Designing a Digital Medical Interview Assistant for Radiology

Radiologists rarely interact with the patients whose radiological images they are reviewing due to time and resource constraints. However, relevant information about the patient’s medical history could improve reporting performance and quality. In this work, our objective was to collect requirements for a digital medical interview assistant (DMIA) that collects the medical history from patients by means of a conversational agent and structures as well as provides the collected data to radiologists. Requirements were gathered based on a narrative literature review, a patient questionnaire and input from a radiologist. Based on these results, a system architecture for the DMIA was developed. 37 functional and 17 non-functional requirements were identified. The resulting architecture comprises five components, namely Chatbot, Natural language processing (NLP), Administration, Content Definition and Workflow Engine. To be able to quickly adapt the chatbot content according to the information needs of a specific radiological examination, there is a need for developing a sustainable process for the content generation that considers standardized data modelling as well as rewording of clinical language into consumer health vocabulary understandable to a diverse patient user group

Designing a Digital Medical Interview Assistant for Radiology.

Radiologists rarely interact with the patients whose radiological images they are reviewing due to time and resource constraints. However, relevant information about the patient's medical history could improve reporting performance and quality. In this work, our objective was to collect requirements for a digital medical interview assistant (DMIA) that collects the medical history from patients by means of a conversational agent and structures as well as provides the collected data to radiologists. Requirements were gathered based on a narrative literature review, a patient questionnaire and input from a radiologist. Based on these results, a system architecture for the DMIA was developed. 37 functional and 17 non-functional requirements were identified. The resulting architecture comprises five components, namely Chatbot, Natural language processing (NLP), Administration, Content Definition and Workflow Engine. To be able to quickly adapt the chatbot content according to the information needs of a specific radiological examination, there is a need for developing a sustainable process for the content generation that considers standardized data modelling as well as rewording of clinical language into consumer health vocabulary understandable to a diverse patient user group

A Step Forward in Cancer Informatics—It Is Mandatory to Make Guidelines Machine Readable

Clinical guidelines are general recommendations for practicing clinicians regarding prevention, diagnosis and treatment of a given disease. One of the most comprehensive and used guidelines are developed and regularly updated by the National Comprehensive Cancer Network (NCCN). Guidelines are readily available for download in portable document format (PDF). A machine-readable representation of NCCN guidelines is currently not available. In this writing, we argue on the necessity that clinical guidelines should be published in a machine-readable format. After review of the available literature, we describe the most important achievements in the field. Publication of guidelines in a machine-readable form may also be beneficial for other scientific and technical disciplines

Contemporary treatment patterns and survival of cervical cancer patients in Ethiopia.

BACKGROUND Cervical cancer is the second commonly diagnosed cancer and the second leading cause of cancer death in women in Ethiopia, with rates among the highest worldwide. However, there are limited data on cervical cancer treatment patterns and survival in the country. Herein, we examine treatment patterns and survival of cervical cancer patients treated in Tikur Anbessa Hospital Radiotherapy Center (TAHRC), the only hospital with radiotherapy facility in the country. METHODS Women with histologically verified cervical cancer who were seen in 2014 (January 1, 2014 to December 31, 2014) at TAHRC were included. Information about clinical characteristics and treatments were extracted from the patients' medical record files. The information on vital status was obtained from medical chart and through telephone calls. RESULT Among 242 patients included in the study, the median age at diagnosis was 48 years. The median waiting time for radiotherapy was 5.6 months (range 2 to 9 months). Stage migration occurred in 13% of patients while waiting for radiotherapy. Consequently, the proportion of patients with stage III or IV disease increased from 66% at first consultation to 74% at the initiation of radiotherapy. Among 151 patients treated with curative intent, only 34 (22.5%) of the patients received concurrent chemotherapy while the reaming patients received radiotherapy alone. The 5-year overall survival rate was 28.4% (20.5% in the worst-case scenario). As expected, survival was lower in patients with advanced stage at initiation of radiotherapy and in those treated as palliative care. CONCLUSION The survival of cervical cancer patients remains low in Ethiopia because of late presentation and delay in receipt of radiotherapy, leading to stage migration in substantial proportion of the cases. Concerted and coordinated multisectoral efforts are needed to promote early presentation of cervical cancer and to shorten the unacceptable, long waiting time for radiotherapy

Applications of Machine Learning in Palliative Care: A Systematic Review

Objective: To summarize the available literature on using machine learning (ML) for palliative care practice as well as research and to assess the adherence of the published studies to the most important ML best practices. Methods: The MEDLINE database was searched for the use of ML in palliative care practice or research, and the records were screened according to PRISMA guidelines. Results: In total, 22 publications using machine learning for mortality prediction (n = 15), data annotation (n = 5), predicting morbidity under palliative therapy (n = 1), and predicting response to palliative therapy (n = 1) were included. Publications used a variety of supervised or unsupervised models, but mostly tree-based classifiers and neural networks. Two publications had code uploaded to a public repository, and one publication uploaded the dataset. Conclusions: Machine learning in palliative care is mainly used to predict mortality. Similarly to other applications of ML, external test sets and prospective validations are the exception

an analysis of the ClinicalTrials.gov database

Background To evaluate the current status of prospective interventional clinical trials that includes brachytherapy (BT) procedures. Methods The records of 175,538 (100 %) clinical trials registered at ClinicalTrials.gov were downloaded on September 2014 and a database was established. Trials using BT as an intervention were identified for further analyses. The selected trials were manually categorized according to indication(s), BT source, applied dose rate, primary sponsor type, location, protocol initiator and funding source. We analyzed trials across 8 available trial protocol elements registered within the database. Results In total 245 clinical trials were identified, 147 with BT as primary investigated treatment modality and 98 that included BT as an optional treatment component or as part of the standard treatment. Academic centers were the most frequent protocol initiators in trials where BT was the primary investigational treatment modality (p < 0.01). High dose rate (HDR) BT was the most frequently investigated type of BT dose rate (46.3 %) followed by low dose rate (LDR) (42.0 %). Prostate was the most frequently investigated tumor entity in trials with BT as the primary treatment modality (40.1 %) followed by breast cancer (17.0 %). BT was rarely the primary investigated treatment modality for cervical cancer (6.8 %). Conclusion Most clinical trials using BT are predominantly in early phases, investigator-initiated and with low accrual numbers. Current investigational activities that include BT mainly focus on prostate and breast cancers. Important questions concerning the optimal usage of BT will not be answered in the near future

Radiation Therapy in Extensive Stage Small Cell Lung Cancer.

Lung cancer is the major cancer killer in the Western world, with the small cell lung cancer (SCLC) representing around 15-20% of all lung cancers. Extensive disease small cell lung cancer (ED SCLC) is found in approximately two-thirds of all cases, composed of both metastatic (M1) and non-metastatic (but presumably with tumor burden too large for locoregional-only approach) variant. Standard treatment options involve chemotherapy (CHT) over the past several decades. Radiation therapy (RT) had mostly been used in palliation of locoregional and/or metastatic disease. In contrast to its established role in treating metastatic disease, thoracic RT (TRT) had never been established as important part of the treatment aspects in this setting. In the past two decades, thoracic oncologists have witnessed wide introduction of modern RT and CHT aspects in ED SCLC, which led to more frequent use of RT and rise in the number of clinical studies. Since the pivotal study of Jeremic et al., who were the first to show importance of TRT in ED SCLC, a number of single-institutional studies have reconfirmed this observation, while recent prospective randomized trials (CREST and RTOG 0937) brought more substance to this issue. Similarly, the issue of prophylactic cranial irradiation was investigated in EORTC and the Japanese study, respectively, bringing somewhat conflicting results and calling for additional research in this setting. Future studies in ED SCLC could incorporate questions of RT dose and fractionation as well as the number of CHT cycles and type of combined Rt-CHT (sequential vs concurrent)

Applications of Machine Learning in Palliative Care: A Systematic Review.

Objective: To summarize the available literature on using machine learning (ML) for palliative care practice as well as research and to assess the adherence of the published studies to the most important ML best practices. Methods: The MEDLINE database was searched for the use of ML in palliative care practice or research, and the records were screened according to PRISMA guidelines. Results: In total, 22 publications using machine learning for mortality prediction (n = 15), data annotation (n = 5), predicting morbidity under palliative therapy (n = 1), and predicting response to palliative therapy (n = 1) were included. Publications used a variety of supervised or unsupervised models, but mostly tree-based classifiers and neural networks. Two publications had code uploaded to a public repository, and one publication uploaded the dataset. Conclusions: Machine learning in palliative care is mainly used to predict mortality. Similarly to other applications of ML, external test sets and prospective validations are the exception

Portfolio of prospective clinical trials including brachytherapy: an analysis of the ClinicalTrials.gov database

Background: To evaluate the current status of prospective interventional clinical trials that includes brachytherapy (BT) procedures. Methods: The records of 175,538 (100 %) clinical trials registered at ClinicalTrials.gov were downloaded on September 2014 and a database was established. Trials using BT as an intervention were identified for further analyses. The selected trials were manually categorized according to indication(s), BT source, applied dose rate, primary sponsor type, location, protocol initiator and funding source. We analyzed trials across 8 available trial protocol elements registered within the database. Results: In total 245 clinical trials were identified, 147 with BT as primary investigated treatment modality and 98 that included BT as an optional treatment component or as part of the standard treatment. Academic centers were the most frequent protocol initiators in trials where BT was the primary investigational treatment modality (p<0.01). High dose rate (HDR) BT was the most frequently investigated type of BT dose rate (46.3 %) followed by low dose rate (LDR) (42.0 %). Prostate was the most frequently investigated tumor entity in trials with BT as the primary treatment modality (40.1 %) followed by breast cancer (17.0 %). BT was rarely the primary investigated treatment modality for cervical cancer (6.8 %). Conclusion: Most clinical trials using BT are predominantly in early phases, investigator-initiated and with low accrual numbers. Current investigational activities that include BT mainly focus on prostate and breast cancers. Important questions concerning the optimal usage of BT will not be answered in the near future