The project for objective measures using computational psychiatry technology (PROMPT): Rationale, design, and methodology.

INTRODUCTION: Depressive and neurocognitive disorders are debilitating conditions that account for the leading causes of years lived with disability worldwide. However, there are no biomarkers that are objective or easy-to-obtain in daily clinical practice, which leads to difficulties in assessing treatment response and developing new drugs. New technology allows quantification of features that clinicians perceive as reflective of disorder severity, such as facial expressions, phonic/speech information, body motion, daily activity, and sleep. METHODS: Major depressive disorder, bipolar disorder, and major and minor neurocognitive disorders as well as healthy controls are recruited for the study. A psychiatrist/psychologist conducts conversational 10-min interviews with participants ≤10 times within up to five years of follow-up. Interviews are recorded using RGB and infrared cameras, and an array microphone. As an option, participants are asked to wear wrist-band type devices during the observational period. Various software is used to process the raw video, voice, infrared, and wearable device data. A machine learning approach is used to predict the presence of symptoms, severity, and the improvement/deterioration of symptoms. DISCUSSION: The overall goal of this proposed study, the Project for Objective Measures Using Computational Psychiatry Technology (PROMPT), is to develop objective, noninvasive, and easy-to-use biomarkers for assessing the severity of depressive and neurocognitive disorders in the hopes of guiding decision-making in clinical settings as well as reducing the risk of clinical trial failure. Challenges may include the large variability of samples, which makes it difficult to extract the features that commonly reflect disorder severity. TRIAL REGISTRATION: UMIN000021396, University Hospital Medical Information Network (UMIN).

Evaluating depression with multimodal wristband-type wearable device: screening and assessing patient severity utilizing machine-learning.

OBJECTIVE: We aimed to develop a machine learning algorithm to screen for depression and assess severity based on data from wearable devices. METHODS: We used a wearable device that calculates steps, energy expenditure, body movement, sleep time, heart rate, skin temperature, and ultraviolet light exposure. Depressed patients and healthy volunteers wore the device continuously for the study period. The modalities were compared hourly between patients and healthy volunteers. XGBoost was used to build machine learning models and 10-fold cross-validation was applied for the validation. RESULTS: Forty-five depressed patients and 41 healthy controls participated, creating a combined 5,250 days' worth of data. Heart rate, steps, and sleep were significantly different between patients and healthy volunteers in some comparisons. Similar differences were also observed longitudinally when patients' symptoms improved. Based on seven days' data, the model identified symptomatic patients with 0.76 accuracy and predicted Hamilton Depression Rating Scale-17 scores with a 0.61 correlation coefficient. Skin temperature, sleep time-related features, and the correlation of those modalities were the most significant features in machine learning. LIMITATIONS: The small number of subjects who participated in this study may have weakened the statistical significance of the study. There are differences in the demographic data among groups although we performed a correction for multiple comparisons. Validation in independent datasets was not performed, although 10-fold cross validation with the internal data was conducted. CONCLUSION: The results indicated that utilizing wearable devices and machine learning may be useful in identifying depression as well as assessing severity.

Severe neonatal CMV infection complicated with thrombotic microangiopathy successfully treated with ganciclovir.

We report a neonate of severe cytomegalovirus (CMV) infection who presented vomiting, severe thrombocytopenia and thrombotic microangiopathy (TMA). He showed occasional vomiting at 3 weeks of age and visited us with systemic petechiae at 29 days old. Platelet was markedly decreased to 18,000/µL and fragmented red blood cells were increased in the peripheral blood. Intravenous ganciclovir (GCV) administration was started at 35 days old after detection of CMV in the peripheral blood. His normal values of T-cell receptor excision circles (TREC) and signal joint kappa-deleting recombination excision circles (sjKREC) excluded the possibility of severe immunodeficiency. Congenital CMV infection was denied later, when CMV of the dried blood spot obtained for neonatal mass-screening at 4 days old was proved negative. We provided 6-week treatment with no side effect such as myelosuppression. The left hearing abnormality found at first was improved along with other symptoms. GCV seems to be effective and safe for severe neonatal CMV infection.