Disease computable phenotype is a data model designed to identify specific clinical conditions or characteristics, which automatically extracts information from clinical databases such as electronic health records through algorithms. Phenotypic data for rare diseases often reside in unstructured text. Due to the scarcity of rare disease cases, atypical symptoms, and insufficient physician experience, misdiagnosis and underdiagnosis rates remain high. In this context, the application of computable phenotype technology holds promise for improving the accuracy and efficiency of rare disease diagnosis. This article reviews the current research status, challenges, and opportunities of computable phenotype technology in biomedicine, particularly in the field of rare diseases, and proposes a development and validation framework for rare disease computable phenotypes, aiming to provide research and development insights for computable phenotypes to empower the diagnosis and treatment of rare diseases.

Objective The aim is to understand the common scenarios of non-medical use of prescription opioids(NMUPO)and analyze the potential facilitating and hindering factors in the regulatory process of NMUPO from the perspective of healthcare professionals.Methods Healthcare professionals in local hospitals were surveyed through a two-stage purposive sampling from June to August 2022 in Ningbo,China.The survey was conducted using a semi-structured questionnaire on topics,and thematic analysis were used to identify and summarise key themes and patterns.Results A total of 75 participants were included,the average age was(43.9±7.2)years,and 54(72.0%)were male.The most common NMUPO scenarios involved middle-aged males pretending acute severe pain to obtain injectable opioids.The facilitating and hindering factors related to the regulation of NMUPO can be categorized into three types:institutional governance,technical support,and individual behaviors.At the institutional level,facilitating factors included strict national prescribing policies and local"narcotic drug card"systems,while barriers comprised incomplete lists of controlled substances.At the technological support level,facilitating factors included the establishment of regional health information platforms,while barriers included the lack of standardized prescription guidelines and diagnostic decision-support tools.At the individual level,facilitating factors included the public's cautious attitude toward drug misuse,while barriers included strained doctor-patient relationships.Conclusion China still faces significant challenges in addressing NMUPO and urgently needs to improve the existing regulatory system.It is recommended that reforms be carried out in areas such as pharmaceutical control mechanisms,drug treatment and rehabilitation services,preventive health education activities,and the optimized use of health information systems.

Objective To develop and validate a prediction model to identify high-risk individuals who are at-risk to develop acute liver injury(ALI)within 180 days in new statin users,and to support early clinical intervention.Methods Data were sourced from the Yinzhou Regional Health Information Platform,covering statin initiators aged 18 years and older from January 1,2010,to October 31,2021.The dataset was divided into a derivation cohort and a temporal validation cohort based on the time of statin initiation.Predictors were selected using LASSO regression,and the model was constructed using the extreme gradient boosting(XGBoost)algorithm combined with cost-sensitive learning.Model performance was evaluated using Brier scores,Harrell's C-index,and calibration curves.Results A total of 126,440 statin initiators were included,with 90,542 in the derivation cohort and 35,898 in the validation cohort.Within 180 days of initial statin use,412(0.33％)patients developed ALI,including 305(0.34％)in the derivation cohort and 107(0.30％)in the validation cohort.The final model incorporated 16 predictors,which included demographic characteristics,lifestyle factors,family history,medical history,statin use,and concomitant medication use.The model demonstrated excellent overall performance[Brier score=0.0043,95％CI(0.0038,0.0049)],discrimination[Harrell's C-index=0.761,95％CI(0.725,0.794)],and calibration in internal validation.In temporal validation,the model also performed well[Brier score=0.0044,95％CI(0.0036,0.0052),Harrell's C-index=0.703,95％CI(0.614,0.781)].Conclusion This study develope and validate a prediction model for ALI in statin users,providing clinicians with a reliable tool for individualized risk assessment.This model can help achieve risk stratification and reduce the occurrence of ALI.

Rare events are widely prevalent in various disciplines,including rare adverse reactions to vaccines and drugs,clinical rare diseases,and low-probability clinical outcomes.The reason for research interest on such events is that their occurrence often brings incalculable and serious consequences.In the context of big data,numerous methods have emerged for rare event data analysis,including sampling based,category weighting,ensemble learning,and deep learning.This article systematically summarizes the research progress of current rare event data analysis methods,and introduces their basic principles and applicable scenarios.By analyzing the advantages and disadvantages of existing methods,the challenges of rare event research are sorted out and summarized,and potential research directions in related fields are explored to provide references for researchers.

Objective To develop and validate a prediction model to identify high-risk individuals who are at-risk to develop acute liver injury(ALI)within 180 days in new statin users,and to support early clinical intervention.Methods Data were sourced from the Yinzhou Regional Health Information Platform,covering statin initiators aged 18 years and older from January 1,2010,to October 31,2021.The dataset was divided into a derivation cohort and a temporal validation cohort based on the time of statin initiation.Predictors were selected using LASSO regression,and the model was constructed using the extreme gradient boosting(XGBoost)algorithm combined with cost-sensitive learning.Model performance was evaluated using Brier scores,Harrell's C-index,and calibration curves.Results A total of 126,440 statin initiators were included,with 90,542 in the derivation cohort and 35,898 in the validation cohort.Within 180 days of initial statin use,412(0.33％)patients developed ALI,including 305(0.34％)in the derivation cohort and 107(0.30％)in the validation cohort.The final model incorporated 16 predictors,which included demographic characteristics,lifestyle factors,family history,medical history,statin use,and concomitant medication use.The model demonstrated excellent overall performance[Brier score=0.0043,95％CI(0.0038,0.0049)],discrimination[Harrell's C-index=0.761,95％CI(0.725,0.794)],and calibration in internal validation.In temporal validation,the model also performed well[Brier score=0.0044,95％CI(0.0036,0.0052),Harrell's C-index=0.703,95％CI(0.614,0.781)].Conclusion This study develope and validate a prediction model for ALI in statin users,providing clinicians with a reliable tool for individualized risk assessment.This model can help achieve risk stratification and reduce the occurrence of ALI.

Rare events are widely prevalent in various disciplines,including rare adverse reactions to vaccines and drugs,clinical rare diseases,and low-probability clinical outcomes.The reason for research interest on such events is that their occurrence often brings incalculable and serious consequences.In the context of big data,numerous methods have emerged for rare event data analysis,including sampling based,category weighting,ensemble learning,and deep learning.This article systematically summarizes the research progress of current rare event data analysis methods,and introduces their basic principles and applicable scenarios.By analyzing the advantages and disadvantages of existing methods,the challenges of rare event research are sorted out and summarized,and potential research directions in related fields are explored to provide references for researchers.

Objective:To analyze the incidence and risk factors of ocular surface disease among power grid construction workers in plateau.Methods:A total of 11 132 construction personnel from the Ngari prefecture-central Tibet power grid interconnection project were included from 2019 to 2020.Baseline characteristics including age, gender, body mass index, developmental and nutritional status, relevant clinical indicators, etc.and follow-up data regarding incidence of ocular surface diseases were obtained from the medical records of Ali interconnection project staff medical station.The altitude of workplace and residence of the study population were obtained from the website (https: //zh-cn.topographic-map.com/legal/).The mean age of the subjects was (36.17±10.48) years, of which 95.33%(10, 612 subjects) were male.The median follow-up time was 1.53 years.The altitude of the residence and workplace were (1 954.77±940.64) and (4 535.09±232.71) meters, respectively.The incidence of ocular surface diseases in groups with different characteristics was calculated.Differential variables for the incidence of ocular surface diseases were screened by univariate Cox proportional hazards regression model.Influencing factors of ocular surface diseases multivariate were explored by Cox proportional hazards model.This study was approved by the Ethics Committee of Peking University Health Science Center (No.IRB00001052-21066).Results:During the follow-up period, the incidence of ocular surface disease was 9.27% (1 032 cases), and the incidence of conjunctivitis and keratitis was 6.58% (733 cases) and 1.80% (200 cases), respectively.Multivariate Cox proportional hazards regression analysis showed that for every 1 000 meters increase in altitude of residence, the risk of ocular surface disease decreased by 15% ( HR[95% CI]: 0.85[0.80~0.91], P<0.001).For every 100 meters increase in altitude of workplace, the risk of ocular surface disease increased by 5% ( HR[95% CI]: 1.04[1.01~1.07], P=0.006).Decreased blood oxygen saturation ( HR[95% CI]: 1.09[1.02~1.16], P=0.007), hearing pulmonary dry rales (hazard ratio ( HR)[95% CI]: 1.53[1.12~2.09], P=0.007) and heart murmurs ( HR[95% CI]: 4.44[1.43~13.83], P=0.010) were associated with ocular surface disease. Conclusions:The incidence of ocular surface disease in personnel engaged in electric grid construction at high altitudes should not be ignored.High working altitude, low residence altitude, pulmonary dry rales, heart murmurs and low blood oxygen saturation are factors associated with the incidence of ocular surface disease.

Objective The aim is to understand the common scenarios of non-medical use of prescription opioids(NMUPO)and analyze the potential facilitating and hindering factors in the regulatory process of NMUPO from the perspective of healthcare professionals.Methods Healthcare professionals in local hospitals were surveyed through a two-stage purposive sampling from June to August 2022 in Ningbo,China.The survey was conducted using a semi-structured questionnaire on topics,and thematic analysis were used to identify and summarise key themes and patterns.Results A total of 75 participants were included,the average age was(43.9±7.2)years,and 54(72.0%)were male.The most common NMUPO scenarios involved middle-aged males pretending acute severe pain to obtain injectable opioids.The facilitating and hindering factors related to the regulation of NMUPO can be categorized into three types:institutional governance,technical support,and individual behaviors.At the institutional level,facilitating factors included strict national prescribing policies and local"narcotic drug card"systems,while barriers comprised incomplete lists of controlled substances.At the technological support level,facilitating factors included the establishment of regional health information platforms,while barriers included the lack of standardized prescription guidelines and diagnostic decision-support tools.At the individual level,facilitating factors included the public's cautious attitude toward drug misuse,while barriers included strained doctor-patient relationships.Conclusion China still faces significant challenges in addressing NMUPO and urgently needs to improve the existing regulatory system.It is recommended that reforms be carried out in areas such as pharmaceutical control mechanisms,drug treatment and rehabilitation services,preventive health education activities,and the optimized use of health information systems.

Rare diseases are a collective term for diseases with extremely low prevalence and incidence rates.Up to now,China has released two lists identifying a total of 207 rare diseases.Given that most rare diseases do not have drugs with corresponding indications,physicians frequently resort to using off-label drugs when treating patients with rare diseases.However,there is currently no systematic guideline or expert consensus for the use of off-label medications in China.To comprehensively collect existing evidence of off-label drug use for rare diseases,fully analyze and evaluate the rationality of off-label drug use for rare diseases,and standardize the management of off-label drug use for rare diseases,the Rare Disease Branch of Beijing Medical Association,Chinese Pharmaceutical Association,Beijing Pharmaceutical Association,and the School of Public Health,Peking University have jointly initiated the drafting of the Expert Consensus on Off-label Use of Drugs for Rare Diseases.This consensus refer to the WHO Handbook for Guideline Development,the Guidelines for Developing/Revising Clinical Diagnostic and Treatment Guidelines in China(2022 Edition),the AGREE Ⅱ and the STAR tools.This protocol outlines the background and purpose of consensus,as well as the comprehensive framework for consensus development,encompassing panel formation,clinical issue identification,evidence retrieval,data extraction,and evidence-based recommendation formulation.

The emergence of artificial intelligence(AI)has had a significant impact on medical research and practice,both in terms of the number of studies and research paradigms,and has become an important tool for the development of pharmacoepidemiology.However,traditional AI has faced many challenges,while facilitating pharmacoepidemiology research,such as complex data processing,difficulty in identifying drug exposures and potential outcomes,and time-consuming and laborious study design and implementation.The rapid development of generative AI,represented by large language models(LLMs),has demonstrated a unique potential to enhance research efficiency,shift research paradigms,and facilitate knowledge discovery.LLMs are equipped with natural language understanding and generation capabilities.Through deep mining of multi-dimensional data resources,LLMs can quickly and accurately extract,analyze,summarize,and present the required information,which can not only help drug discovery,drug repurposing,pharmacovigilance and other pharmacoepidemiological tasks,but also provide powerful support for the whole process of research protocol design,data analysis,result interpretation and paper publication.Driven by LLMs,pharmacoepidemiology research is gradually moving into a new stage based on big data and automated analysis.Of course,LLMs also have problems of data bias,"illusion"of results,and ethical and legal regulation.By strengthening interdisciplinary cooperation,establishing a standardized evaluation system,improving ethical and regulatory guidance,enhancing data quality,strengthening practitioner training and capacity building,and promoting human-machine collaborative research modes,it is expected that the potential of LLMs in pharmacoepidemiology will be fully released,and it will provide a more scientific,rapid,and efficient technological support for drug regulation and public health decision-making.