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1. AutoPrognosis 2.0: Democratizing diagnostic and prognostic modeling in healthcare with automated machine learning

   https://doi.org/10.1371/journal.pdig.0000276  

   Imrie, Fergus; Cebere, Bogdan; McKinney, Eoin F.; van der Schaar, Mihaela (June 2023, PLOS Digital Health)

   Guillot, Gilles (Ed.)

   Diagnostic and prognostic models are increasingly important in medicine and inform many clinical decisions. Recently, machine learning approaches have shown improvement over conventional modeling techniques by better capturing complex interactions between patient covariates in a data-driven manner. However, the use of machine learning introduces technical and practical challenges that have thus far restricted widespread adoption of such techniques in clinical settings. To address these challenges and empower healthcare professionals, we present an open-source machine learning framework, AutoPrognosis 2.0, to facilitate the development of diagnostic and prognostic models. AutoPrognosis leverages state-of-the-art advances in automated machine learning to develop optimized machine learning pipelines, incorporates model explainability tools, and enables deployment of clinical demonstrators,withoutrequiring significant technical expertise. To demonstrate AutoPrognosis 2.0, we provide an illustrative application where we construct a prognostic risk score for diabetes using the UK Biobank, a prospective study of 502,467 individuals. The models produced by our automated framework achieve greater discrimination for diabetes than expert clinical risk scores. We have implemented our risk score as a web-based decision support tool, which can be publicly accessed by patients and clinicians. By open-sourcing our framework as a tool for the community, we aim to provide clinicians and other medical practitioners with an accessible resource to develop new risk scores, personalized diagnostics, and prognostics using machine learning techniques. Software:https://github.com/vanderschaarlab/AutoPrognosis 

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2. Scientific basis and active ingredients of current therapeutic interventions for stroke rehabilitation

   https://doi.org/10.3233/RNN-211243  

   Ranganathan, Rajiv; Doherty, Carson; Gussert, Michael; Kaplinski, Eva; Koje, Mary; Krishnan, Chandramouli (June 2022, Restorative Neurology and Neuroscience)

   Background: Despite tremendous advances in the treatment and management of stroke, restoring motor and functional outcomes after stroke continues to be a major clinical challenge. Given the wide range of approaches used in motor rehabilitation, several commentaries have highlighted the lack of a clear scientific basis for different interventions as one critical factor that has led to suboptimal study outcomes. Objective: To understand the content of current therapeutic interventions in terms of their active ingredients. Methods: We conducted an analysis of randomized controlled trials in stroke rehabilitation over a 2-year period from 2019-2020. Results: There were three primary findings: (i) consistent with prior reports, most studies did not provide an explicit rationale for why the treatment would be expected to work, (ii) most therapeutic interventions mentioned multiple active ingredients and there was not a close correspondence between the active ingredients mentioned versus the active ingredients measured in the study, and (iii) multimodal approaches that involved more than one therapeutic approach tended to be combined in an ad-hoc fashion, indicating the lack of a targeted approach. Conclusion: These results highlight the need for strengthening cross-disciplinary connections between basic science and clinical studies, and the need for structured development and testing of therapeutic approaches to find more effective treatment interventions. 

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3. Estimating Individualized Treatment Rules for Ordinal Treatments

   https://doi.org/10.1111/biom.12865  

   Chen, Jingxiang; Fu, Haoda; He, Xuanyao; Kosorok, Michael R.; Liu, Yufeng (March 2018, Biometrics)

   Summary Precision medicine is an emerging scientific topic for disease treatment and prevention that takes into account individual patient characteristics. It is an important direction for clinical research, and many statistical methods have been proposed recently. One of the primary goals of precision medicine is to obtain an optimal individual treatment rule (ITR), which can help make decisions on treatment selection according to each patient's specific characteristics. Recently, outcome weighted learning (OWL) has been proposed to estimate such an optimal ITR in a binary treatment setting by maximizing the expected clinical outcome. However, for ordinal treatment settings, such as individualized dose finding, it is unclear how to use OWL. In this article, we propose a new technique for estimating ITR with ordinal treatments. In particular, we propose a data duplication technique with a piecewise convex loss function. We establish Fisher consistency for the resulting estimated ITR under certain conditions, and obtain the convergence and risk bound properties. Simulated examples and an application to a dataset from a type 2 diabetes mellitus observational study demonstrate the highly competitive performance of the proposed method compared to existing alternatives. 

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4. Nondestructive 3D Pathology with Light-Sheet Fluorescence Microscopy for Translational Research and Clinical Assays

   https://doi.org/10.1146/annurev-anchem-091222-092734  

   Liu, Jonathan T.C.; Glaser, Adam K.; Poudel, Chetan; Vaughan, Joshua C. (June 2023, Annual Review of Analytical Chemistry)

   In recent years, there has been a revived appreciation for the importance of spatial context and morphological phenotypes for both understanding disease progression and guiding treatment decisions. Compared with conventional 2D histopathology, which is the current gold standard of medical diagnostics, nondestructive 3D pathology offers researchers and clinicians the ability to visualize orders of magnitude more tissue within their natural volumetric context. This has been enabled by rapid advances in tissue-preparation methods, high-throughput 3D microscopy instrumentation, and computational tools for processing these massive feature-rich data sets. Here, we provide a brief overview of many of these technical advances along with remaining challenges to be overcome. We also speculate on the future of 3D pathology as applied in translational investigations, preclinical drug development, and clinical decision-support assays. 

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5. Wearable Glucose Monitoring and Implantable Drug Delivery Systems for Diabetes Management

   https://doi.org/10.1002/adhm.202100194  

   Zhang, Jinyuan; Xu, Jian; Lim, Jongcheon; Nolan, James_K; Lee, Hyowon; Lee, Chi_Hwan (April 2021, Advanced Healthcare Materials)

   Abstract The global cost of diabetes care exceeds $1 trillion each year with more than $327 billion being spent in the United States alone. Despite some of the advances in diabetes care including continuous glucose monitoring systems and insulin pumps, the technology associated with managing diabetes has largely remained unchanged over the past several decades. With the rise of wearable electronics and novel functional materials, the field is well‐poised for the next generation of closed‐loop diabetes care. Wearable glucose sensors implanted within diverse platforms including skin or on‐tooth tattoos, skin‐mounted patches, eyeglasses, contact lenses, fabrics, mouthguards, and pacifiers have enabled noninvasive, unobtrusive, and real‐time analysis of glucose excursions in ambulatory care settings. These wearable glucose sensors can be integrated with implantable drug delivery systems, including an insulin pump, glucose responsive insulin release implant, and islets transplantation, to form self‐regulating closed‐loop systems. This review article encompasses the emerging trends and latest innovations of wearable glucose monitoring and implantable insulin delivery technologies for diabetes management with a focus on their advanced materials and construction. Perspectives on the current unmet challenges of these strategies are also discussed to motivate future technological development toward improved patient care in diabetes management. 

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