Olivia Goldhill, a writer who focuses on philosophy and psychology, has written an interesting post on a paper published by Colin Walsh, a data scientist at the Vanderbilt University Medical Center, co-authored with Jessica D Ribeiro and Joseph C Franklin.
Walsh and his colleagues have created machine-learning algorithms that predict, with unnerving accuracy, the likelihood that a patient will attempt suicide. In trials, results have been 80-90% accurate when predicting whether someone will attempt suicide within the next two years, and 92% accurate in predicting whether someone will attempt suicide within the next week.
The prediction is based on data that’s widely available from all hospital admissions, including age, gender, zip codes, medications, and prior diagnoses. Walsh and his team gathered data on 5,167 patients from Vanderbilt University Medical Center that had been admitted with signs of self-harm or suicidal ideation. They read each of these cases to identify the 3,250 instances of suicide attempts.
Please do read Olivia’s piece further about important questions related to role of computers in such sensitive matters and the complexity of such algorithms.
The paper is published in the Sage Journal Clinical Psychological Science in April (Vol 5, Issue 3, 2017) and can be accessed from here. The authors write:
We developed machine learning algorithms that accurately predicted future suicide attempts (AUC = 0.84, precision = 0.79, recall = 0.95, Brier score = 0.14). Moreover, accuracy improved from 720 days to 7 days before the suicide attempt, and predictor importance shifted across time. These findings represent a step toward accurate and scalable risk detection and provide insight into how suicide attempt risk shifts over time.
I am guessing that ensemble methods were used for modeling the algorithms and that Deep Learning has not been used. I have reached out to the authors and am awaiting their response.
Adapted from: Deep Learning, the book co-authored by Ian Goodfellow, Yoshua Bengio and Aaron Courville and “Representation Learning: A Review and New Perspectives” by Yoshua Bengio, Aaron Courville and Pascal Vincent (click here for a copy of this paper).
The power of software has been the ability to codify tasks that can be clearly defined and listed. AI, in its early days was fed problems that were intellectually hard for humans but relatively easy for computers – tasks that could still be formally described via mathematical rules. The real challenge were problems that humans solved intuitively (automatically) but hard for computers to “get” – such as recognizing images or spoken words with context and continuity.
Instead of formally specifying all this intuitive knowledge, computers must learn from experience, fed as data. It must build its own specifications of these experiences as a hierarchy of concepts. Complicated concepts are built on simpler ones. Thus, the degree of abstraction increases as you get to complicated concepts. If this hierarchy of concepts is visualized as graphs, then it would be deep, or one with multiple layers and hence this approach is called AI Deep Learning.
Thus, Deep Learning models “either involve a greater amount of composition of learned functions or learned concepts than traditional machine learning does”. Now, its graphs (and the concepts) are heavily dependent on the choice of data representation on which they are applied. That is why data representation or feature engineering is so important.
“Such feature engineering is important but labor-intensive and highlights the weakness of current learning algorithms: their inability to extract and organize the discriminative information from the data. Feature engineering is a way to take advantage of human ingenuity and prior knowledge to compensate for that weakness. In order to expand the scope and ease of applicability of machine learning, it would be highly desirable to make learning algorithms less dependent on feature engineering, so that novel applications could be constructed faster, and more importantly, to make progress towards Artificial Intelligence (AI). An AI must fundamentally understand the world around us, and we argue that this can only be achieved if it can learn to identify and disentangle the underlying explanatory factors hidden in the observed milieu of low-level sensory data.”