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Vitamins

Vitamins

Vitamins, derived from the Latin word for life, are essential organic substances required in small quantities for optimal growth and overall well-being. Unlike other organic nutrients, vitamins don't act as sources of energy or building materials but rather facilitate these nutrients' utilization by the body. Vitamins are predominantly coenzymes, assisting enzymes in specific chemical actions, like the oxidation of glucose for energy involving B vitamins. Most vitamins are not produced...

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Published on: January 11, 2020

Efficient Prediction of Vitamin B Deficiencies via Machine-Learning Using Routine Blood Test Results in Patients With

Hidetaka Tamune^1,2,3, Jumpei Ukita^3,4, Yu Hamamoto^1,2

¹Department of Neuropsychiatry, Tokyo Metropolitan Tama Medical Center, Tokyo, Japan.

Frontiers in Psychiatry

|March 11, 2020

Summary

Machine learning models can predict vitamin B deficiencies in psychiatric patients using routine blood tests. This approach aids in rapid risk assessment and treatment decisions for intense psychiatric episodes.

Keywords:

decision support techniques or decision making early diagnosis folic acid machine learning random forest classifier vitamin B deficiency

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Area of Science:

Psychiatry
Biochemistry
Computational Biology

Background:

Vitamin B deficiencies are prevalent globally and linked to psychiatric symptoms.
Epidemiology of vitamin B deficiencies in patients with acute psychiatric episodes is understudied.
Current diagnostic methods for vitamin deficiencies are time-consuming and costly.

Purpose of the Study:

To investigate the epidemiology of vitamin B deficiencies (B1, B12, B9) in patients with intense psychiatric episodes.
To develop and validate machine-learning models for predicting these deficiencies.
To identify patient characteristics and routine blood test results that can predict vitamin deficiencies.

Main Methods:

Analysis of 497 consecutive patients at high risk of self-harm or harm to others.
Training and validation of machine-learning models (k-NN, logistic regression, SVM, random forest).
Prediction of vitamin B1, B12, and folate deficiencies using demographic data and 29 routine blood test results.

Main Results:

Prevalence of vitamin B1, B12, and folate deficiencies were 22.5%, 16.1%, and 14.5%, respectively.
Random forest model showed good predictive performance (AUCs 0.716, 0.599, 0.796) on unseen data.
Identified associations between vitamin deficiencies, complete blood count, alkaline phosphatase (ALP), and thyroid-stimulating hormone (TSH).

Conclusions:

Machine learning effectively predicts vitamin deficiencies in patients with active psychiatric symptoms.
This predictive approach can accelerate risk stratification and guide treatment decisions for vitamin replacement therapy.
Further research is needed to validate external generalizability and assess clinical utility and cost-effectiveness.