Single- and Multi-label Prediction of Burden on Families of Schizophrenia Patients

Bermejo, Pablo; Lucas, Marta; Rodríguez-Montes, José Antonio; Tárraga, Pedro J.; Lucas, Javier; Gámez, José A.; Puerta, José M.

doi:10.1007/978-3-642-38326-7_18

Cited by 4 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Alzheimer's Disease COMPASS [200], SVM [198,200], DT [200], Genetic Algorithm [199], NN [214] Imaging [198,200], Biological [199], Smart Meter [214] Anxiety BN [226], ARM [226], DT [227,228], Regression [228], RF [228], kmeans clustering [229], NB [230], SVM [231] Electronic Health Records [226], Survey [227,230] Parkinson's Disease SVM [209] Imaging [209], Clinical Assessment [209] Post-traumatic Stress Disorder k-means clustering [229], kNN [250], NN [250], NLP [251], RF [201], Regression [201], SVM [201,250] Audio [250], Biological [201], Clinical Notes [251], Clinical Assessment [201], Social Media [229] Psychosis Gaussian Processes [206], SVM [207,208] Biological [206], Clinical Assessment [206], Survey [207,…”

Section: Mental Health Application ML Technique(s) Data Typementioning

confidence: 99%

Machine learning in mental health: A systematic scoping review of methods and applications

Shatte¹,

Hutchinson²,

Teague³

2018

Preprint

View full text Add to dashboard Cite

ObjectiveThis paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice. Materials and MethodsEight health and information technology research databases were searched using the terms "big data" or "machine learning" and "mental health". Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type and size, and study results. Articles were then synthesised via narrative review. ResultsThree hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health; and, (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's Disease. ML techniques used included support vector machines, decision trees, neural networks, latent dirichlet allocation, and clustering. Discussion and ConclusionOverall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration.With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to improve other areas of psychological functioning. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field. BACKGROUND AND SIGNIFICANCE

show abstract

Section: Mental Health Application ML Technique(s) Data Typementioning

confidence: 99%

Machine learning in mental health: A systematic scoping review of methods and applications

Shatte¹,

Hutchinson²,

Teague³

2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Based on a set of clinical cases extracted from Electronic Patient Records (EPRs) and a set of medical interventions, the system is able to predict if a variation is likely to happen in a specific clinical case. More recently, Bermejo et al [29] proposed a system to predict the output of questionnaires used to measure the level of anxiety or depression in caregivers of schizophrenia patients. The goal of this prediction system is to anticipate an appropriate treatment or advice for the family caregivers from Primary Care consultations.…”

Section: Prediction Systems In Healthcare and Surgerymentioning

confidence: 99%

Automatic matching of surgeries to predict surgeons’ next actions

Forestier

Petitjean

Riffaud

et al. 2017

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

This work shows that, even from the low-level description of surgeries and without other sources of information, it is often possible to predict the next surgical task when the conditions are consistent with the previously recorded surgeries. We also showed that our method is able to assess when there is actually a large divergence between the predictions and decide that it is not reasonable to make a prediction.

show abstract

“…Furthermore, tagging with a given picture might help the automated process to discard other tags or being more sure about some others; for instance, if a picture is tagged with label b o a t , it is more probable that the scene also belongs to tag sea rather than street . In general, multilabelling can be found in many applications such as bioinformatics (Clare, Clare, & King, ; Elisseeff & Weston, ), scene classification (Boutell, Brown, Luo, & Shen, ), or mental health (Bermejo et al ), among others.…”

Section: Introductionmentioning

confidence: 99%

Adapting the CMIM algorithm for multilabel feature selection. A comparison with existing methods

2017

Self Cite

View full text Add to dashboard Cite

The multilabel paradigm has recently attracted the attention of the machine learning community, multilabel problems being those which do not have only one class but several binomial classes instead. Although intensive research has been carried on lately into the multilabel classification paradigm, this is not the case of feature subset selection methods. In this work, we propose an adaptation of the well‐known CMIM feature selection algorithm, which is capable of approximating the conditional multivariate mutual information of each candidate attribute with respect to the whole set of labels. This capacity to search any degree of interaction among labels is the reason why our proposal performs better than other state‐of‐the‐art algorithms when the dataset on which it is run contains correlated labels.

show abstract

Single- and Multi-label Prediction of Burden on Families of Schizophrenia Patients

Cited by 4 publications

References 8 publications

Machine learning in mental health: A systematic scoping review of methods and applications

Machine learning in mental health: A systematic scoping review of methods and applications

Automatic matching of surgeries to predict surgeons’ next actions

Adapting the CMIM algorithm for multilabel feature selection. A comparison with existing methods

Contact Info

Product

Resources

About