Smartphone as a monitoring tool for bipolar disorder: a systematic review including data analysis, machine learning algorithms and predictive modelling

Antosik-Wójcińska, Anna; Dominiak, Monika; Chojnacka, Magdalena; Kaczmarek-Majer, Katarzyna; Opara, Karol; Radziszewska, Weronika; Olwert, Anna; Święcicki, Łukasz

doi:10.1016/j.ijmedinf.2020.104131

Cited by 55 publications

(28 citation statements)

References 59 publications

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“…Decision tree is a kind of machine learning, which is a prediction model [ 16 , 22 , 23 ]. The process of decision analysis of decision tree [ 24 ] is like the trunk of a tree, which reflects the process of grouping learning data, so it is called decision tree.…”

Section: Methodsmentioning

confidence: 99%

“…Antosik-Wojcinska et al [ 16 ] found that the voice data generated in the process of mobile phone calls and mobile phone use could reflect the social and physical activities of the actor, and had the characteristics of objectivity, which could be used as an effective marker to reflect the mood state. Mood is an important diagnostic feature in the diagnosis of depression, so voice data can be used as one of the diagnostic data of depression.…”

Section: Introductionmentioning

confidence: 99%

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A Convenient and Low-Cost Model of Depression Screening and Early Warning Based on Voice Data Using for Public Mental Health

Chen

Pan

2021

IJERPH

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Depression is a common mental health disease, which has great harm to public health. At present, the diagnosis of depression mainly depends on the interviews between doctors and patients, which is subjective, slow and expensive. Voice data are a kind of data that are easy to obtain and have the advantage of low cost. It has been proved that it can be used in the diagnosis of depression. The voice data used for modeling in this study adopted the authoritative public data set, which had passed the ethical review. The features of voice data were extracted by Python programming, and the voice features were stored in the format of CSV files. Through data processing, a big database, containing 1479 voice feature samples, was generated for modeling. Then, the decision tree screening model of depression was established by 10-fold cross validation and algorithm selection. The experiment achieved 83.4% prediction accuracy on voice data set. According to the prediction results of the model, the patients can be given early warning and intervention in time, so as to realize the health management of personal depression.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Convenient and Low-Cost Model of Depression Screening and Early Warning Based on Voice Data Using for Public Mental Health

Chen

Pan

2021

IJERPH

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“…One of the common and widely used dataset is the PIMA Indian Diabetes dataset available from the UCI repository. Some of the publications presented a comparative study on the impact of the different data mining techniques, and other publications are presented to propose an improvement on current methods and results in a new method for diabetes prediction [15,16].…”

Section: Related Workmentioning

confidence: 99%

Improving the Accuracy of Diabetes Diagnosis Applications through a Hybrid Feature Selection Algorithm

Ju-sheng

Safara

2021

Neural Process Lett

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Artificial intelligence is a future and valuable tool for early disease recognition and support in patient condition monitoring. It can increase the reliability of the cure and decision making by developing useful systems and algorithms. Healthcare workers, especially nurses and physicians, are overworked due to a massive and unexpected increase in the number of patients during the coronavirus pandemic. In such situations, artificial intelligence techniques could be used to diagnose a patient with life-threatening illnesses. In particular, diseases that increase the risk of hospitalization and death in coronavirus patients, such as high blood pressure, heart disease and diabetes, should be diagnosed at an early stage. This article focuses on diagnosing a diabetic patient through data mining techniques. If we are able to diagnose diabetes in the early stages of the disease, we can force patients to stay home and care for their health, so the risk of being infected with the coronavirus would be reduced. The proposed method has three steps: preprocessing, feature selection and classification. Several combinations of Harmony search algorithm, genetic algorithm, and particle swarm optimization algorithm are examined with K-means for feature selection. The combinations have not examined before for diabetes diagnosis applications. K-nearest neighbor is used for classification of the diabetes dataset. Sensitivity, specificity, and accuracy have been measured to evaluate the results. The results achieved indicate that the proposed method with an accuracy of 91.65% outperformed the results of the earlier methods examined in this article.

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“…In the last decades, with the increase of technologies and the spreading of intelligent objects, the way medicine and healthcare are practiced is rapidly changing. In 2019, the World Health Organization officially proposed Digital health as new discipline that combines Artificial Intelligence, Internet of Things, Big Data and Data Analytics techniques for the health sector 1 . Mobile Health and Remote Monitoring, Sensors and Ambient Assisted Living together with Artificial Intelligence, Robotics and Data Analytics are just some examples of this new research branch [8].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Incremental Semi-supervised Fuzzy Clustering for Bipolar Disorder Episode Prediction

et al. 2020

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Bipolar Disorder (BD) is a chronic mental illness characterized by changing episodes from euthymia (healthy state) through depression and mania to the mixed states. In this context, data collected through the interaction of patients with smartphones enable the creation of predictive models to support the early prediction of a starting episode. Previous research on predicting a new BD episode use mostly supervised learning methods that require labeled data and hence force a filtering of the available data to retain only those data that have valid labels (from the psychiatric assessment). To avoid limitations of supervised learning, in this paper we investigate the use of a semi-supervised learning approach that combines both labeled and unlabeled data to derive a model for BD episode prediction. Specifically we apply the DISSFCM (Dynamic Incremental Semi-Supervised Fuzzy C-Means) algorithm which offers the possibility to process in an incremental fashion the data stream of the voice signal captured by the smartphone, thus exploiting the evolving time structure of data which is ignored by static learning methods. DISS-FCM processes data in form of chunks and creates a dynamic collection of clusters thanks to a splitting mechanism that generates new clusters to better capture the hidden geometrical structure of data. This gives DISSFCM the ability to detect changes in data and dynamically adapt the model to them, thus improving the prediction accuracy. Preliminary results on real-world data collected at the Department of Affective Disorders, Institute of Psychiatry and Neurology in Warsaw (Poland) show that DISSFCM is able to predict some of healthy episodes (euthymia) and disease episodes even when only 25% of labeled data are available. Moreover DISSFM performs better than its previous version without split (ISSFCM) and it also overcomes the batch algorithm (SSFCM) that uses the whole dataset to create the model.

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Smartphone as a monitoring tool for bipolar disorder: a systematic review including data analysis, machine learning algorithms and predictive modelling

Cited by 55 publications

References 59 publications

A Convenient and Low-Cost Model of Depression Screening and Early Warning Based on Voice Data Using for Public Mental Health

A Convenient and Low-Cost Model of Depression Screening and Early Warning Based on Voice Data Using for Public Mental Health

Improving the Accuracy of Diabetes Diagnosis Applications through a Hybrid Feature Selection Algorithm

Dynamic Incremental Semi-supervised Fuzzy Clustering for Bipolar Disorder Episode Prediction

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