Opportunities for Smartphones in Clinical Care: The Future of Mobile Mood Monitoring

Sandstrom, Gillian M.; Lathia, Neal; Mascolo, Cecilia; Rentfrow, Peter J.

doi:10.4088/jcp.15com10054

Cited by 29 publications

(12 citation statements)

References 7 publications

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“…The use of machine learning for clinical predictions is growing in popularity [25], although significant challenges lie ahead. For example, small datasets or the presence of rare conditions might limit the ability of an algorithm to generalize.…”

Section: Discussionmentioning

confidence: 99%

The need to approximate the use-case in clinical machine learning

et al. 2017

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The availability of smartphone and wearable sensor technology is leading to a rapid accumulation of human subject data, and machine learning is emerging as a technique to map those data into clinical predictions. As machine learning algorithms are increasingly used to support clinical decision making, it is vital to reliably quantify their prediction accuracy. Cross-validation (CV) is the standard approach where the accuracy of such algorithms is evaluated on part of the data the algorithm has not seen during training. However, for this procedure to be meaningful, the relationship between the training and the validation set should mimic the relationship between the training set and the dataset expected for the clinical use. Here we compared two popular CV methods: record-wise and subject-wise. While the subject-wise method mirrors the clinically relevant use-case scenario of diagnosis in newly recruited subjects, the record-wise strategy has no such interpretation. Using both a publicly available dataset and a simulation, we found that record-wise CV often massively overestimates the prediction accuracy of the algorithms. We also conducted a systematic review of the relevant literature, and found that this overly optimistic method was used by almost half of the retrieved studies that used accelerometers, wearable sensors, or smartphones to predict clinical outcomes. As we move towards an era of machine learning-based diagnosis and treatment, using proper methods to evaluate their accuracy is crucial, as inaccurate results can mislead both clinicians and data scientists.

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Section: Discussionmentioning

confidence: 99%

The need to approximate the use-case in clinical machine learning

et al. 2017

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“…The use of machine learning for clinical predictions is growing in popularity [21]. This is in part because the computing power of electronic devices has increased to the point that current mobile devices are as powerful as supercomputers from a few decades ago.…”

Section: Discussionmentioning

confidence: 99%

Voodoo Machine Learning for Clinical Predictions

Saeb

Lonini

Jayaraman

et al. 2016

Preprint

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Abstract. The availability of smartphone and wearable sensor technology is leading to a rapid accumulation of human subject data, and machine learning is emerging as a technique to map that data into clinical predictions. As machine learning algorithms are increasingly used to support clinical decision making, it is important to reliably quantify their prediction accuracy. Cross-validation is the standard approach for evaluating the accuracy of such algorithms; however, several cross-validations methods exist and only some of them are statistically meaningful.Here we compared two popular cross-validation methods: record-wise and subject-wise. Using both a publicly available dataset and a simulation, we found that record-wise cross-validation often massively overestimates the prediction accuracy of the algorithms. We also found that this erroneous method is used by almost half of the retrieved studies that used accelerometers, wearable sensors, or smartphones to predict clinical outcomes. As we move towards an era of machine learning based diagnosis and treatment, using proper methods to evaluate their accuracy is crucial, as erroneous results can mislead both clinicians and data scientists.

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“…Mobile phones are perhaps the most widely used in contemporary computational social science efforts, owing to their ubiquity and the extensive range of raw datastreams collected by a single platform (Harari et al, ). The ubiquity of smart phones means there is a potential to engage diverse and hard‐to reach populations (Sandstrom, Lathia, Mascolo, & Rentfrow, ). Given that mobile phones are already being charged and cared for by participants, they do not pose much burden beyond privacy concerns.…”

Section: Practical Considerationsmentioning

confidence: 99%

Automated sensing of daily activity: A new lens into development

Barbaro

2019

Developmental Psychobiology

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Rapidly maturing technologies for sensing and activity recognition can provide unprecedented access to the complex structure daily activity and interaction, promising new insight into the mechanisms by which experience shapes developmental outcomes. Motion data, autonomic activity, and “snippets” of audio and video recordings can be conveniently logged by wearable sensors (Lazer et al., 2009). Machine learning algorithms can process these signals into meaningful markers, from child and parent behavior to outcomes such as depression or teenage drinking. Theoretically motivated aspects of daily activity can be combined and synchronized to examine reciprocal effects between children’s behaviors and their environments or internal processes. Captured over longitudinal time, such data provide a new opportunity to study the processes by which individual differences emerge and stabilize. This paper introduces the reader to developments in sensing and activity recognition with implications for developmental phenomena across the lifespan, sketching a framework for leveraging mobile sensors for transactional analyses that bridge micro‐ and longitudinal‐ timescales of development. It finishes by detailing resources and best practices to facilitate the next generation of developmentalists to contribute to this emerging area.

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Opportunities for Smartphones in Clinical Care: The Future of Mobile Mood Monitoring

Cited by 29 publications

References 7 publications

The need to approximate the use-case in clinical machine learning

The need to approximate the use-case in clinical machine learning

Voodoo Machine Learning for Clinical Predictions

Automated sensing of daily activity: A new lens into development

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