Validation of a novel Psychosis-Implicit Association Test (P-IAT) as a diagnostic support tool.

Despite significant advances in early-intervention services for psychosis, delays in identifying patients continue to impede the delivery of prompt and effective treatments. We sought to develop and preliminarily validate a self-administered psychosis implicit association task (P-IAT) as a screening and diagnostic support tool for identifying individuals with psychotic illness in community settings. The P-IAT is a response latency task, designed to measure the extent to which individuals implicitly associate psychosis-related terms with the "self." The P-IAT was administered to 57 participants across 3 groups: healthy controls (N=19), inpatients hospitalized with active psychosis (N=19), and outpatients with psychotic disorders (N=19). Mean D-scores (the output of the task) differed significantly between the illness groups and healthy controls (Mann-Whitney U=138, p<.001). A receiver operating curve was plotted to assess the performance of D-scores in predicting a psychosis diagnosis, yielding an area under the curve of 0.81. When participant D-scores exceeded -0.24, the test achieved a specificity of 100% (sensitivity: 47%), with all 18 participants scoring above this threshold belonging to the illness groups. The discriminant performance of the P-IAT suggests its potential to augment existing screening instruments and inform referral decision making, particularly in settings with limited access to specialist providers.

Acoustic and Facial Features From Clinical Interviews for Machine Learning-Based Psychiatric Diagnosis: Algorithm Development.

BACKGROUND: In contrast to all other areas of medicine, psychiatry is still nearly entirely reliant on subjective assessments such as patient self-report and clinical observation. The lack of objective information on which to base clinical decisions can contribute to reduced quality of care. Behavioral health clinicians need objective and reliable patient data to support effective targeted interventions. OBJECTIVE: We aimed to investigate whether reliable inferences-psychiatric signs, symptoms, and diagnoses-can be extracted from audiovisual patterns in recorded evaluation interviews of participants with schizophrenia spectrum disorders and bipolar disorder. METHODS: We obtained audiovisual data from 89 participants (mean age 25.3 years; male: 48/89, 53.9%; female: 41/89, 46.1%): individuals with schizophrenia spectrum disorders (n=41), individuals with bipolar disorder (n=21), and healthy volunteers (n=27). We developed machine learning models based on acoustic and facial movement features extracted from participant interviews to predict diagnoses and detect clinician-coded neuropsychiatric symptoms, and we assessed model performance using area under the receiver operating characteristic curve (AUROC) in 5-fold cross-validation. RESULTS: The model successfully differentiated between schizophrenia spectrum disorders and bipolar disorder (AUROC 0.73) when aggregating face and voice features. Facial action units including cheek-raising muscle (AUROC 0.64) and chin-raising muscle (AUROC 0.74) provided the strongest signal for men. Vocal features, such as energy in the frequency band 1 to 4 kHz (AUROC 0.80) and spectral harmonicity (AUROC 0.78), provided the strongest signal for women. Lip corner-pulling muscle signal discriminated between diagnoses for both men (AUROC 0.61) and women (AUROC 0.62). Several psychiatric signs and symptoms were successfully inferred: blunted affect (AUROC 0.81), avolition (AUROC 0.72), lack of vocal inflection (AUROC 0.71), asociality (AUROC 0.63), and worthlessness (AUROC 0.61). CONCLUSIONS: This study represents advancement in efforts to capitalize on digital data to improve diagnostic assessment and supports the development of a new generation of innovative clinical tools by employing acoustic and facial data analysis.

Clinician Perspectives on Using Computational Mental Health Insights From Patients' Social Media Activities: Design and Qualitative Evaluation of a Prototype.

BACKGROUND: Previous studies have suggested that social media data, along with machine learning algorithms, can be used to generate computational mental health insights. These computational insights have the potential to support clinician-patient communication during psychotherapy consultations. However, how clinicians perceive and envision using computational insights during consultations has been underexplored. OBJECTIVE: The aim of this study is to understand clinician perspectives regarding computational mental health insights from patients' social media activities. We focus on the opportunities and challenges of using these insights during psychotherapy consultations. METHODS: We developed a prototype that can analyze consented patients' Facebook data and visually represent these computational insights. We incorporated the insights into existing clinician-facing assessment tools, the Hamilton Depression Rating Scale and Global Functioning: Social Scale. The design intent is that a clinician will verbally interview a patient (eg, How was your mood in the past week?) while they reviewed relevant insights from the patient's social media activities (eg, number of depression-indicative posts). Using the prototype, we conducted interviews (n=15) and 3 focus groups (n=13) with mental health clinicians: psychiatrists, clinical psychologists, and licensed clinical social workers. The transcribed qualitative data were analyzed using thematic analysis. RESULTS: Clinicians reported that the prototype can support clinician-patient collaboration in agenda-setting, communicating symptoms, and navigating patients' verbal reports. They suggested potential use scenarios, such as reviewing the prototype before consultations and using the prototype when patients missed their consultations. They also speculated potential negative consequences: patients may feel like they are being monitored, which may yield negative effects, and the use of the prototype may increase the workload of clinicians, which is already difficult to manage. Finally, our participants expressed concerns regarding the prototype: they were unsure whether patients' social media accounts represented their actual behaviors; they wanted to learn how and when the machine learning algorithm can fail to meet their expectations of trust; and they were worried about situations where they could not properly respond to the insights, especially emergency situations outside of clinical settings. CONCLUSIONS: Our findings support the touted potential of computational mental health insights from patients' social media account data, especially in the context of psychotherapy consultations. However, sociotechnical issues, such as transparent algorithmic information and institutional support, should be addressed in future endeavors to design implementable and sustainable technology.

Internet Search Activity of Young People With Mood Disorders Who Are Hospitalized for Suicidal Thoughts and Behaviors: Qualitative Study of Google Search Activity.

BACKGROUND: Little is known about the internet search activity of people with suicidal thoughts and behaviors (STBs). This data source has the potential to inform both clinical and public health efforts, such as suicide risk assessment and prevention. OBJECTIVE: We aimed to evaluate the internet search activity of suicidal young people to find evidence of suicidal ideation and behavioral health-related content. METHODS: Individuals aged between 15 and 30 years (N=43) with mood disorders who were hospitalized for STBs provided access to their internet search history. Searches that were conducted in the 3-month period prior to hospitalization were extracted and manually evaluated for search themes related to suicide and behavioral health. RESULTS: A majority (27/43, 63%) of participants conducted suicide-related searches. Participants searched for information that exactly matched their planned or chosen method of attempting suicide in 21% (9/43) of cases. Suicide-related search queries also included unusual suicide methods and references to suicide in popular culture. A majority of participants (33/43, 77%) had queries related to help-seeking themes, including how to find inpatient and outpatient behavioral health care. Queries related to mood and anxiety symptoms were found among 44% (19/43) of participants and included references to panic disorder, the inability to focus, feelings of loneliness, and despair. Queries related to substance use were found among 44% (19/43) of participants. Queries related to traumatic experiences were present among 33% (14/43) of participants. Few participants conducted searches for crisis hotlines (n=3). CONCLUSIONS: Individuals search the internet for information related to suicide prior to hospitalization for STBs. The improved understanding of the search activity of suicidal people could inform outreach, assessment, and intervention strategies for people at risk. Access to search data may also benefit the ongoing care of suicidal patients.

A Social Media Study on Mental Health Status Transitions Surrounding Psychiatric Hospitalizations.

For people diagnosed with a mental illness, psychiatric hospitalization is one step in a long journey, consisting of clinical recovery such as removal of symptoms, and social reintegration involving resuming social roles and responsibilities, overcoming stigma and self-maintenance of the condition. Both clinical recovery and social reintegration need to go hand-in-hand for the overall well-being of individuals. However, research exploring social media for mental health has considered narrower, disjoint conceptualizations of people with mental illness - either as a patient or as a support-seeker. In this paper, we combine medical records with social media data of 254 consented individuals who have experienced a psychiatric hospitalization to address this gap. Adopting a theory-driven, Gaussian Mixture modeling approach, we provide a taxonomy of six heterogeneous behavioral patterns characterizing peoples' mental health status transitions around hospitalizations. Then we present an empirically derived framework, based on feedback from clinical researchers, to understand peoples' trajectories around clinical recovery and social reintegration. Finally, to demonstrate the utility of this taxonomy and the empirical framework, we assess social media signals that are indicative of individuals' reintegration trajectories post-hospitalization. We discuss the implications of combining peoples' clinical and social experiences in mental health care and the opportunities this intersection presents to post-discharge support and technology-based interventions for mental health.

Identifying signals associated with psychiatric illness utilizing language and images posted to Facebook.

Prior research has identified associations between social media activity and psychiatric diagnoses; however, diagnoses are rarely clinically confirmed. Toward the goal of applying novel approaches to improve outcomes, research using real patient data is necessary. We collected 3,404,959 Facebook messages and 142,390 images across 223 participants (mean age = 23.7; 41.7% male) with schizophrenia spectrum disorders (SSD), mood disorders (MD), and healthy volunteers (HV). We analyzed features uploaded up to 18 months before the first hospitalization using machine learning and built classifiers that distinguished SSD and MD from HV, and SSD from MD. Classification achieved AUC of 0.77 (HV vs. MD), 0.76 (HV vs. SSD), and 0.72 (SSD vs. MD). SSD used more (P < 0.01) perception words (hear, see, feel) than MD or HV. SSD and MD used more (P < 0.01) swear words compared to HV. SSD were more likely to express negative emotions compared to HV (P < 0.01). MD used more words related to biological processes (blood/pain) compared to HV (P < 0.01). The height and width of photos posted by SSD and MD were smaller (P < 0.01) than HV. MD photos contained more blues and less yellows (P < 0.01). Closer to hospitalization, use of punctuation increased (SSD vs HV), use of negative emotion words increased (MD vs. HV), and use of swear words increased (P < 0.01) for SSD and MD compared to HV. Machine-learning algorithms are capable of differentiating SSD and MD using Facebook activity alone over a year in advance of hospitalization. Integrating Facebook data with clinical information could one day serve to inform clinical decision-making.

Designing a Clinician-Facing Tool for Using Insights From Patients' Social Media Activity: Iterative Co-Design Approach.

BACKGROUND: Recent research has emphasized the need for accessing information about patients to augment mental health patients' verbal reports in clinical settings. Although it has not been introduced in clinical settings, computational linguistic analysis on social media has proved it can infer mental health attributes, implying a potential use as collateral information at the point of care. To realize this potential and make social media insights actionable to clinical decision making, the gaps between computational linguistic analysis on social media and the current work practices of mental health clinicians must be bridged. OBJECTIVE: This study aimed to identify information derived from patients' social media data that can benefit clinicians and to develop a set of design implications, via a series of low-fidelity (lo-fi) prototypes, on how to deliver the information at the point of care. METHODS: A team of clinical researchers and human-computer interaction (HCI) researchers conducted a long-term co-design activity for over 6 months. The needs-affordances analysis framework was used to refine the clinicians' potential needs, which can be supported by patients' social media data. On the basis of those identified needs, the HCI researchers iteratively created 3 different lo-fi prototypes. The prototypes were shared with both groups of researchers via a videoconferencing software for discussion and feedback. During the remote meetings, potential clinical utility, potential use of the different prototypes in a treatment setting, and areas of improvement were discussed. RESULTS: Our first prototype was a card-type interface that supported treatment goal tracking. Each card included attribute levels: depression, anxiety, social activities, alcohol, and drug use. This version confirmed what types of information are helpful but revealed the need for a glanceable dashboard that highlights the trends of these information. As a result, we then developed the second prototype, an interface that shows the clinical state and trend. We found that focusing more on the changes since the last visit without visual representation can be more compatible with clinicians' work practices. In addition, the second phase of needs-affordances analysis identified 3 categories of information relevant to patients with schizophrenia: symptoms related to psychosis, symptoms related to mood and anxiety, and social functioning. Finally, we developed the third prototype, a clinical summary dashboard that showed changes from the last visit in plain texts and contrasting colors. CONCLUSIONS: This exploratory co-design research confirmed that mental health attributes inferred from patients' social media data can be useful for clinicians, although it also revealed a gap between computational social media analyses and clinicians' expectations and conceptualizations of patients' mental health states. In summary, the iterative co-design process crystallized design directions for the future interface, including how we can organize and provide symptom-related information in a way that minimizes the clinicians' workloads.

Nipple and areolar hyperpigmentation secondary to the use of estradiol spray on the ipsilateral forearm skin: a report of two cases.

BACKGROUND: Metered transdermal estradiol spray may cause ipsilateral hyperpigmentation of the nipple and areola. CASES: Two women who were using transdermal estradiol spray for the treatment of postmenopausal symptoms developed hyperpigmentation of the ipsilateral areola and nipple. The appearance was variable in onset and did not appear to be related to the duration of use. CONCLUSION: Estradiol transdermal spray can have the rare side effect of ipsilateral nipple and areolar hyperpigmentation. The time of onset can vary.

Hair growth in patients alopecia areata totalis after treatment with simvastatin and ezetimibe.

Alopecia areata is an autoimmune disorder characterized by the sudden development of a circumscribed patch of non-scarring hair loss on the scalp or any hair-bearing surface.The presentation of this disorder can be hair loss in a single circumscribed patch, complete loss of hair on the scalp (alopecia totalis) or complete loss of hair on the entire body (alopecia universalis). The following cases involve two patients with treatment-refractory alopecias that benefited significantly after treatment with a combination of ezetimibe and simvastatin, in addition to the continuation of intra-lesional corticosteroid injections. In this report, the known immunomodulatory effects of statins in combination with ezetimibe are discussed along with the known histopathologic findings of autoimmune alopecia. Major histocompatibility complex class II (MHC-II) and intracellular adhesion molecule-1 (ICAM-1) appear to be involved in both the immunomodulatory effects of statins and the pathophysiology of autoimmune alopecia.

Tinea versicolor clearance with oral isotretinoin therapy.

We describe the incidental clearance of preexisting tinea versicolor skin infection with the treatment of oral isotretinoin therapy for acne vulgaris.

Plantar wart treatment with combination imiquimod and salicylic acid pads.

Treatment of plantar warts is often difficult and may be painful, often employing destructive treatment modalities. We report the successful treatment of a patient with a large plantar wart using Imiquimod 5% cream under occlusion with a 40% salicylic acid pad. This combination treatment modality likely allows successful delivery of Imiquimod through the thick skin on the plantar surface. Once penetrated, an anti-viral state is created by upregulating specific cytokines to eradicate the human papilloma virus (HPV).

Plantar wart treatment with combination imiquimod and salicylic acid pads.

Treatment of plantar warts is often difficult and may be painful, often employing destructive treatment modalities. We report the successful treatment of a patient with a large plantar wart using Imiquimod 5% cream under occlusion with a 40% salicylic acid pad. This combination treatment modality likely allows successful delivery of Imiquimod through the thick skin on the plantar surface. Once penetrated, an anti-viral state is created by upregulating specific cytokines to eradicate the human papilloma virus (HPV).