1. A hybrid deep learning-based suicide ideation detection system (100) for automated analysis and classification of suicide-related risk in digital and clinical textual data, the system (100) comprising: a user device (102) configured to input at least on of textual, linguistic, and behavioural information from at least one digital, clinical, and social media source; a communication network (104) configured to transmit the acquired information to at least one of remote and local device; a processing unit (106) configured to receive, analyse, and classify the information transmitted through the communication network (104) to determine the likelihood of suicide ideation in real time; wherein the processing unit (106) comprises: a data input module (108) configured to acquire and structure input data from multiple sources including social media content, online communications, and patient records; a pre-processing module (110) configured to clean, normalize, anonymised, and organize textual data for analytical consistency; a feature extraction module (112) configured to transform pre-processed data into contextual and emotional representations using deep neural embedding models; a risk assessment module (114) configured to evaluate suicide ideation probability using a hybrid deep learning framework incorporating contextual, sequential, and attention-based learning layers; an integrity verification module (116) configured to perform identity-based remote data integrity checking to ensure authenticity and privacy of sensitive data; a performance evaluation module (118) configured to assess the accuracy, reliability, and interpretability of the detection process using explainable artificial intelligence techniques; and an alert generation module (120) configured to generate notifications, visual signals, and digital alerts when the analysed content meets and exceeds a defined suicide risk threshold.

2. The system (100) as claimed in claim 1, wherein the user device (102) comprises at least computing terminal, mobile interface, and clinical monitoring device configured to collect at least one of textual data, chat messages, behavioural patterns, and user-generated content.

3. The system (100) as claimed in claim 1, wherein the data input module (108) is configured to aggregate, synchronize, and route incoming data streams from heterogeneous sources including social media platforms, electronic health record systems, chat based interfaces, online forums, and patient self-reporting tools, and to perform metadata extraction comprising timestamps, source identifiers, and contextual attributes to enable structured downstream processing.

4. The system (100) as claimed in claim 1, wherein the pre-processing module (110) is configured to perform tokenization, lemmatization, stop word removal, noise filtering, and text standardization, and to support multilingual text inputs.

5. The system (100) as claimed in claim 1, wherein the feature extraction module (112) employs a hybrid contextual encoder comprising transformer based embedding’s and bidirectional recurrent layers configured to capture semantic, emotional, and psychological dependencies.

6. The system (100) as claimed in claim 1, wherein the risk assessment module (114) integrates multi-head attention mechanisms and adaptive thresholding for context sensitive evaluation of suicide ideation probability.

7. The system (100) as claimed in claim 1, wherein the integrity verification module (116) comprises a private key generator and tag verification unit configured to authenticate user identity and verify data integrity through pairing-based cryptographic operations.

8. The system (100) as claimed in claim 1, wherein the performance evaluation module (118) employs explainable artificial intelligence visualization techniques including shapley additive explanations, attention mapping, and interpretive heat maps to enhance transparency and assist clinical decision support.

9. The system (100) as claimed in claim 1, wherein the alert generation module (120) transmits digital notifications, intervention prompts, and alerts containing suicide risk scores and context summaries to authorized monitoring systems and mental health professionals.

10. A method (200) for detecting suicide a hybrid deep learning-based suicide ideation detection system (100), the method (200) comprising: acquiring (202) textual, linguistic, and behavioural data from at least one of digital, clinical, and social media sources through the user device (102) connected via a communication network (104); receiving and structuring (204) the acquired data within the processing unit (106) for subsequent analysis; pre-processing (206) the received data using the pre-processing module (110) to remove noise, standardize text, perform anonymization, and normalize linguistic structures for consistent representation; extracting (208) contextual, semantic, and emotional features from the pre-processed data using the feature extraction module (112) configured with transformer based and sequential neural encoders; assessing (210) suicide ideation probability through the risk assessment module (114) by applying hybrid deep learning layers incorporating attention-based contextual reasoning and temporal pattern analysis; verifying (212) the authenticity and integrity of input data using the identity-based remote data integrity checking module (116) employing cryptographic verification for secure and privacy preserving computation; evaluating (214) detection performance metrics including accuracy, precision, recall, and interpretability using the performance evaluation module (118) integrated with explainable artificial intelligence visualizations; and generating (216) an automated, visual, and digital alert through the alert generation module (120) when the analysed content exceeds a predefined suicide risk threshold, thereby enabling real-time intervention and monitoring.

Description:FIELD OF DISCLOSURE
[0001] The present disclosure generally relates to the field of artificial intelligence and natural language processing system, more specifically, relates to the model for detecting suicide ideation using deep learning.
BACKGROUND OF THE DISCLOSURE
[0002] Suicide ideation, which refers to the presence of thoughts or expressions associated with self-harm or suicide, represents a critical public health concern worldwide. Early identification of such ideation is vital for enabling timely intervention and reducing the risk of suicide attempts or fatalities. However, the recognition of suicidal tendencies within textual or digital content remains challenging due to the complex, context-dependent, and often implicit nature of language used in such expressions. On social media and online communication platforms, individuals may employ metaphorical, coded, or ambiguous phrasing that conceals intent, making manual detection unreliable and inconsistent.
[0003] The widespread use of digital platforms, including social media networks, online forums, and messaging services, provides new opportunities to identify early signs of suicidal ideation through textual or multimodal cues. Individuals increasingly express distress, hopelessness, or withdrawal indirectly through online communication. However, detecting such ideation in user-generated content presents numerous challenges. Suicide-related expressions are often subtle, ambiguous, or metaphorical in nature, making their detection through conventional keyword-based methods highly unreliable. Additionally, the informal language, abbreviations, emojis, and cultural nuances typical of social media posts add layers of linguistic complexity that conventional algorithms fail to interpret accurately.
[0004] Conventional suicide screening approaches rely primarily on structured questionnaires, keyword-based searches, or manually curated datasets. These methods often fail to capture subtle linguistic cues, emotional tone, and contextual variations inherent in naturally occurring text. Additionally, such approaches struggle to process the massive volume of data generated daily across diverse digital ecosystems, limiting their scalability and effectiveness in real-time mental health monitoring.
[0005] Existing deep learning and machine learning methods have demonstrated potential in detecting suicidal ideation, yet they face several limitations. Many models depend on surface level textual features or fixed word embedding’s that inadequately capture emotional nuance, sarcasm, or cross-linguistic differences. Moreover, the reliance on small, platform specific datasets results in limited generalization across populations, languages, and domains. The absence of multimodal analysis incorporating behavioural patterns, images, or communication metadata further restricts the comprehensiveness of existing models.
[0006] Another major challenge lies in the interpretability of deep learning systems. Most existing models function as opaque “black boxes,” offering minimal insight into the reasoning behind their predictions. This lack of transparency makes it difficult for mental health professionals to trust automated outputs or use them as actionable diagnostic aids. Furthermore, ethical concerns regarding data privacy, informed consent, and false positive detection necessitate models that are not only accurate but also secure, auditable, and compliant with data protection regulations.
[0007] The present invention solves the problems of the prior art by introducing a coffee fruit harvesting device that provides an efficient, low damage, and selective means of collecting ripe coffee cherries while minimizing labour intensity and operational costs. The device integrates a mechanical gripping and vibration assisted mechanism configured to detach only mature coffee fruits from the branches without harming unripe cherries or plant stems. It comprises a lightweight extendable frame, adjustable harvesting arms, and a motorized actuator that applies controlled oscillations to the fruiting branches. A sensor based maturity detection unit assists in distinguishing ripe fruits based on colour and firmness, thereby improving selectivity and yield quality. The harvested fruits are directed into a collection chamber through a flexible conduit system, ensuring minimal spillage and contamination. This invention therefore addresses inefficiencies of manual picking and conventional mechanical shakers, offering a precise, energy efficient, and sustainable harvesting solution for small and large coffee plantations alike.
[0008] Thus, in light of the above-stated discussion, there exists a need for a model for detecting suicide ideation using deep learning.
SUMMARY OF THE DISCLOSURE
[0009] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensues and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0010] According to illustrative embodiments, the present disclosure focuses on a hybrid deep learning-based suicide ideation detection system which overcomes the above-mentioned disadvantages and provide the users with a useful and commercial choice.
[0011] An objective of the present disclosure is to provide an advanced and reliable suicide ideation detection system capable of automatically identifying linguistic, semantic, and behavioural indicators of self harm and suicidal intent within digital communications, social media posts, clinical narratives, and user-generated textual content. The system is intended to enable early identification of at risk individuals by continuously monitoring and analysing incoming data with minimal manual intervention.
[0012] Another objective of the present disclosure is to offer a deep learning-based analytical framework that overcomes the limitations of traditional keyword driven and rule-based methods by at least one of capturing indirect, ambiguous, coded, and metaphorical expressions of suicidal thoughts. This is achieved through the use of contextual language models, emotion rich embedding’s, and sequential learning architectures that collectively improve the system’s interpretive capability.
[0013] Another objective of the present disclosure is to incorporate a hybrid neural architecture combining transformer based encoders, bidirectional sequential models, and attention mechanisms, enabling the model to understand deeper semantic relationships, long range dependencies, and subtle emotional cues present within complex textual data. This hybridisation enhances precision and reduces false positives and negatives when analysing nuanced mental health related content.
[0014] Another objective of the present disclosure is to integrate an identity based remote data integrity verification mechanism configured to ensure that the sensitive user data, clinical records, and behavioural logs used during model training and inference remain authentic, unaltered, and cryptographically verifiable. This identity centric integrity scheme strengthens trustworthiness, prevents tampering, and ensures compliance with security and privacy requirements for mental health applications.
[0015] Another objective of the present disclosure is to provide an explainable artificial intelligence module capable of generating interpretable insights, visual attributions, attention heatmaps, and relevance scores that illustrate how the model evaluated specific words, phrases, and contextual structures when determining suicide ideation risk. This enhances transparency and allows clinicians, psychologists, researchers, and authorised systems to validate, audit, and trust the output of the detection model.
[0016] Another objective of the present disclosure is to develop a system capable of adapting to evolving linguistic trends, emerging slang, cultural variations, and platform specific communication styles through periodic retraining, self-updating modules, and dynamic feature recalibration, ensuring that the system remains effective across diverse populations and changing modes of expression.
[0017] Another objective of the present disclosure is to generate automated, real-time alerts, notifications, and risk assessment signals when the analysed content exceeds a predefined threshold of suicide ideation probability. Such alerts may be transmitted to clinicians, counsellors, emergency-response systems, and authorised intervention teams to facilitate timely support and potentially life-saving intervention.
[0018] Yet another objective of the present disclosure is to present a modular, scalable, and privacy preserving technical architecture that supports multimodal data ingestion and can be deployed across cloud environments, distributed computing infrastructures, and edge based systems. This architecture ensures real-time performance, secure data handling, and seamless integration with existing mental health monitoring platforms and digital health frameworks.
[0019] In light of the above, in one aspect of the present disclosure, hybrid deep learning-based suicide ideation detection system is disclosed herein. The system comprises a user device configured to input at least on of textual, linguistic, and behavioural information from at least one digital, clinical, and social media source. The system also comprises a communication network configured to transmit the acquired information to at least one of remote and local device. The system comprises a processing unit configured to receive, analyse, and classify the information transmitted through the communication network to determine the likelihood of suicide ideation in real time, wherein the processing unit further comprises a data input module configured to acquire and structure input data from multiple sources including social media content, online communications, and patient records. The system also comprises a pre-processing module configured to clean, normalize, anonymised, and organize textual data for analytical consistency. The system also comprises a feature extraction module configured to transform pre-processed data into contextual and emotional representations using deep neural embedding models. The system also comprises a risk assessment module configured to evaluate suicide ideation probability using a hybrid deep learning framework incorporating contextual, sequential, and attention-based learning layers. The system also includes an integrity verification module configured to perform identity-based remote data integrity checking to ensure authenticity and privacy of sensitive data. The system also includes a performance evaluation module configured to assess the accuracy, reliability, and interpretability of the detection process using explainable artificial intelligence techniques. The system further includes an alert generation module configured to generate notifications, visual signals, and digital alerts when the analysed content meets and exceeds a defined suicide risk threshold.
[0020] In one embodiment, the user device comprises at least computing terminal, mobile interface, and clinical monitoring device configured to collect at least one of textual data, chat messages, behavioural patterns, and user-generated content.
[0021] In one embodiment, the data input module 108 is configured to aggregate, synchronize, and route incoming data streams from heterogeneous sources including social media platforms, electronic health record systems, chat based interfaces, online forums, and patient self-reporting tools, and to perform metadata extraction comprising timestamps, source identifiers, and contextual attributes to enable structured downstream processing.
[0022] In one embodiment, the pre-processing module is configured to perform tokenization, lemmatization, stop word removal, noise filtering, and text standardization, and to support multilingual text inputs.
[0023] In one embodiment, the feature extraction module employs a hybrid contextual encoder comprising transformer based embedding’s and bidirectional recurrent layers configured to capture semantic, emotional, and psychological dependencies.
[0024] In one embodiment, the risk assessment module integrates multi-head attention mechanisms and adaptive thresholding for context sensitive evaluation of suicide ideation probability.
[0025] In one embodiment, the integrity verification module comprises a private key generator and tag verification unit configured to authenticate user identity and verify data integrity through pairing-based cryptographic operations.
[0026] In one embodiment, the performance evaluation module employs explainable artificial intelligence visualization techniques including shapley additive explanations, attention mapping, and interpretive heat maps to enhance transparency and assist clinical decision support.
[0027] In one embodiment, the alert generation module transmits digital notifications, intervention prompts, and alerts containing suicide risk scores and context summaries to authorized monitoring systems and mental health professionals.
[0028] In light of above, in one aspect of the resent disclosure, a method for a for detecting suicide a hybrid deep learning-based suicide ideation detection system is disclosed herein, the method comprises acquiring textual, linguistic, and behavioural data from at least one of digital, clinical, and social media sources through the user device connected via a communication network. The method also comprises receiving and structuring the acquired data within the processing unit for subsequent analysis. The method also comprises pre-processing the received data using the pre-processing module to remove noise, standardize text, perform anonymization, and normalize linguistic structures for consistent representation. The method also comprises extracting contextual, semantic, and emotional features from the pre-processed data using the feature extraction module configured with transformer based and sequential neural encoders. The method also comprises assessing suicide ideation probability through the risk assessment module by applying hybrid deep learning layers incorporating attention-based contextual reasoning and temporal pattern analysis. The method further comprises verifying the authenticity and integrity of input data using the identity-based remote data integrity checking module employing cryptographic verification for secure and privacy preserving computation. The method also comprises evaluating detection performance metrics including accuracy, precision, recall, and interpretability using the performance evaluation module integrated with explainable artificial intelligence visualizations. The method further comprises generating an automated, visual, and digital alert through the alert generation module when the analysed content exceeds a predefined suicide risk threshold, thereby enabling real-time intervention and monitoring.
[0029] These and other advantages will be apparent from the present application of the embodiments described herein.
[0030] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
[0031] These elements, together with the other aspects of the present disclosure and various features are pointed out with particularity in the claims annexed hereto and form a part of the present disclosure. For a better understanding of the present disclosure, its operating advantages, and the specified object attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0032] To describe the technical solutions in the embodiments of the present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure.
[0033] The advantages and features of the present disclosure will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawing, in which:
[0034] FIG. 1 illustrates a block diagram of a hybrid deep learning-based suicide ideation detection system, in accordance with an embodiment of the present disclosure.
[0035] FIG. 2 illustrates a flow chart of a method, outlining the sequential steps for a model for detecting suicide ideation using deep learning, in accordance with an embodiment of the present disclosure.
[0036] Like reference, numerals refer to like parts throughout the description of several views of the drawing.
[0037] The a hybrid deep learning-based suicide ideation detection system is illustrated in the accompanying drawings, which like reference letters indicate corresponding parts in the various figures. It should be noted that the accompanying figure is intended to present illustrations of exemplary embodiments of the present disclosure. This figure is not intended to limit the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0038] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure.
[0039] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details.
[0040] Various terms as used herein are shown below. To the extent a term is used, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0041] The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
[0042] The terms “having”, “comprising”, “including”, and variations thereof signify the presence of a component.
[0043] Referring now to FIG. 1 to FIG. 2 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrates a perspective view for a hybrid deep learning-based suicide ideation detection system, in accordance with an embodiment of the present disclosure.
[0044] The system 100 may include a user device 102. The system 100 may include a communication network module 104. The system 100 may include a processing unit 106, which further comprises a data input module 108, a pre-processing module 110, a feature extraction module 112, a risk assessment module 114, an integrity verification module 116, a performance evaluation module 118, an alert generation module 120.
[0045] The user device 102 configured to input at least on of textual, linguistic, and behavioural information from at least one digital, clinical, and social media source. The user device 102 may comprise at least one of computer terminal, mobile interface, tablet, wearable sensor, and any network enabled device capable of generating, receiving, and forwarding user-generated data. The user device 102 transmits at least one of raw and partially processed data such as chat messages, patient health updates, social media posts, behavioural logs, and other communication streams to the processing pipeline. The device is further configured to support secure user authentication, privacy protection, and compliant data handling prior to transmitting sensitive mental health related information for analysis.
[0046] In one embodiment of the present invention, the user device 102 comprises at least computing terminal, mobile interface, and clinical monitoring device configured to collect at least one of textual data, chat messages, behavioural patterns, and user-generated content.
[0047] The communication network 104 configured to transmit the acquired information to at least one of remote and local device. The communication network 104 may include wired networks, wireless networks, cellular networks, cloud based communication layers, and any hybrid communication infrastructure capable of supporting encrypted, low latency, and reliable data transfer. The network ensures stable end-to-end connectivity for continuous ingestion of real-time data streams while maintaining adherence to data security standards such as transport layer security, secure socket layer, health insurance portability and accountability act-compliant protocols when handling sensitive mental health information.
[0048] The processing unit 106 configured to receive, analyse, and classify the information transmitted through the communication network 104 to determine the likelihood of suicide ideation in real time. The processing unit 106 may be implemented on at least one of cloud-based platform, distributed server network, local computing environment, and edge processing device capable of executing deep learning inference. The processing unit orchestrates data flow across internal modules, executes hybrid neural models, performs data integrity verification, computes interpretability metrics, and generates risk alerts. The processing unit provides scalable computational resources for high throughput analysis of multilingual, noisy, and diverse textual inputs.
[0049] The data input module 108 configured to acquire and structure input data from multiple sources such as social media platforms, clinical electronic health record systems, digital communication logs, online forums, and patient self-reporting tools. The module organizes incoming inputs into structured formats suitable for further processing, extracts metadata such as timestamps and user identifiers where permissible, and routes each data instance into the pre-processing module. The data input module ensures compatibility with multiple file formats, streaming protocols, and asynchronous data ingestion methods.
[0050] In one embodiment of the present invention, the data input module 108 is configured to aggregate, synchronize, and route incoming data streams from heterogeneous sources including social media platforms, electronic health record systems, chat based interfaces, online forums, and patient self-reporting tools, and to perform metadata extraction comprising timestamps, source identifiers, and contextual attributes to enable structured downstream processing.
[0051] The pre-processing module 110 configured to clean, normalize, anonymised, and organize textual data for analytical consistency. The processing unit 106 may be implemented on a cloud-based platform, distributed server network, local computing environment, and edge processing device capable of executing deep learning inference. The processing unit orchestrates data flow across internal modules, executes hybrid neural models, performs data integrity verification, computes interpretability metrics, and generates risk alerts. The processing unit provides scalable computational resources for high throughput analysis of multilingual, noisy, and diverse textual inputs.
[0052] In one embodiment of the present invention, the pre-processing module 110 configured to clean, normalize, anonymised, and organize textual data for analytical consistency.
[0053] The feature extraction module 112 configured to transform pre-processed data into contextual and emotional representations using deep neural embedding models. The module may incorporate transformer based encoders, contextual word embedding’s, sentiment extraction frameworks, and bidirectional recurrent layers to derive high dimensional feature vectors. These representations capture semantic meaning, emotional tone, lexical dependencies, and long range contextual cues that contribute to identifying suicidal intent within the text.
[0054] In one embodiment of the present invention, the feature extraction module 112 employs a hybrid contextual encoder comprising transformer based embedding’s and bidirectional recurrent layers configured to capture semantic, emotional, and psychological dependencies.
[0055] The risk assessment module 114 configured to evaluate suicide ideation probability using a hybrid deep learning framework incorporating contextual, sequential, and attention-based learning layers. The module applies multi-head attention mechanisms to identify highly influential words and phrases, analyses temporal and semantic dependencies through bidirectional recurrent units, and computes a probabilistic risk score that reflects the likelihood of suicidal intent. The module may further implement at least one of adaptive and domain-specific thresholding to ensure accurate classification under varying linguistic and behavioural contexts.
[0056] In one embodiment of the present invention, wherein the risk assessment module 114 integrates multi-head attention mechanisms and adaptive thresholding for context sensitive evaluation of suicide ideation probability.
[0057] The integrity verification module 116 configured to perform identitybased remote data integrity checking to ensure authenticity and privacy of sensitive data. The module employs pairing based cryptographic techniques, a private key generator, and a tag verification unit to authenticate user identities and verify that stored and transmitted data has not been altered. The module ensures that all training, inference, and audit datasets used by the system remain intact and uncompromised throughout the data lifecycle.
[0058] In one embodiment of the present invention, the integrity verification module 116 comprises a private key generator and tag verification unit configured to authenticate user identity and verify data integrity through pairing-based cryptographic operations.
[0059] The performance evaluation module 118 configured to assess the accuracy, reliability, and interpretability of the detection process using explainable artificial intelligence techniques. These techniques may include shapley additive explanations based relevance scoring, attention heatmaps, visualization of influential text regions, and interpretability metrics that demonstrate how the model arrived at its conclusions. The module further computes performance indicators such as precision, recall, F1 score, and confidence stability across diverse data samples.
[0060] In one embodiment of the present invention, the performance evaluation module 118 employs explainable artificial intelligence visualization techniques including shapley additive explanations, attention mapping, and interpretive heat maps to enhance transparency and assist clinical decision support.
[0061] The alert generation module 120 configured to generate notifications, visual signals, and digital alerts when the analysed content meets and exceeds a defined suicide risk threshold. Alerts may include risk scores, context summaries, and relevant textual highlights and can be transmitted to authorize monitoring systems, clinicians, mental-health professionals, and crisis-intervention teams through secure communication channels. The module supports real-time alerting and ensures timely intervention for individuals exhibiting high risk indicators.
[0062] In one embodiment of the present invention, the alert generation module 120 transmits digital notifications, intervention prompts, and alerts containing suicide risk scores and context summaries to authorized monitoring systems and mental health professionals.
[0063] FIG. 2 illustrates a flow chart of a method, outlining the sequential steps for a model for detecting suicide ideation using deep learning, in accordance with an embodiment of the present disclosure.
[0064] At step 202, the textual, linguistic, and behavioural data from at least one of digital, clinical, and social media sources through the user device 102 connected via a communication network 104.
[0065] At step 204, the acquired data within the processing unit 106 for subsequent analysis.
[0066] At step 206, the received data using the pre-processing module 110 to remove noise, standardize text, perform anonymization, and normalize linguistic structures for consistent representation.
[0067] The step 208, the contextual, semantic, and emotional features from the pre-processed data using the feature extraction module 112 configured with transformer based and sequential neural encoders.
[0068] The step 210, the suicide ideation probability through the risk assessment module 114 by applying hybrid deep learning layers incorporating attention-based contextual reasoning and temporal pattern analysis.
[0069] The step 212, the authenticity and integrity of input data using the identity-based remote data integrity checking module 116 employing cryptographic verification for secure and privacy preserving computation.
[0070] The step 214, the detection performance metrics including accuracy, precision, recall, and interpretability using the performance evaluation module 118 integrated with explainable artificial intelligence visualizations.
[0071] The step 216, the automated, visual, and digital alert through the alert generation module 120 when the analysed content exceeds a predefined suicide risk threshold, thereby enabling real-time intervention and monitoring.
[0072] The data input module 108 is configured to acquire, ingest, and structurally organize data originating from multiple heterogeneous sources including social media platforms, online communication channels, electronic health record systems, clinical reporting interfaces, chatbot logs, and patient self-assessment tools. The module establishes a standardized entry point for diverse forms of textual, linguistic, and behavioural information, ensuring that all incoming content is captured in a format compatible with subsequent analytical operations performed by the processing unit 106.
[0073] The data input module 108 is further configured to synchronize asynchronous data streams, enabling the system to manage high volume, irregular, and real-time inputs without fragmentation. The module may employ buffered ingestion pipelines, application programming interface based collectors, and batch data loaders to ensure complete and uninterrupted retrieval of incoming content. Additionally, the module is capable of extracting metadata attributes such as timestamps, source identifiers, device information, contextual tags, and content-origin markers when available, thereby enriching the overall analytical context.
[0074] In certain embodiments, the data input module 108 supports secure acquisition workflows for clinical and sensitive mental health related data by incorporating authentication layers, encrypted communication channels, and controlled data-access protocols. These mechanisms ensure that incoming patient-related records meet privacy, confidentiality, and regulatory standards. The structured data produced by the data input module 108 is transmitted directly to the pre-processing module 110 for cleaning, normalization, and anonymization, establishing a coherent and reliable foundation for deep learning-based suicide ideation detection.
[0075] The best mode contemplated for implementing the present suicide ideation detection system 100 involves deploying the processing unit 106 on a cloud-based deep learning environment configured with graphical processing unit accelerated servers capable of executing transformer based language models and sequential learning architectures. In the preferred implementation, the data input module 108 acquires multi source textual inputs using representative state transfer in application programming interface, secure clinical interfaces, and authenticated data streams, which are routed into the pre-processing module 110. The pre-processing module applies advanced normalization procedures, including language detection, text standardization, anonymization, lemmatization, and noise removal, to ensure that all incoming content is formatted uniformly for model inference. The feature extraction module 112 operates using a hybrid encoder framework combining transformer embedding’s, such as bidirectional encoder representations from transformers based contextual representations, with bidirectional long short term memory layers to capture semantic, emotional, and psychological dependencies within the text. The extracted feature vectors are passed to the risk assessment module 114, which implements a multi-head attention mechanism and a domain-tuned suicide ideation classifier trained on a diverse, multi-platform dataset containing both high-risk and non-risk expressions. This module computes a suicide risk probability score in real time, enabling fast and accurate evaluation of complex, indirect, and metaphorical suicide-related content. To ensure secure handling of sensitive mental-health data, the integrity verification module 116 employs an identity-based remote data integrity checking protocol using a private key generator and pairing based cryptographic primitives. These operations verify authenticity and prevent tampering of training datasets, clinical text records, and real-time inference data. The performance evaluation module 118 continuously monitors the model’s behaviour using explainable artificial intelligence techniques, such as shapley additive explanation visualizations and attention-based interpretability maps, providing transparent insights into the reasoning behind model predictions. When the analysed content exceeds the predefined risk threshold, the alert generation module 120 issues an automated suicide risk alert through secure communication channels to authorized mental-health professionals, clinical systems, and intervention teams. In this preferred mode of operation, the system maintains high accuracy, strong data integrity, and transparent explainability while enabling timely intervention and risk mitigation for individuals exhibiting suicide ideation.
[0076] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it will be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0077] A person of ordinary skill in the art may be aware that, in combination with the examples described in the embodiments disclosed in this specification, units and algorithm steps may be implemented by electronic hardware, computer software, or a combination thereof.
[0078] The foregoing descriptions of specific embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present disclosure and its practical application, and to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the scope of the present disclosure.
[0079] Disjunctive language such as the phrase “at least one of X, Y, Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.
[0080] In a case that no conflict occurs, the embodiments in the present disclosure and the features in the embodiments may be mutually combined. The foregoing descriptions are merely specific implementations of the present disclosure, but are not intended to limit the protection scope of the present disclosure. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present disclosure shall fall within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
, Claims:I/We Claim:
1. A hybrid deep learning-based suicide ideation detection system (100) for automated analysis and classification of suicide-related risk in digital and clinical textual data, the system (100) comprising:
a user device (102) configured to input at least on of textual, linguistic, and behavioural information from at least one digital, clinical, and social media source;
a communication network (104) configured to transmit the acquired information to at least one of remote and local device;
a processing unit (106) configured to receive, analyse, and classify the information transmitted through the communication network (104) to determine the likelihood of suicide ideation in real time;
wherein the processing unit (106) comprises:
a data input module (108) configured to acquire and structure input data from multiple sources including social media content, online communications, and patient records;
a pre-processing module (110) configured to clean, normalize, anonymised, and organize textual data for analytical consistency;
a feature extraction module (112) configured to transform pre-processed data into contextual and emotional representations using deep neural embedding models;
a risk assessment module (114) configured to evaluate suicide ideation probability using a hybrid deep learning framework incorporating contextual, sequential, and attention-based learning layers;
an integrity verification module (116) configured to perform identity-based remote data integrity checking to ensure authenticity and privacy of sensitive data;
a performance evaluation module (118) configured to assess the accuracy, reliability, and interpretability of the detection process using explainable artificial intelligence techniques; and
an alert generation module (120) configured to generate notifications, visual signals, and digital alerts when the analysed content meets and exceeds a defined suicide risk threshold.
2. The system (100) as claimed in claim 1, wherein the user device (102) comprises at least computing terminal, mobile interface, and clinical monitoring device configured to collect at least one of textual data, chat messages, behavioural patterns, and user-generated content.
3. The system (100) as claimed in claim 1, wherein the data input module (108) is configured to aggregate, synchronize, and route incoming data streams from heterogeneous sources including social media platforms, electronic health record systems, chat based interfaces, online forums, and patient self-reporting tools, and to perform metadata extraction comprising timestamps, source identifiers, and contextual attributes to enable structured downstream processing.
4. The system (100) as claimed in claim 1, wherein the pre-processing module (110) is configured to perform tokenization, lemmatization, stop word removal, noise filtering, and text standardization, and to support multilingual text inputs.
5. The system (100) as claimed in claim 1, wherein the feature extraction module (112) employs a hybrid contextual encoder comprising transformer based embedding’s and bidirectional recurrent layers configured to capture semantic, emotional, and psychological dependencies.
6. The system (100) as claimed in claim 1, wherein the risk assessment module (114) integrates multi-head attention mechanisms and adaptive thresholding for context sensitive evaluation of suicide ideation probability.
7. The system (100) as claimed in claim 1, wherein the integrity verification module (116) comprises a private key generator and tag verification unit configured to authenticate user identity and verify data integrity through pairing-based cryptographic operations.
8. The system (100) as claimed in claim 1, wherein the performance evaluation module (118) employs explainable artificial intelligence visualization techniques including shapley additive explanations, attention mapping, and interpretive heat maps to enhance transparency and assist clinical decision support.
9. The system (100) as claimed in claim 1, wherein the alert generation module (120) transmits digital notifications, intervention prompts, and alerts containing suicide risk scores and context summaries to authorized monitoring systems and mental health professionals.
10. A method (200) for detecting suicide a hybrid deep learning-based suicide ideation detection system (100), the method (200) comprising:
acquiring (202) textual, linguistic, and behavioural data from at least one of digital, clinical, and social media sources through the user device (102) connected via a communication network (104);
receiving and structuring (204) the acquired data within the processing unit (106) for subsequent analysis;
pre-processing (206) the received data using the pre-processing module (110) to remove noise, standardize text, perform anonymization, and normalize linguistic structures for consistent representation;
extracting (208) contextual, semantic, and emotional features from the pre-processed data using the feature extraction module (112) configured with transformer based and sequential neural encoders;
assessing (210) suicide ideation probability through the risk assessment module (114) by applying hybrid deep learning layers incorporating attention-based contextual reasoning and temporal pattern analysis;
verifying (212) the authenticity and integrity of input data using the identity-based remote data integrity checking module (116) employing cryptographic verification for secure and privacy preserving computation;
evaluating (214) detection performance metrics including accuracy, precision, recall, and interpretability using the performance evaluation module (118) integrated with explainable artificial intelligence visualizations; and
generating (216) an automated, visual, and digital alert through the alert generation module (120) when the analysed content exceeds a predefined suicide risk threshold, thereby enabling real-time intervention and monitoring.