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Last Posted: May 19, 2022
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Machine learning paves the way toward the prevention of mental health crises
Nature Medicine, May 18, 2022

Experiencing a mental health crisis has a detrimental impact on a patient’s life. A machine learning algorithm trained retrospectively with electronic health records can predict almost 60% of mental health crises 4 weeks in advance. Prospective evaluation of the algorithm in clinical practice reveals its potential to enable preemptive interventions.

A high-generalizability machine learning framework for predicting the progression of Alzheimer’s disease using limited data
C Wang et al, NPJ Digital Medicine, April 12, 2022

Alzheimer’s disease is a neurodegenerative disease that imposes a substantial financial burden on society. A number of machine learning studies have been conducted to predict the speed of its progression, which varies widely among different individuals, for recruiting fast progressors in future clinical trials. However, because the data in this field are very limited, two problems have yet to be solved: the first is that models built on limited data tend to induce overfitting and have low generalizability, and the second is that no cross-cohort evaluations have been done.

Genetic origins of schizophrenia find common ground
CO Lyegbe et al, Nature, April 11,2022

There has long been debate about whether the genetic component of complex disorders, such as schizophrenia, is attributable mainly to rare or common DNA variants1. Two studies now provide evidence for key roles of both types of variant. The first study identified hundreds of common genetic variants that each have a tiny influence on schizophrenia risk. The second discovered a handful of rare variants, each of which have a large effect. Together, these studies show that common and rare genetic causes of illness might often disrupt the same biological processes that lead to disease.

A machine-learning based objective measure for ALS disease severity
FG Vieira et al, NPJ Digital Medicine, April 8, 2022

We developed a machine learning (ML) based objective measure for ALS disease severity based on voice samples and accelerometer measurements from a four-year longitudinal dataset. 584 people living with ALS consented and carried out prescribed speaking and limb-based tasks. 542 participants contributed 5814 voice recordings, and 350 contributed 13,009 accelerometer samples, while simultaneously measuring ALSFRS-R scores.

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