A Beautiful Mind: How ML Algorithms Can Help Create Psychiatric Applications


Blog #9          : A Beautiful Mind: How ML Algorithms Can Help Create Psychiatric Applications
Name               : Kathleen 蔣慧敏
Student ID      : D0726920
Source             : https://analyticsindiamag.com/a-beautiful-mind-how-ml-algorithms-can-help-create-psychiatric-applications/

In ML psychiatry, there are four different algorithms that can be used such as classification, regression, clustering and modelling the available information of the individuals. As the article explained, these algorithms can be based on three techniques:
  • Supervised techniques are used when the cases are labelled into groups of diagnosis,
  • Unsupervised techniques are used in situations when the unlabelled samples are to be divided into groups of related cases and
  • Semi-supervised techniques are used when both labelled and unlabelled cases are present.

How ML Algorithms Are Used in Psychiatry
In machine-readable psychiatry, we can use ML algorithms such as random forest, neural networks, SVM and decision trees.
The random forest method is a technique that uses bootstrapping and jackknife statistical techniques to estimate the bias and variance. Random forest method is used for classification and prediction. It is also appropriate when the datasets are non-linear and involve higher order interactions because this method is independent of any parametric assumptions.
The Support Vector Machine (SVM) is a method for supervised classification. In psychiatry, we can use SVM to differentiate healthy from depressed using exhaustive techniques like feature weight ranking and feature scoring.
Neural Network can be used to understand the changes in the brain of psychiatric individuals. Neuroimaging studies can also predict conditions from mild cognitive impairment to Alzheimer. By using ML with data fusion of structural and functional task-based MRI, we can also identify depression.
Natural Language Processing (NLP) is a method that can be used to study how a person responds and interacts.
Requirements for A Machine-Readable Psychiatry
  1.  Data Availability: Large amount of datasets is very crucial in order to get accurate results.
  2.  Data Management: Proper management is very important to avoid harmful diagnosis.
  3.  Longitudinal Data: Prospectively collected data is more suitable for longitudinal analysis and retrospective data for cross-sectional analysis.
  4.  Indirect Factors: Indirect factors such as age, gender, heartbeat, respiration, culture, drug use are also important in affecting an individual’s condition.


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