Commentary: the ethical challenges of machine learning in psychiatry: a focus on data, diagnosis, and treatment

被引:1
|
作者
Barron, Daniel S. [1 ,2 ,3 ,4 ]
机构
[1] Yale Univ, Dept Psychiat, New Haven, CT 06520 USA
[2] Univ Washington, Dept Anesthesiol & Pain Med, Seattle, WA 98195 USA
[3] Harvard Univ, Brigham & Womens Hosp, Dept Psychiat, Boston, MA 02115 USA
[4] Harvard Univ, Brigham & Womens Hosp, Dept Anesthesiol & Pain Med, Boston, MA 02115 USA
关键词
Schizophrenia; diagnosis; machine learning;
D O I
10.1017/S0033291721001008
中图分类号
B849 [应用心理学];
学科分类号
040203 ;
摘要
The clinical interview is the psychiatrist's data gathering procedure. However, the clinical interview is not a defined entity in the way that 'vitals' are defined as measurements of blood pressure, heart rate, respiration rate, temperature, and oxygen saturation. There are as many ways to approach a clinical interview as there are psychiatrists; and trainees can learn as many ways of performing and formulating the clinical interview as there are instructors (Nestler, 1990). Even in the same clinical setting, two clinicians might interview the same patient and conduct very different examinations and reach different treatment recommendations. From the perspective of data science, this mismatch is not one of personal style or idiosyncrasy but rather one of uncertain salience: neither the clinical interview nor the data thereby generated is operationalized and, therefore, neither can be rigorously evaluated, tested, or optimized.
引用
收藏
页码:2522 / 2524
页数:3
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