Ascertaining Pain in Mental Health Records:Combining Empirical and Knowledge-Based Methods for Clinical Modelling of Electronic Health Record Text

In recent years, state-of-the-art clinical Natural Language Processing (NLP), as in other domains, has been dominated by neural networks and other statistical models. In contrast to the unstructured nature of Electronic Health Record (EHR) text, biomedical knowledge is increasingly available in structured and codified forms, underpinned by curated databases, machine-readable clinical guidelines, and logically defined terminologies. This thesis examines the incorporation of external medical knowledge into clinical NLP and tests these methods on a use case of ascertaining physical pain in clinical notes of mental health records.Pain is a common reason for accessing healthcare resources and has been a growing area of research, especially its impact on mental health. Pain also presents a unique NLP problem due to its ambiguous nature and the varying circumstances in which it can be used. For these reasons, pain has been chosen as a use case, making it a good case study for the application of the methods explored in this thesis. Models are built by assimilating both structured medical knowledge and clinical NLP and leveraging the inherent relations that exist within medical ontologies. The data source used in this project is a mental health EHR database called CRIS, which contains de-identified patient records from the South London and Maudsley NHS Foundation Trust, one of the largest mental health providers in Western Europe.A lexicon of pain terms was developed to identify documents within CRIS mentioning painrelated terms. Gold standard annotations were created by conducting manual annotations on these documents. These gold standard annotations were used to build models for a binary classification task, with the objective of classifying sentences from the clinical text as “relevant”, which indicates the sentence contains relevant mentions of pain, i.e., physical pain affecting the patient, or “not relevant”, which indicates the sentence does not contain mentions of physical pain, or the mention does not relate to the patient (ex: someone else in physical pain). Two models incorporating structured medical knowledge were built:1. a transformer-based model, SapBERT, that utilises a knowledge graph of the UMLS ontology, and2. a knowledge graph embedding model that utilises embeddings from SNOMED CT, which was then used to build a random forest classifier. This was achieved by modelling the clinical pain terms and their relations from SNOMED CT into knowledge graph embeddings, thus combining the data-driven view of clinical language, with the logical view of medical knowledge.These models have been compared with NLP models (binary classifiers) that do not incorporate such structured medical knowledge:1. a transformer-based model, BERT_base, and2. a random forest classifier model.Amongst the two transformer-based models, SapBERT performed better at the classification task (F1-score: 0.98), and amongst the random forest models, the one incorporating knowledge graph embeddings performed better (F1-score: 0.94). The SapBERT model was run on sentences from a cohort of patients within CRIS, with the objective of conducting a prevalence study to understand the distribution of pain based on sociodemographic and diagnostic factors.The contribution of this research is both methodological and practical, showing the difference between a conventional NLP approach of binary classification and one that incorporates external knowledge, and further utilising the models obtained from both these approaches ina prevalence study which was designed based on inputs from clinicians and a patient and public involvement group. The results emphasise the significance of going beyond the conventional approach to NLP when addressing complex issues such as pain.<br/

Re: Non-disabled and disabled women sexual health comparison

The authors stressed the fact that the provision of sexual and reproductive health services to disabled women poses a special challenge as these women do not seek medical help when in need. We opine that the disabled subset of women deserves a compassionate and unprejudiced attitude from health care professionals towards their sexual well-being.According to WHO, sexual health is defined as - ‘a state of physical, emotional, mental and social well-being in relation to sexuality; it is not merely the absence of disease, dysfunction or infirmity. Sexual health requires a positive and respectful approach to sexuality and sexual relationships, as well as the possibility of having pleasurable and safe sexual experiences, free of coercion, discrimination and violence. For sexual health to be attained and maintained, the sexual rights of all persons must be respected, protected and fulfilled’.2 Free of discrimination and violence is the key here, wherein we suggest that disabled women need to be protected with special legal and social protection which is easily accessible to them

Scar endometriosis following caesarean section: a rare case report

Endometriosis is presence of functioning endometrial tissue (glands and stroma) outside the uterine cavity. Endometriosis can sometimes occur in previous surgical scar. Scar endometriosis is rare and difficult to diagnose. It mostly follows obstetrical and gynaecological surgeries. We present here a case report of a patient who developed scar endometriosis following a caesarean section which was dealt adequately in the subsequent caesarean section

Identifying Mentions of Pain in Mental Health Records Text: A Natural Language Processing Approach

Pain is a common reason for accessing healthcare resources and is a growing area of research, especially in its overlap with mental health. Mental health electronic health records are a good data source to study this overlap. However, much information on pain is held in the free text of these records, where mentions of pain present a unique natural language processing problem due to its ambiguous nature. This project uses data from an anonymised mental health electronic health records database. The data are used to train a machine learning based classification algorithm to classify sentences as discussing patient pain or not. This will facilitate the extraction of relevant pain information from large databases, and the use of such outputs for further studies on pain and mental health. 1,985 documents were manually triple-annotated for creation of gold standard training data, which was used to train three commonly used classification algorithms. The best performing model achieved an F1-score of 0.98 (95% CI 0.98-0.99).Comment: 5 pages, 2 tables, submitted to MEDINFO 2023 conferenc

Nurse’s knowledge and attitude regarding cervical cancer screening at a tertiary care hospital

Background: Cervical cancer is one of the commonest cancers among women which cause morbidity and mortality worldwide. Though, it is a preventable disease, most of the women with cervical cancer present in advanced stage due to lack of knowledge about the disease and screening among general population. The objective was to assess the level of knowledge and explore attitude towards cervical cancer screening among female nursing staff.Methods: A cross sectional, questionnaire based study was conducted on 34 female nursing staff in a tertiary care hospital of Uttarakhand, India in the month of January 2015. With the help of predesigned questionnaire, information was collected regarding demographic profile, knowledge about cervical cancer and attitude towards screening techniques.Results: In this study, 79% of the respondents had knowledge about screening methods for cervical cancer and 91% had knowledge about HPV vaccine. Though 82% of them were aware of pap smear and 89% had good attitude towards it, 85.29% respondent knew about colposcopy as one of the screening technique for cervical cancer. None of the respondent had undergone a pap smear themselves.Conclusions: The study showed that, female nursing staff had average knowledge and positive attitude towards cervical cancer screening. They were not aware of the routine screening guidelines and had limited understanding of different types of cervical cancer screening techniques. Hence, it is recommended that routine training should be given on regular basis to all the health care providers

Development of a Knowledge Graph Embeddings Model for Pain

Pain is a complex concept that can interconnect with other concepts such as a disorder that might cause pain, a medication that might relieve pain, and so on. To fully understand the context of pain experienced by either an individual or across a population, we may need to examine all concepts related to pain and the relationships between them. This is especially useful when modeling pain that has been recorded in electronic health records. Knowledge graphs represent concepts and their relations by an interlinked network, enabling semantic and context-based reasoning in a computationally tractable form. These graphs can, however, be too large for efficient computation. Knowledge graph embeddings help to resolve this by representing the graphs in a low-dimensional vector space. These embeddings can then be used in various downstream tasks such as classification and link prediction. The various relations associated with pain which are required to construct such a knowledge graph can be obtained from external medical knowledge bases such as SNOMED CT, a hierarchical systematic nomenclature of medical terms. A knowledge graph built in this way could be further enriched with real-world examples of pain and its relations extracted from electronic health records. This paper describes the construction of such knowledge graph embedding models of pain concepts, extracted from the unstructured text of mental health electronic health records, combined with external knowledge created from relations described in SNOMED CT, and their evaluation on a subject-object link prediction task. The performance of the models was compared with other baseline models.Comment: Accepted at AMIA 2023, New Orlean

Role of surgical management in invasive mole: a report of 2 cases and review of literature

Invasive mole is a rare gestational trophoblastic neoplasia with proliferative trophoblast invading into myometrium or uterine vasculature. Primary management of invasive mole is chemotherapy, but hysterectomy can be performed in selective cases. In this report, we discuss two cases of invasive mole, which required surgical intervention in the form of a hysterectomy. Both patients had a favorable outcome and are in remission

Pancytopenia and transient synovitis of hip joint in a SARS CoV-2 positive pregnant female: a case report

Pregnant women are at an increased risk for severe COVID-19 illness. Apart from the typical clinical manifestations, atypical presenting features of COVID-19 are also being found. We report the case of a 20 years old COVID positive antenatal patient with pancytopenia. The patient presented with scar tenderness and was taken up for emergency caesarean section at a platelet count of 5860 per microlitre. She was managed with intraoperative and postoperative transfusion of blood products. She developed chronic persistent hip pain and was diagnosed to have transient synovitis of the hip joint, which was managed conservatively. COVID-19 is a new disease with evolving clinical presentation. Pancytopenia and synovitis of hip are a rare manifestation of COVID-19 and has never been reported in a pregnant woman with COVID-19

Aggressive angiomyxoma of the vulva - a rare entity: case report and review of literature

Aggressive angiomyxoma (AA) is an extremely rare locally invasive mesenchymal tumor with a high risk of recurrence. Till date, only about 350 cases reported worldwide. Because of the rarity it should be considered as differential diagnosis whenever patient present with vulvovaginal growth. The diagnosis is clinched on histopathology. These are hormone-dependent and have estrogen and progesterone receptors. Hence sometimes GnRH agonists are used for ovarian estrogen secretion suppression but long-term use is not advocated due to side effects. A 45-year-old P4 L4 perimenopausal female presented to the GOPD with a 4×4×3 cms pedunculated painless globular mass on right labia majora. On palpation, the globular mass was firm, non-tender and with a smooth surface. Mass was excised and on gross histopathology, cut sections showed white myxoid areas. On microscopy epidermal lined tissue with stellate and spindle-shaped mesenchymal cells was found, embedded in a loose myxoid stroma with few collagen fibers. The cells were small and bland and lacked nuclear atypia. Small to medium-sized blood vessels were present with the thickened wall. Entrapped nerves and adipocytes were also present. No necrosis or mitosis was identified. All these features were suggestive of an aggressive angiomyxoma. Immunohistochemistry markers ER, PR, CD34, desmin, SMA were all positive. Imaging was done to rule out metastatic lesions and wide local excision was done around the stump with laparoscopic bilateral oophorectomy. Aggressive angiomyxoma is a rare disease. In women with asymptomatic growth in the vulvovaginal region, perineum or pelvis, aggressive angiomyxoma should be considered as a differential diagnosis. Ideal treatment is a wide local excision to prevent local recurrences, which are common and a hypoestrogenic milieu is created by either GnRH Agonists or by bilateral oophorectomy due to their hormone-sensitive nature

Fear and concerns of women delivering during coronavirus pandemic

Literature is sparse regarding the fears and concerns of women delivering during COVID 19 pandemic. We interviewed 12 women delivering during the initial first week of lockdown period. There were three key concern of women- fear of being exposed at hospital, restricted number of hospital visitors made them confined and self-isolated thereby making them bored and frustrated, risk of baby being infected. Virtual communication through mobile was seen as a major support in all serving as a means of contact with their loved ones. Understanding a pregnant women’s concern and fear during this pandemic will enable a health care worker in better counselling