Metaprofile’s AI-powered semantic metadata delivers ‚genetic‘ content discovery

DCJ also contributed to the design and conduction of the cohort study, and contributed suggested edits to the manuscript. FC and GAC designed and performed the automated text analysis; DFS and MS contributed to the analysis of the data; NM, SR, and MC collected and preprocessed data on patients with schizophrenia and their controls. All the authors reviewed the results, edited the manuscript, and gave final approval for submission of the manuscript. Drs Corcoran and Cecchi had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Of note, the sample size employed in this initial study was small, with five participants developing psychosis during the follow-up period.

The seven best autoPET teams report in the journal Nature Machine Intelligence on how algorithms can detect tumor lesions in positron emission tomography (PET) and computed tomography (CT). To investigate whether standard clinical ratings could differentiate CHR+ and CHR− individuals, we entered variables from clinical ratings—the SIPS/SOPS13—into several classifiers. The best prediction obtained was less accurate than the automated analysis, misclassifying 3 of 5 CHR+ patients and 4 of 29 CHR− patients to yield an accuracy of 79%, consistent with prior studies (see Table 2 for classification performance metrics). Of the 34 participants, 5 were known to develop schizophrenia (or schizoaffective disorder) within 2.5 years.

Speech analyses

Innovative technology empowering businesses in today’s world can transform this potential into reality, turning your data into a strategic asset that drives business success. GB contributed to the conception of the study, the interpretation of data, and drafting the manuscript. CMC led the prospective clinical high-risk cohort study and oversaw all data collection, and worked on and edited iterative drafts of the manuscript.

AI Agents: why your enterprise needs a semantic layer for true intelligence

It aims to simplify the process of analyzing customer feedback and provide valuable insights that can be used to enhance products, services, and overall customer satisfaction. „Evidence on how AI algorithms will change patient outcomes needs to come from comparisons with alternative diagnostic tests in randomised controlled trials,“ adds Dr Livia Faes from Moorfields Eye Hospital, London. „So far, there are hardly any such trials where diagnostic decisions made by an AI algorithm are acted upon to see what then happens to outcomes which really matter to patients, like timely treatment, time to discharge from hospital, or even survival rates.“ Your fitness watch knows that sleep quality affects recovery time, which influences exercise recommendations. Similarly, a modern ontology maps the relationships between different aspects of your business data.

Because speech in emergent psychosis often shows marked reductions in verbosity (referred to clinically as poverty of speech), we also included the maximum number of words per phrase in the classification. As part of Microsoft Azure Cognitive Service for Language and Text, Microsoft Azure Text Analytics is a cloud-based service that uses natural language processing to identify sentiment, key phrases, language, and other insights from unstructured text. It analyzes text-based documents in both English and other languages, and it can be used to understand customer sentiment, analyze key topics, and detect language within text datasets. ​​Keatext is a text analytics and customer feedback analysis software platform based on natural language processing.

Participants were prospectively characterized for symptoms every 3 months for up to 2.5 years, with transition to psychosis determined using the SIPS/SOPS ‘presence of psychosis’ criteria. Another aspect recognised by the WHO is the use of AI for drug action pathway identification, comparative studies across systems like Ayurveda, TCM, and Unani, and the development of artificial chemical sensors to assess traditional parameters such as Rasa, Guna, and Virya, Ayush said. Union Minister of State (Independent Charge) for Ayush and Minister of State for Health and Family Welfare Prataprao Jadhav said India’s AI initiatives, mentioned in WHO’s brief, reflect the commitment of Indian scientists to advancing traditional medicine through cutting-edge technology.

„Within those handful of high-quality studies, we found that deep learning could indeed detect diseases ranging from cancers to eye diseases as accurately as health professionals. But it’s important to note that AI did not substantially out-perform human diagnosis.“ It connects analytics platforms with data sources by organizing facts (data values), dimensions (attributes), and hierarchies (taxonomies). This creates a consistent, business-friendly view of the data, so anyone in your organization can access and analyze it without needing technical expertise. With these two features, we were able to characterize semantic coherence by measuring components of the distributions of first- and second-order coherence over the speech samples, including features such as the minimum, mean, median, and s.d.

Self-report of symptoms, on which much of psychiatric assessment relies, depends on the patient’s motivation and capacity to accurately report their introspective experiences, which may be influenced by psychiatric illness. Although clinicians routinely detect disorganized speech on the basis of clinical observations, our data suggest that automated analytic methods allow for superior assessment. As a direct, objective measure, automated speech analysis could thus provide important information to complement existing methods for patient assessment. Finally, these findings support the use of advanced computational methods to characterize complex human behaviors such as speech in both normal and pathological states.

• With the help of this software, users can sift through growing volumes of text data to identify main ideas or topics, extract key terms, analyze sentiment, and identify correlations between words.
• The platform integrates with existing cloud, on-premises, and hybrid environments to support customized NLP workflows.
• About SPECTRA Semantic StudioSPECTRA Semantic Studio is a cutting-edge platform that allows law enforcement agencies to train and deploy customized AI models tailored to their unique operational language and contextual needs.
• The future of law enforcement technology isn’t just about speed — it’s about strategic accuracy with human expertise.
• Conversational AI does involves aspects of the technology like speech recognition and natural language understanding, which Microsoft includes in cognitive services on its Azure cloud computing platform.

Recent developments in computer science, including natural language processing, could provide the foundation for future development of objective clinical tests for psychiatry. As artificial intelligence becomes more integrated into law enforcement, concerns are growing over the reliability of generic AI tools in complex investigative environments. The approach focuses on contextual accuracy, allowing law enforcement units to build AI that reflects their local operational realities rather than relying on universal, pre-trained models. Text analysis tools are software applications that extract meaningful information and insights from textual data.

Schizophrenia, although relatively rare (lifetime prevalence ~1%), is among the most catastrophic mental illnesses both personally and societally. Improving the capacity to predict psychosis among high-risk populations would have important ramifications for early identification and preventive intervention, potentially critically altering the long-term life trajectory of people with emergent psychotic disorders. Discrimination between individuals who transitioned to psychosis (clinical high risk+ (CHR+); in red) and those who did not (CHR−; in blue) presented as the convex hull of CHR− individuals. Discrimination was based on three features extracted from free speech using automated methods.

• Discrimination was based on three features extracted from free speech using automated methods.
• The semantic coherence feature that best discriminated those who transitioned to psychosis from those who did not was the minimum semantic coherence value (i.e., the coherence at the point of maximal discontinuity) within each transcribed text.
• Although clinicians routinely detect disorganized speech on the basis of clinical observations, our data suggest that automated analytic methods allow for superior assessment.
• Much like a living organism that thrives through interaction with its environment, adaptable knowledge architecture evolves alongside your business with a human-in-the-loop approach.
• Such a fine-grained behavioral analysis could allow tighter mapping between psychiatrically relevant phenotypes and their underlying biology, in essence carving nature more closely at its joints.
• Decrease in the flow of meaning from one spoken phrase to the next, and grammatical markers of speech complexity, identified the five individuals who later developed psychosis.

Using automated analysis, transcripts of interviews were evaluated for semantic and syntactic features predicting later psychosis onset. Speech features were fed into a convex hull classification algorithm with leave-one-subject-out cross-validation to assess their predictive value for psychosis outcome. The canonical correlation between the speech features and prodromal symptom ratings was computed. These findings represent the initial stages in the use of emerging computer science behavioral analysis techniques, already prominently used in industry, to characterize and predict human behavior in the context of psychiatric health and illness. Using automated approaches, we were able to extract indices of speech-semantic coherence and syntax and use these to accurately predict the subsequent development of psychosis in high-risk youths. Prognostic prediction using this approach outperformed prediction on the basis of standard psychiatric ratings.

While the watch itself collects raw sensor data, like heart rate, steps, and sleep cycles, the semantic layer organizes this data and defines relationships, calculations, and hierarchies that make it easier for you to understand. It doesn’t generate insights automatically but instead provides a structured framework that allows you to explore and interpret your data meaningfully. Because the concept of semantic coherence we employed does not have a mathematical definition, in this validation we tested the coherence measure against a corpus of classic literature and assessed how the measure changed when we modified the original texts in a way that is relevant to the concept of semantic coherence. „Together, these AI-enabled platforms are not only preserving and validating India’s traditional knowledge systems of medicine but are also advancing their global integration within evidence-based, digital healthcare frameworks,“ Kotecha said. These leading text analysis tools extract meaningful information and insights from textual data.