making use of preventive medicine, whose focus is identifying the disease risk, and then, applying the right action in case of early signs. Therefore, by resorting to DM (Data Mining) and its techniques, there is the ability to uncover patterns and relationships amongst the objects in healthcare data, giving the potential to use it more efficiently, and to produce business intelligence and extract knowledge that will be crucial for future answers about possible diseases and treatments on patients. Nowadays, the concept of DM is already applied in medical information systems for clinical purposes such as diagnosis and treatments, that by making use of predictive models can diagnose some group of diseases, in this case, heart attacks. The focus of this project consists on applying machine learning techniques to develop a predictive model based on a real dataset, in order to detect through the analysis of patient’s data whether a person can have a heart attack or not. At the end, the best model is found by comparing the different algorithms used and assessing its results, and then, selecting the one with the best measures. The correct identification of early cardiovascular problems signs through the analysis of patient data can lead to the possible prevention of heart attacks, to the consequent reduction of complications and secondary effects that the disease may bring, and most importantly, to the decrease on the number of deaths in the future. Making use of Data Mining and analytics in healthcare will allow the analysis of high volumes of data, the development of new predictive models, and the understanding of the factors and variables that have the most influence and contribution for this disease, which people should pay attention. Hence, this practical approach is an example of how predictive analytics can have an important impact in the healthcare sector: through the collection of patient’s data, models learn from it so that in the future they can predict new unknown cases of heart attacks with better accuracies. In this way, it contributes to the creation of new models, to the tracking of patient’s health data, to the improvement of medical decisions, to efficient and faster responses, and to the wellbeing of the population that can be improved if diseases like this can be predicted and avoided. To conclude, this project aims to present and show how Data Mining techniques are applied in healthcare and medicine, and how they contribute for the better knowledge of cardiovascular diseases and for the support of important decisions that will influence the patient’s quality of life

Sequential Condition Evolved Interaction Knowledge Graph for Traditional Chinese Medicine Recommendation

Traditional Chinese Medicine (TCM) has a rich history of utilizing natural herbs to treat a diversity of illnesses. In practice, TCM diagnosis and treatment are highly personalized and organically holistic, requiring comprehensive consideration of the patient's state and symptoms over time. However, existing TCM recommendation approaches overlook the changes in patient status and only explore potential patterns between symptoms and prescriptions. In this paper, we propose a novel Sequential Condition Evolved Interaction Knowledge Graph (SCEIKG), a framework that treats the model as a sequential prescription-making problem by considering the dynamics of the patient's condition across multiple visits. In addition, we incorporate an interaction knowledge graph to enhance the accuracy of recommendations by considering the interactions between different herbs and the patient's condition. Experimental results on a real-world dataset demonstrate that our approach outperforms existing TCM recommendation methods, achieving state-of-the-art performance

Rough-set based learning methods: A case study to assess the relationship between the clinical delivery of cannabinoid medicine for anxiety, depression, sleep, patterns and predictability

COVID-19 is an unprecedented health crisis causing a great deal of stress and mental health challenges in populations in Canada. Recently, research is emerging highlighting the potential of cannabinoids’ beneficial effects related to anxiety, mood, and sleep disorders as well as pointing to an increased use of medicinal cannabis since COVID-19 was declared a pandemic. Furthermore, evidence points to a correlation between mental health and sleep patterns. The objective of this research is threefold: i) to assess the relationship of the clinical delivery of cannabinoid medicine, by utilizing machine learning, to anxiety, depression and sleep scores; ii) to discover patterns based on patient features such as specific cannabis recommendations, diagnosis information, decreasing/increasing levels of clinical assessment tools (GAD7, PHQ9 and PSQI) scores over a period of time (including during the COVID timeline); and iii) to predict whether new patients could potentially experience either an increase or decrease in clinical assessment tool scores. The dataset for this thesis was derived from patient visits to Ekosi Health Centres in Manitoba, Canada and Ontario, Canada from January, 2019 to April, 2021. Extensive pre-processing and feature engineering was performed. To determine the outcome of a patients treatment, a class feature (Worse, Better, or No Change) indicative of their progress or lack thereof due to the treatment received was introduced. Three well-known supervised machine learning models (tree-based, rule-based and nearest neighbour) were trained on the patient dataset. In addition, seven rough and rough-fuzzy hybrid methods were also trained on the same dataset. All experiments were conducted using a 10-fold CV method. Sensitivity and specificity measures were higher in all classes with rough and rough-fuzzy hybrid methods. The highest accuracy of 99.15% was obtained using the rule-based rough-set learning method.Ekosi Health Center, MitacsMaster of Science in Applied Computer Scienc

Advancing mental health care with AI-enabled precision psychiatry tools: A patent review

The review provides an overview of patents on AI-enabled precision psychiatry tools published between 2015 and mid-October 2022. Multiple analytic approaches, such as graphic network analysis and topic modeling, are used to analyze the scope, content, and trends of the retained patents. The included tools aim to provide accurate diagnoses according to established psychometric criteria, predict the response to specific treatment approaches, suggest optimal treatments, and make prognoses regarding disorder courses without intervention. About one-third of the tools recommend treatment options or include treatment administration related to digital therapeutics, pharmacotherapy, and electrotherapy. Data sources used to make predictions include behavioral data collected through mobile devices, neuroimaging, and electronic health records. The complexity of technology combinations used in the included devices has increased until 2021. The topics extracted from the patent data illuminate current trends and potential future developments in AI-enabled precision psychiatry. The most impactful patents and associated available products reveal relevant commercialization possibilities and likely future developments. Overall, the review highlights the potential of adopting AI-enabled precision psychiatry tools in practice

Treatment Selection: Understanding What Works For Whom In Mental Health

Individuals seeking treatment for mental health problems often have to choose between several different treatment options. For disorders like depression and PTSD, many of the available treatments have been found to be, on average, equally effective. Research on precision medicine aims to identify the most effective treatment for each patient. This work is based on the idea that individuals respond differently to treatment, and that these differences can be studied and characterized. The push for personalized and precision approaches in mental health involves identifying moderators - variables that predict differential response into treatment recommendations. Unfortunately, there has been little real-world application of these findings, in part due to the lack of systems suited to translating the information in actionable recommendations. This dissertation will review the history of treatment selection in mental health, and will present specific examples of treatment selection models in depression and PTSD. Differences between treatment selection in the context of two equivalently effective interventions and stratified medicine applications in which goal is to optimize the allocation of stronger and weaker interventions will be discussed. Methodological challenges in building (e.g., variable selection) and evaluating (e.g., cross-validation) treatment selection systems will be explored. Approaches to precision medicine being used by different groups will be compared. Finally, recommendations for future directions will be made