Predicting persistent depressive symptoms in older adults: A machine learning approach to personalised mental healthcare

Hatton, Chris; Paton, Lewis William; McMillan, Dean; Cussens, James; Gilbody, Simon; Tiffin, Paul A.

doi:10.1016/j.jad.2018.12.095

Cited by 78 publications

(43 citation statements)

References 17 publications

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“…Another study, based on demographic and psychometric data from 284 patients, aimed to predict the likelihood of older adults having persistent depressive symptoms after 12 months, using a ML approach. This approach provided a superior predictive performance using ML compared to logistic regression (mean accuracy 72% vs. 67% p < 0.0001) [14]. Similar accuracy (75%) was also con irmed when using deep learning neural networks to model fall risk on the basis of accelerometer data to in particular multi-task learning, effectively assess fall risk on the basis of wearable sensor data [15].…”

Section: Examples Of ML and Dl Implementation In The Medical Care Of mentioning

confidence: 65%

Artificial Intelligence in the healthcare of older people

Makaetova-Ladinska¹,

Harwood²,

Maltby³

2020

Arch Psychiatr Ment Health

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Clinical applications of Artifi cial Intelligence (AI) in healthcare are relatively rare. The high expectations in relation to data analysis infl uencing general healthcare have not materialized, with few exceptions, and then predominantly in the fi eld of rare diseases, oncology and pathology, and interpretation of laboratory results. While electronic health records, introduced over the last decade or so in the UK have increased access to medical and treatment histories of patients, diagnoses, medications, treatment plans, immunization dates, allergies, radiology images, laboratory and test results, these have potential for evidence-based tools that providers can use to make decisions about a patient's care, as well as streamline workfl ow. In the following text, we review the advances achieved using machine learning and deep learning technology, as well as robot use and telemedicine in the healthcare of older people. Key points: 1. Artifi cial Intelligence use is extensively explored in prevention, diagnosis, novel drug designs and after-care. 2. AI studies on older adults include a small number of patients and lack reproducibility needed for their wider clinical use in different clinical settings and larger populations. 3. Telemedicine and robot assisted technology are well received by older service users. 4. Ethical concerns need to be resolved prior to wider AI use in routine clinical setting. seem to face a number of barriers in contacting communitybased primary health care, such as limited access, lack of standardized information systems and care pathways [3], all necessary to address their complex health care and social care needs. Indeed, older adults have much higher prevalence of nearly all major chronic and long-term conditions. In addition, they are more likely to succumb to adverse health events, such as a falls or infections, and these can lead to dramatic changes in their physical and mental wellbeing even after an apparently minor incident [4]. However, person and familyfocused care, self-management resources, and successful collaborative practice have been all highlighted as facilitators of good health care provision both by older people and their families [3]. All the above places the importance of diagnosis, monitoring of disease risks and their prevention, as well as management and optimizing of geriatric syndromes in the community for both older people living independenly or in 24h care facilities. In particular, identi ication of health issues/ diagnosis, support/treatment needs evaluation, development

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Section: Examples Of ML and Dl Implementation In The Medical Care Of mentioning

confidence: 65%

Artificial Intelligence in the healthcare of older people

Makaetova-Ladinska¹,

Harwood²,

Maltby³

2020

Arch Psychiatr Ment Health

View full text Add to dashboard Cite

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“…Articles were included if they investigated the use of machine learning approaches in predicting treatment outcomes or find more homogeneous clusters (in an adult patient population). Searching the databases resulted in 2,277 unique records, 72 remained after title and abstract screening, and 16 studies were included in our analysis after full-text screening (26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41). The results of these studies (and the techniques that were used) are reported in the supplementary tables, below we narratively highlight the key findings.…”

Section: State Of the Art: What Has Been Done?mentioning

confidence: 99%

“…Interestingly, four studies assessed the added value of harnessing "advanced" machine learning techniques in classifying patients, by comparing the performance of their model to the accuracy of a logistic regression model (26,29,32,41). The machine learning models outperformed the logistic regression in three of these studies (26,32,41).…”

Section: Predicting Outcomes From Clinical Variablesmentioning

confidence: 99%

The Predictive Validity of Machine Learning Models in the Classification and Treatment of Major Depressive Disorder: State of the Art and Future Directions

2020

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Major depressive disorder imposes a substantial disease burden worldwide, ranking as the third leading contributor to global disability. In spite of its ubiquity, classifying and treating depression has proven troublesome. One argument put forward to explain this predicament is the heterogeneity of patients diagnosed with the disorder. Recently, many areas of daily life have witnessed the surge of machine learning techniques, computational approaches to elucidate complex patterns in large datasets, which can be employed to make predictions and detect relevant clusters. Due to the multidimensionality at play in the pathogenesis of depression, it is suggested that machine learning could contribute to improving classification and treatment. In this paper, we investigated literature focusing on the use of machine learning models on datasets with clinical variables of patients diagnosed with depression to predict treatment outcomes or find more homogeneous subgroups. Identified studies based on best practices in the field are evaluated. We found 16 studies predicting outcomes (such as remission) and identifying clusters in patients with depression. The identified studies are mostly still in proof-of-concept phase, with small datasets, lack of external validation, and providing single performance metrics. Larger datasets, and models with similar variables present across these datasets, are needed to develop accurate and generalizable models. We hypothesize that harnessing natural language processing to obtain data 'hidden' in clinical texts might prove useful in improving prediction models. Besides, researchers will need to focus on the conditions to feasibly implement these models to support psychiatrists and patients in their decision-making in practice. Only then we can enter the realm of precision psychiatry.

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“…Such models may generalize to novel predictor data. In contrast to traditional statistical approaches, ML focuses on prediction rather than explanation (Hatton et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

A Machine-Based Prediction Model of ADHD Using CPT Data

Slobodin

Yahav

Berger

2020

Front. Hum. Neurosci.

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Despite the popularity of the continuous performance test (CPT) in the diagnosis of attention-deficit/hyperactivity disorder (ADHD), its specificity, sensitivity, and ecological validity are still debated. To address some of the known shortcomings of traditional analysis and interpretation of CPT data, the present study applied a machine learningbased model (ML) using CPT indices for the Prediction of ADHD.Using a retrospective factorial fitting, followed by a bootstrap technique, we trained, cross-validated, and tested learning models on CPT performance data of 458 children aged 6-12 years (213 children with ADHD and 245 typically developed children). We used the MOXO-CPT version that included visual and auditory stimuli distractors. Results showed that the ML proposed model performed better and had a higher accuracy than the benchmark approach that used clinical data only. Using the CPT total score (that included all four indices: Attention, Timeliness, Hyperactivity, and Impulsiveness), as well as four control variables [age, gender, day of the week (DoW), time of day (ToD)], provided the most salient information for discriminating children with ADHD from their typically developed peers. This model had an accuracy rate of 87%, a sensitivity rate of 89%, and a specificity rate of 84%. This performance was 34% higher than the best-achieved accuracy of the benchmark model. The ML detection model could classify children with ADHD with high accuracy based on CPT performance. ML model of ADHD holds the promise of enhancing, perhaps complementing, behavioral assessment and may be used as a supportive measure in the evaluation of ADHD.

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Predicting persistent depressive symptoms in older adults: A machine learning approach to personalised mental healthcare

Cited by 78 publications

References 17 publications

Artificial Intelligence in the healthcare of older people

Artificial Intelligence in the healthcare of older people

The Predictive Validity of Machine Learning Models in the Classification and Treatment of Major Depressive Disorder: State of the Art and Future Directions

A Machine-Based Prediction Model of ADHD Using CPT Data

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