Identification of social determinants of health using multi-label classification of electronic health record clinical notes.

OBJECTIVES: Social determinants of health (SDH), key contributors to health, are rarely systematically measured and collected in the electronic health record (EHR). We investigate how to leverage clinical notes using novel applications of multi-label learning (MLL) to classify SDH in mental health and substance use disorder patients who frequent the emergency department. METHODS AND MATERIALS: We labeled a gold-standard corpus of EHR clinical note sentences (N = 4063) with 6 identified SDH-related domains recommended by the Institute of Medicine for inclusion in the EHR. We then trained 5 classification models: linear-Support Vector Machine, K-Nearest Neighbors, Random Forest, XGBoost, and bidirectional Long Short-Term Memory (BI-LSTM). We adopted 5 common evaluation measures: accuracy, average precision-recall (AP), area under the curve receiver operating characteristic (AUC-ROC), Hamming loss, and log loss to compare the performance of different methods for MLL classification using the F1 score as the primary evaluation metric. RESULTS: Our results suggested that, overall, BI-LSTM outperformed the other classification models in terms of AUC-ROC (93.9), AP (0.76), and Hamming loss (0.12). The AUC-ROC values of MLL models of SDH related domains varied between (0.59-1.0). We found that 44.6% of our study population (N = 1119) had at least one positive documentation of SDH. DISCUSSION AND CONCLUSION: The proposed approach of training an MLL model on an SDH rich data source can produce a high performing classifier using only unstructured clinical notes. We also provide evidence that model performance is associated with lexical diversity by health professionals and the auto-generation of clinical note sentences to document SDH.

Information needs and perceptions of chatbots for hypertension medication self-management: a mixed methods study.

OBJECTIVE: Chatbots have potential to deliver interactive self-management interventions but have rarely been studied in the context of hypertension or medication adherence. The objective of this study was to better understand patient information needs and perceptions of chatbots to support hypertension medication self-management. MATERIALS AND METHODS: Mixed methods were used to assess self-management needs and preferences for using chatbots. We purposively sampled adults with hypertension who were prescribed at least one medication. Participants completed questionnaires on sociodemographics, health literacy, self-efficacy, and technology use. Semi-structured interviews were conducted, audio-recorded, and transcribed verbatim. Quantitative data were analyzed using descriptive statistics, and qualitative data were analyzed using applied thematic analysis. RESULTS: Thematic saturation was met after interviewing 15 participants. Analysis revealed curiosity toward chatbots, and most perceived them as humanlike. The majority were interested in using a chatbot to help manage medications, refills, communicate with care teams, and for accountability toward self-care tasks. Despite general enthusiasm, there were concerns with chatbots providing too much information, making demands for lifestyle changes, invading privacy, and usability issues with deployment on smartphones. Those with overall positive perceptions toward chatbots were younger and taking fewer medications. DISCUSSION: Chatbot-related informational needs were consistent with existing self-management research, and many felt chatbots would be valuable if customizable and compatible with patient portals, pharmacies, or health apps. CONCLUSION: Although most were not familiar with chatbots, patients were interested in interacting with them, but this varied. This research informs future design and functionalities of conversational interfaces to support hypertension self-management.

Conversational Agents for Chronic Disease Self-Management: A Systematic Review.

We conducted a systematic literature review to assess how conversational agents have been used to facilitate chronic disease self-management. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework was used. Literature was searched across five databases, and we included full-text articles that contained primary research findings for text-based conversational agents focused on self-management for chronic diseases in adults. 1,606 studies were identified, and 12 met inclusion criteria. Outcomes were largely focused on usability of conversational agents, and participants mostly reported positive attitudes with some concerns related to privacy and shallow content. In several studies, there were improvements on the Patient Health Questionnaire (p<0.05), Generalized Anxiety Disorder Scale (p=0.004), Perceived Stress Scale (p=0.048), Flourishing Scale (p=0.032), and Overall Anxiety Severity and Impairment Scale (p<0.05). There is early evidence that suggests conversational agents are acceptable, usable, and may be effective in supporting self-management, particularly for mental health.

Fracturas del cuello del fémur en el niño / Femur neck fractures in child

Se revisan 27 fracturas traumáticas del cuello del femur en pacientes de 2 a 17 años, encontrandose la mayor incidencia entre los 6 y los 8 años. Los trabajos publicados y las casuisticas son poco numerosas. es una lesion rara. Dividimos las fracturas en transepifisiarias, transcervicales, cervicotrocantericas e intertrocantéricas. Las mas frecuentes son las transcervicales y las basicervicales, 20 casos. En general estas fracturas on el resultado de un traumatismo severo acompañandose con frecuencia de lesiones asociadas esteo-articulares y viscerales. El 81.5/100 fueron desalojadas. El tratamiento en las fracturas desalojadas o en las intertrocatericas fue ortopedico con un resultado clinico y radiológico excelente. Se controlaron 20 casos con un promedio de 2 años y medio. La consolidación se obtuvo en la mayoría de los casos a los 4 meses...

Anestesia en oftalmologia: revision / Anesthesia in ophthalmology: revision

Los pacientes sometidos a cirugia ocular, generalmente se encuentran en las edades extremas de la vida; ninos (estrabismo, enfermedades congenitas) y ancianos (cataratas, desprendimiento de retina). Muchos factores como: edad del paciente, estado fisico, tipo de procedimiento quirurgico, procedimiento anestesico y riesgo anestesico influyen en los indices de mortalidad perioperatoria. Generalmente el mayor peso del riesgo lo llevan la edad del paciente, el estado fisico y el manejo anestesico, mas que el procedimiento quirurgico en si. La mayoria de las cirugias oculares son electivas, lo que facilita que el paciente pueda ser llevado a cirugia en condiciones optimas