Use of Machine Learning Algorithms Based on Text, Audio, and Video Data in the Prediction of Anxiety and Posttraumatic Stress in General and Clinical Populations: A Systematic Review.

Research in machine learning (ML) algorithms using natural behavior (i.e., text, audio, and video data) suggests that these techniques could contribute to personalization in psychology and psychiatry. However, a systematic review of the current state of the art is missing. Moreover, individual studies often target ML experts who may overlook potential clinical implications of their findings. In a narrative accessible to mental health professionals, we present a systematic review conducted in 5 psychology and 2 computer science databases. We included 128 studies that assessed the predictive power of ML algorithms using text, audio, and/or video data in the prediction of anxiety and posttraumatic stress disorder. Most studies (n = 87) were aimed at predicting anxiety, while the remainder (n = 41) focused on posttraumatic stress disorder. They were mostly published since 2019 in computer science journals and tested algorithms using text (n = 72) as opposed to audio or video. Studies focused mainly on general populations (n = 92) and less on laboratory experiments (n = 23) or clinical populations (n = 13). Methodological quality varied, as did reported metrics of the predictive power, hampering comparison across studies. Two-thirds of studies, which focused on both disorders, reported acceptable to very good predictive power (including high-quality studies only). The results of 33 studies were uninterpretable, mainly due to missing information. Research into ML algorithms using natural behavior is in its infancy but shows potential to contribute to diagnostics of mental disorders, such as anxiety and posttraumatic stress disorder, in the future if standardization of methods, reporting of results, and research in clinical populations are improved.

Learning multiview face subspaces and facial pose estimation using independent component analysis.

An independent component analysis (ICA) based approach is presented for learning view-specific subspace representations of the face object from multiview face examples. ICA, its variants, namely independent subspace analysis (ISA) and topographic independent component analysis (TICA), take into account higher order statistics needed for object view characterization. In contrast, principal component analysis (PCA), which de-correlates the second order moments, can hardly reveal good features for characterizing different views, when the training data comprises a mixture of multiview examples and the learning is done in an unsupervised way with view-unlabeled data. We demonstrate that ICA, TICA, and ISA are able to learn view-specific basis components unsupervisedly from the mixture data. We investigate results learned by ISA in an unsupervised way closely and reveal some surprising findings and thereby explain underlying reasons for the emergent formation of view subspaces. Extensive experimental results are presented.

[Preliminary report about the screening program for colorectal cancer by sequential fecal occult blood in Wuhan area for 4 years].

OBJECTIVE: To evaluate the sequential fecal occult blood test (SFOBT) program for the screening of colorectal cancer and elucidate the prevalence of colorectal cancer in Wuhan area. METHODS: At 19 screening sites, 63,961 residents were recruited as target population according to random cluster and stratified sampling for four years (between 2005 and 2008). Residents aged over 40 years old received SFOBT. Those with positive SFOBT underwent colonoscopy. RESULTS: The target population was 63,961. There were 25,837 people whose age was over 40. Finally, 7784 participants received the SFOBT screening, with a medium age of 56 years old. The positive rate of SFOBT was 12.3% (956 persons). Of the 956 persons, 240 participants underwent colonoscopy. Colorectal cancer was found in 14 cases (6.5%), gastric cancer in 2 cases (0.9%), colorectal adenoma in 53 cases(24.8%), colorectal inflammation in 80 cases (37.3%) and hemorrhoids in 65 cases (30.4%). CONCLUSIONS: The prevalence of colorectal cancer is relatively high in Wuhan area. The SFOBT is available and feasible in screening early changes of colorectal cancer.