ADscreen: A speech processing-based screening system for automatic identification of patients with Alzheimer's disease and related dementia.

Alzheimer's disease and related dementias (ADRD) present a looming public health crisis, affecting roughly 5 million people and 11 % of older adults in the United States. Despite nationwide efforts for timely diagnosis of patients with ADRD, >50 % of them are not diagnosed and unaware of their disease. To address this challenge, we developed ADscreen, an innovative speech-processing based ADRD screening algorithm for the protective identification of patients with ADRD. ADscreen consists of five major components: (i) noise reduction for reducing background noises from the audio-recorded patient speech, (ii) modeling the patient's ability in phonetic motor planning using acoustic parameters of the patient's voice, (iii) modeling the patient's ability in semantic and syntactic levels of language organization using linguistic parameters of the patient speech, (iv) extracting vocal and semantic psycholinguistic cues from the patient speech, and (v) building and evaluating the screening algorithm. To identify important speech parameters (features) associated with ADRD, we used the Joint Mutual Information Maximization (JMIM), an effective feature selection method for high dimensional, small sample size datasets. Modeling the relationship between speech parameters and the outcome variable (presence/absence of ADRD) was conducted using three different machine learning (ML) architectures with the capability of joining informative acoustic and linguistic with contextual word embedding vectors obtained from the DistilBERT (Bidirectional Encoder Representations from Transformers). We evaluated the performance of the ADscreen on an audio-recorded patients' speech (verbal description) for the Cookie-Theft picture description task, which is publicly available in the dementia databank. The joint fusion of acoustic and linguistic parameters with contextual word embedding vectors of DistilBERT achieved F1-score = 84.64 (standard deviation [std] = ±3.58) and AUC-ROC = 92.53 (std = ±3.34) for training dataset, and F1-score = 89.55 and AUC-ROC = 93.89 for the test dataset. In summary, ADscreen has a strong potential to be integrated with clinical workflow to address the need for an ADRD screening tool so that patients with cognitive impairment can receive appropriate and timely care.

Technological Innovations in Disease Management: Text Mining US Patent Data From 1995 to 2017.

BACKGROUND: Patents are important intellectual property protecting technological innovations that inspire efficient research and development in biomedicine. The number of awarded patents serves as an important indicator of economic growth and technological innovation. Researchers have mined patents to characterize the focuses and trends of technological innovations in many fields. OBJECTIVE: To expand patent mining to biomedicine and facilitate future resource allocation in biomedical research for the United States, we analyzed US patent documents to determine the focuses and trends of protected technological innovations across the entire disease landscape. METHODS: We analyzed more than 5 million US patent documents between 1995 and 2017, using summary statistics and dynamic topic modeling. More specifically, we investigated the disease coverage and latent topics in patent documents over time. We also incorporated the patent data into the calculation of our recently developed Research Opportunity Index (ROI) and Public Health Index (PHI), to recalibrate the resource allocation in biomedical research. RESULTS: Our analysis showed that protected technological innovations have been primarily focused on socioeconomically critical diseases such as "other cancers" (malignant neoplasm of head, face, neck, abdomen, pelvis, or limb; disseminated malignant neoplasm; Merkel cell carcinoma; and malignant neoplasm, malignant carcinoid tumors, neuroendocrine tumor, and carcinoma in situ of an unspecified site), diabetes mellitus, and obesity. The United States has significantly improved resource allocation to biomedical research and development over the past 17 years, as illustrated by the decreasing PHI. Diseases with positive ROI, such as ankle and foot fracture, indicate potential research opportunities for the future. Development of novel chemical or biological drugs and electrical devices for diagnosis and disease management is the dominating topic in patented inventions. CONCLUSIONS: This multifaceted analysis of patent documents provides a deep understanding of the focuses and trends of technological innovations in disease management in patents. Our findings offer insights into future research and innovation opportunities and provide actionable information to facilitate policy makers, payers, and investors to make better evidence-based decisions regarding resource allocation in biomedicine.

Mining news media for understanding public health concerns.

INTRODUCTION: News media play an important role in raising public awareness, framing public opinions, affecting policy formulation, and acknowledgment of public health issues. Traditional qualitative content analysis for news sentiments and focuses are time-consuming and may not efficiently convey sentiments nor the focuses of news media. METHODS: We used descriptive statistics and state-of-art text mining to conduct sentiment analysis and topic modeling, to efficiently analyze over 3 million Reuters news articles during 2007-2017 for identifying their coverage, sentiments, and focuses for public health issues. Based on the top keywords from public health scientific journals, we identified 10 major public health issues (i.e., "air pollution," "alcohol drinking," "asthma," "depression," "diet," "exercise," "obesity," "pregnancy," "sexual behavior," and "smoking"). RESULTS: The news coverage for seven public health issues, "Smoking," "Exercise," "Alcohol drinking," "Diet," "Obesity," "Depression," and "Asthma" decreased over time. The news coverage for "Sexual behavior," "Pregnancy," and "Air pollution" fluctuated during 2007-2017. The sentiments of the news articles for three of the public health issues, "exercise," "alcohol drinking," and "diet" were predominately positive and associated such as "energy." Sentiments for the remaining seven public health issues were mainly negative, linked to negative terms, e.g., diseases. The results of topic modeling reflected the media's focus on public health issues. CONCLUSIONS: Text mining methods may address the limitations of traditional qualitative approaches. Using big data to understand public health needs is a novel approach that could help clinical and translational science awards programs focus on community-engaged research efforts to address community priorities.

Public Opinions Toward Diseases: Infodemiological Study on News Media Data.

BACKGROUND: Society always has limited resources to expend on health care, or anything else. What are the unmet medical needs? How do we allocate limited resources to maximize the health and welfare of the people? These challenging questions might be re-examined systematically within an infodemiological frame on a much larger scale, leveraging the latest advancement in information technology and data science. OBJECTIVE: We expanded our previous work by investigating news media data to reveal the coverage of different diseases and medical conditions, together with their sentiments and topics in news articles over three decades. We were motivated to do so since news media plays a significant role in politics and affects the public policy making. METHODS: We analyzed over 3.5 million archive news articles from Reuters media during the periods of 1996/1997, 2008 and 2016, using summary statistics, sentiment analysis, and topic modeling. Summary statistics illustrated the coverage of various diseases and medical conditions during the last 3 decades. Sentiment analysis and topic modeling helped us automatically detect the sentiments of news articles (ie, positive versus negative) and topics (ie, a series of keywords) associated with each disease over time. RESULTS: The percentages of news articles mentioning diseases and medical conditions were 0.44%, 0.57% and 0.81% in the three time periods, suggesting that news media or the public has gradually increased its interests in medicine since 1996. Certain diseases such as other malignant neoplasm (34%), other infectious diseases (20%), and influenza (11%) represented the most covered diseases. Two hundred and twenty-six diseases and medical conditions (97.8%) were found to have neutral or negative sentiments in the news articles. Using topic modeling, we identified meaningful topics on these diseases and medical conditions. For instance, the smoking theme appeared in the news articles on other malignant neoplasm only during 1996/1997. The topic phrases HIV and Zika virus were linked to other infectious diseases during 1996/1997 and 2016, respectively. CONCLUSIONS: The multi-dimensional analysis of news media data allows the discovery of focus, sentiments and topics of news media in terms of diseases and medical conditions. These infodemiological discoveries could shed light on unmet medical needs and research priorities for future and provide guidance for the decision making in public policy.