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A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health
Modern machine learning approaches have led to performant diagnostic models for a variety of health conditions. Several machine learning approaches, such as decision trees and deep neural networks, can, in principle, approximate any function. However, this power can be considered to be both a gift and a curse, as the propensity toward overfitting is magnified when the input data are heterogeneous and high dimensional and the output class is highly nonlinear. This issue can especially plague diagnostic systems that predict behavioral and psychiatric conditions that are diagnosed with subjective criteria. An emerging solution to this issue is crowdsourcing, where crowd workers are paid to annotate complex behavioral features in return for monetary compensation or a gamified experience. These labels can then be used to derive a diagnosis, either directly or by using the labels as inputs to a diagnostic machine learning model. This viewpoint describes existing work in this emerging field and discusses ongoing challenges and opportunities with crowd-powered diagnostic systems, a nascent field of study. With the correct considerations, the addition of crowdsourcing to human-in-the-loop machine learning workflows for the prediction of complex and nuanced health conditions can accelerate screening, diagnostics, and ultimately access to care.
Journal Article
Criminals and their scientists : the history of criminology in international perspective
This title provides a history of criminology as a history of science and practice. The chapters examine the discourse on crime and criminals that surfaced as part of different discourses and practices, including the activities of the police and the courts, parliamentary debates and media reports.
Book
Multimodal deep learning for dementia classification using text and audio
Dementia is a progressive neurological disorder that affects the daily lives of older adults, impacting their verbal communication and cognitive function. Early diagnosis is important to enhance the lifespan and quality of life for affected individuals. Despite its importance, diagnosing dementia is a complex process. Automated machine learning solutions involving multiple types of data have the potential to improve the process of automated dementia screening. In this study, we build deep learning models to classify dementia cases from controls using the Pitt Cookie Theft dataset from DementiaBank, a database of short participant responses to the structured task of describing a picture of a cookie theft. We fine-tune Wav2vec and Word2vec baseline models to make binary predictions of dementia from audio recordings and text transcripts, respectively. We conduct experiments with four versions of the dataset: (1) the original data, (2) the data with short sentences removed, (3) text-based augmentation of the original data, and (4) text-based augmentation of the data with short sentences removed. Our results indicate that synonym-based text data augmentation generally enhances the performance of models that incorporate the text modality. Without data augmentation, models using the text modality achieve around 60% accuracy and 70% AUROC scores, and with data augmentation, the models achieve around 80% accuracy and 90% AUROC scores. We do not observe significant improvements in performance with the addition of audio or timestamp information into the model. We include a qualitative error analysis of the sentences that are misclassified under each study condition. This study provides preliminary insights into the effects of both text-based data augmentation and multimodal deep learning for automated dementia classification.
Journal Article
The Mueller Report
The only book with exclusive analysis by the Pulitzer Prize-winning staff of The Washington Post, and the most complete and authoritative available. Read the findings of the Special Counsel's investigation into Russian interference in the 2016 election, complete with accompanying analysis by the Post reporters who've covered the story from the beginning. This edition from The Washington Post/Scribner contains: --The long-awaited report -- An introduction by The Washington Post titled \"A President, a Prosecutor, and the Protection of American Democracy\"--A timeline of the major events of the Special Counsel's investigation from May 2017, when Robert Mueller was appointed, to the present day --A guide to individuals involved, including in the Special Counsel's Office, the Department of Justice, the FBI, the Trump Campaign, the White House, the Trump legal defense team, and the Russians --Key documents in the Special Counsel's investigation, including filings pertaining to General Michael T. Flynn, Paul Manafort, Michael Cohen, Roger Stone, and the Russian internet operation in St. Petersburg. Each document is introduced and explained by Washington Post reporters. One of the most urgent and important investigations ever conducted, the Mueller inquiry focuses on Donald Trump, his presidential campaign, and Russian interference in the 2016 election, and draws on the testimony of dozens of witnesses and the work of some of the country's most seasoned prosecutors. The Special Counsel's investigation looms as a turning point in American history. The Mueller Report is essential reading for all citizens concerned about the fate of the presidency and the future of our democracy.
Mobile detection of autism through machine learning on home video: A development and prospective validation study
The standard approaches to diagnosing autism spectrum disorder (ASD) evaluate between 20 and 100 behaviors and take several hours to complete. This has in part contributed to long wait times for a diagnosis and subsequent delays in access to therapy. We hypothesize that the use of machine learning analysis on home video can speed the diagnosis without compromising accuracy. We have analyzed item-level records from 2 standard diagnostic instruments to construct machine learning classifiers optimized for sparsity, interpretability, and accuracy. In the present study, we prospectively test whether the features from these optimized models can be extracted by blinded nonexpert raters from 3-minute home videos of children with and without ASD to arrive at a rapid and accurate machine learning autism classification.
We created a mobile web portal for video raters to assess 30 behavioral features (e.g., eye contact, social smile) that are used by 8 independent machine learning models for identifying ASD, each with >94% accuracy in cross-validation testing and subsequent independent validation from previous work. We then collected 116 short home videos of children with autism (mean age = 4 years 10 months, SD = 2 years 3 months) and 46 videos of typically developing children (mean age = 2 years 11 months, SD = 1 year 2 months). Three raters blind to the diagnosis independently measured each of the 30 features from the 8 models, with a median time to completion of 4 minutes. Although several models (consisting of alternating decision trees, support vector machine [SVM], logistic regression (LR), radial kernel, and linear SVM) performed well, a sparse 5-feature LR classifier (LR5) yielded the highest accuracy (area under the curve [AUC]: 92% [95% CI 88%-97%]) across all ages tested. We used a prospectively collected independent validation set of 66 videos (33 ASD and 33 non-ASD) and 3 independent rater measurements to validate the outcome, achieving lower but comparable accuracy (AUC: 89% [95% CI 81%-95%]). Finally, we applied LR to the 162-video-feature matrix to construct an 8-feature model, which achieved 0.93 AUC (95% CI 0.90-0.97) on the held-out test set and 0.86 on the validation set of 66 videos. Validation on children with an existing diagnosis limited the ability to generalize the performance to undiagnosed populations.
These results support the hypothesis that feature tagging of home videos for machine learning classification of autism can yield accurate outcomes in short time frames, using mobile devices. Further work will be needed to confirm that this approach can accelerate autism diagnosis at scale.
Journal Article
المحارب الدبلوماسي : معارك أحد مقاتلي القبعات الخضر من واشنطن إلى أفغانستان
في هذا الكتاب «المحارب الدبلوماسي : معارك أحد مقاتلي القبعات الخضر من واشنطن إلى أفغانستان»، يتحدث والتز عن تجربته المباشرة الفريدة، ويكشف عن المشاهد والأصوات والعواطف والتعقيدات التي تنطوي عليها الحرب في أفغانستان، كما يسلط الضوء على قضايا السياسة التي عصفت بالمجهود الحربي هناك طوال العقد الماضي، من تجارة المخدرات، والإصابات بين المدنيين، ونقص الموارد مقارنة بحالة العراق، إلى استراتيجية التحالف الشاملة، وفي الوقت نفسه يشير والتز إلى أن استقرار أفغانستان والمنطقة يظل أمرا حيويا للأمن القومي الأمريكي، وأن الالتزام الطويل الأجل، على غرار كوريا الجنوبية أو ألمانيا، أمر حتمي إذا ما أرادت الولايات المتحدة الأمريكية أن تظل آمنة.
Book
Compact subsets of autism screening items predict clinical diagnoses with a machine learning analysis of the QCHAT-10
Early identification improves life outcomes for individuals with autism. This study addresses a central question: do compact subsets of the most predictive QCHAT-10 items, when fed into machine learning (ML) models trained to reproduce the full questionnaire’s screening result, generalize to predicting clinician-established autism diagnoses in independent clinical settings? We applied ML to the 10-question QCHAT-10, training models on New Zealand (
n
= 1054) and Saudi Arabian (
n
= 506) datasets with QCHAT-derived labels and testing on Polish data with clinical diagnoses (
n
= 252). Recursive Feature Elimination identified four-item models retaining three common features: eye contact, following gaze direction, and pretend play. When tested on clinically-diagnosed Polish cases at the 0.3 prediction threshold, the New Zealand model achieved an AUROC of 85% ± 13 (sensitivity 91%, specificity 50%), while the Saudi model reached 87% ± 11 (sensitivity 84%, specificity 80%), compared to the Polish four-item model’s cross-validation AUROC of 91% ± 5. These findings demonstrate partial transfer from the prediction of assessment scores to clinical diagnosis. The convergence on eye contact, gaze following, and pretend play suggests these behaviors represent robust autism risk markers. Compact assessment tools offer advantages, including reduced burden, shortened administration, and simplified deployment, with direct applications for targeted digital phenotyping.
Journal Article
Non-Invasive Biosensing for Healthcare Using Artificial Intelligence: A Semi-Systematic Review
The rapid development of biosensing technologies together with the advent of deep learning has marked an era in healthcare and biomedical research where widespread devices like smartphones, smartwatches, and health-specific technologies have the potential to facilitate remote and accessible diagnosis, monitoring, and adaptive therapy in a naturalistic environment. This systematic review focuses on the impact of combining multiple biosensing techniques with deep learning algorithms and the application of these models to healthcare. We explore the key areas that researchers and engineers must consider when developing a deep learning model for biosensing: the data modality, the model architecture, and the real-world use case for the model. We also discuss key ongoing challenges and potential future directions for research in this field. We aim to provide useful insights for researchers who seek to use intelligent biosensing to advance precision healthcare.
Journal Article
Individualized Stress Mobile Sensing Using Self-Supervised Pre-Training
Stress is widely recognized as a major contributor to a variety of health issues. Stress prediction using biosignal data recorded by wearables is a key area of study in mobile sensing research because real-time stress prediction can enable digital interventions to immediately react at the onset of stress, helping to avoid many psychological and physiological symptoms such as heart rhythm irregularities. Electrodermal activity (EDA) is often used to measure stress. However, major challenges with the prediction of stress using machine learning include the subjectivity and sparseness of the labels, a large feature space, relatively few labels, and a complex nonlinear and subjective relationship between the features and outcomes. To tackle these issues, we examined the use of model personalization: training a separate stress prediction model for each user. To allow the neural network to learn the temporal dynamics of each individual’s baseline biosignal patterns, thus enabling personalization with very few labels, we pre-trained a one-dimensional convolutional neural network (1D CNN) using self-supervised learning (SSL). We evaluated our method using the Wearable Stress and Affect Detection(WESAD) dataset. We fine-tuned the pre-trained networks to the stress-prediction task and compared against equivalent models without any self-supervised pre-training. We discovered that embeddings learned using our pre-training method outperformed the supervised baselines with significantly fewer labeled data points: the models trained with SSL required less than 30% of the labels to reach equivalent performance without personalized SSL. This personalized learning method can enable precision health systems that are tailored to each subject and require few annotations by the end user, thus allowing for the mobile sensing of increasingly complex, heterogeneous, and subjective outcomes such as stress.
Journal Article